- Email: [email protected]
Traffic conflicts of autonomous vehicles
Chapter 12
Traffic conflicts of autonomous vehicles Chapter outline Autonomous vehicles and traffic safety 217 Autonomous navigation and its potential shortcomings 220
Application of traffic conflict analysis 222 References 228
Abstract Emergence of autonomous vehicles and their profound impact on road safety are expected in the foreseeable future. The transition period to roads with all machine-driven vehicles will require efficient and effective safety analysis and management to identify hazards and to make timely responses to them with vehicle and road improvements. The role of traffic conflicts is explained with regard to the potential sources of data, possible challenges, and required changes in data handling and sharing.
The invention of the autonomous vehicle has a long history. Some believe that it started around 1500 with Leonardo da Vinci who designed a cart pushed by a high-tension spring along a pre-set path. Others may argue that the actual progress toward autonomous vehicles began much later, in 1945, with the designing of the first cruise control that was introduced to the market in 1958 (Ford, 2019). Since then, great strides in technological progress have led to the current autonomous vehicles. Some of the vehicles may drive for hundreds and thousands of miles without requiring a human driver’s involvement. Safety and convenience of road travel are appealing justifications for this progress. Communication between autonomous vehicles to enable their cooperative behavior is an additional way to further improve safety. Crashes occur mainly as the result of human failures (Rumar, 1985). Although failures of vehicles and the various elements of road infrastructure cannot be eliminated, human traffic participants are considered responsible in nearly all crash cases. Experts point out this fact to claim that replacing human drivers with machines would dramatically improve safety; some even hope for almost complete elimination of crashes. General Motors is the first company known for sharing with the public its idea of driverless cars at the “Futurama” exhibition in New York in 1939. Instrumented roads in the exhibit generated Measuring Road Safety with Surrogate Events. https://doi.org/10.1016/B978-0-12-810504-7.00012-4 © 2020 Elsevier Inc. All rights reserved.
215
216 Measuring Road Safety with Surrogate Events
an electromagnetic field that propelled the radio-controlled electric cars. This idea might have been an inspiration in the 1950s for a Saturday Evening Post advertisement of “America’s Electric Light and Power Companies” (Fig. 12.1). The envisioned driverless car technology utilized a magnetic strip (dashed lines in Fig. 12.1) to help maintain the desired path and provide a continuous supply of energy. Although the vision was daring, the magnetic strip constrained freedom of movement. The presence of a steering wheel reminds the viewer that humans were expected to drive when leaving equipped roads. Safety implications were not mentioned and they might not have been considered. Today’s technology offers a much more attractive future for autonomous vehicles than was available in the ’50s. Modern visions include unconstrained vehicles’ freedom of motion, elimination of the necessity for human drivers, wireless charging of batteries during motion, and inter-vehicle exchange of information. Nowadays visionaries anticipate that human drivers will be replaced with practically error-free navigation units that will handle even complex road and traffic situations. Self-learning automata in autonomous vehicles fed with information about surroundings scanned with laser, radar, and video sensors may allow driverless vehicles to move with or without human occupants. The pace at which these visions emerge into reality promotes an optimism about the possibility of automated human-like navigation in the foreseeable future. There are already vehicles that may
FIG. 12.1 Driverless Car of the Future, advertisement for “America’s Electric Light and Power Companies” in the ’50s. Saturday Evening Post, 1950s. Credit: The Everett Collection (Weber, 2014).
Traffic conflicts of autonomous vehicles Chapter | 12
217
FIG. 12.2 Waymo vehicle Credit: By Dllu - Own work, CC BY-SA 4.0, https://commons. wikimedia.org/w/index.php?curid=64517567.
drive thousands of miles in complex urban environments without human driver engagement (Fig. 12.2).
Autonomous vehicles and traffic safety In spite of the optimistic anticipation voiced by some experts, the safety of autonomous vehicles is still a matter of research and exploration. While adding machine-driven vehicles to the traffic of pedestrians and humandriven vehicles is sure to increase safety considerably in the near future, the risk of failure, even if lower than today, still looms. A high-profile death caused by a tested autonomous vehicle in March 2018 raised such a concern (Mitchell, 2018). In the interest of safety, the transportation service company that introduced the autonomous Waymo vehicles on public roads decided, two weeks before the initiation of their service, to put their safety backup drivers behind the wheeldafter a year-long period of testing when those drivers sat in the passenger or back seat (Efrati, 2019). Safety concerns and hopes will determine the growth rate of autonomous vehicles’ presence on the road. The perceived lack of safety of autonomous vehicles, regardless if justified or not, may stifle the demand and consequently, the progress. This is a real possibility since risk, when humans are not in control, is perceived by them as higher than the same risk when it is believed to be under their control. Most of the time drivers feel in control of their safety on the road and they ignore tragic crash statistics as not applying to them. Their belief in their invincibility is strengthened over a crash-free period. The same individuals, when removed from the driver’s position in autonomous vehicles, may feel at much higher risk than they are. This effect is expected to be particularly strong in the early stage of introducing autonomous vehicles, when individuals’ experience with these vehicles is low, while any failure of an autonomous vehicle is strengthened by news media coverage.
218 Measuring Road Safety with Surrogate Events
The ultimate success of autonomous vehicles’ emergence into the traffic of human-driven vehicles hinges on developers making quick progress in traffic safety and on the public’s desire for the convenience of travel without the necessity of driving. The initial automated vehicles must be designed to rely on a navigation system fed with data delivered only by their own sensors, with neither another instrumented vehicle nor roadside instrumentation nearby to provide additional information. A reliable navigation system is crucial yet challenging. Due to safety concerns, the system’s initial development must be based on traffic simulation and controlled laboratory experiments (Thorn et al., 2018; USDOT, 2018b). Once a sufficient confidence about the safety performance of the navigation system is reached, carefully designed tests of growing complexity are needed on open roads to confirm the design in actual traffic (Flannagan et al., 2016). The eventual perfection of the system will happen during regular use on the road. The presence of partly automated and, eventually, autonomous vehicles on the road is anticipated to grow. Although connected vehicles and other road users will help each other by revealing their position and motion, the initial scarce presence of this technology makes self-reliance of automated and autonomous units critical for their initial success. Enhanced road instrumentation is another way of improving the safety of all road users (for example, Tarko et al., 2016), including the vulnerable pedestrians and bicyclists who are the most difficult to detect by vehicle sensor. Even with these improvements, the ways a complex system may fail are countless, and failures may occur. The most important strategy in promoting the new technology to its future consumers is refining its ability to avoid crashes. The current methods of safety evaluation and management based on crash data are in conflict, however, with the demand for more efficient and proactive improvements in automated driving technology. Although pre-crash scenarios will be much better documented than they are today, thanks to the navigation data preserved in “black boxes,” the negative effect of a crash on the directly involved road users and on other road users’ perception of safety will not be prevented by current crashbased methods. The more frequent events of navigation failure even when the crash is avoideddthose failures to maintain safe motion that lead to departure from “risk-free” to “crash avoidance” navigationdmust be utilized as well if we are to learn about the causes of these events. The frequency of these events, called here traffic conflicts, and the corresponding probability of crashes associated with these events provide substantial additional knowledge about the severity of the potential problems and the causes of the risk. This approach constitutes a proactive crash risk analysis that should replace the reactive approach to safety improvement based on crashes. For clarity of discussion, let us agree that a navigation failure is manifested by a vehicle’s motion that violates the limits determined by user-comfortable longitudinal and lateral acceleration rates. Such a violation may only occur when justified by the necessity of a crash avoidance maneuver. In other words,
Traffic conflicts of autonomous vehicles Chapter | 12
219
continuation of the vehicle’s motion within comfortable limits would lead to a collision or road departure. This qualification puts the discussed events involving autonomous vehicles in the application domain of the introduced failure-based traffic conflict analysis. A potential for “recordable” crash is included in some definitions of traffic conflict. A crash is recordable if its outcome is not acceptable to those involved in the crash and the police is likely called to investigate and document the event. Considering crash recordability strengthens the link between conflicts and reported crashes. Yet, the number of crashes reported and the expected number of crashes estimated from conflicts are expected to be different due to randomness and underreporting. Autonomous vehicles will reduce this gap by documenting and reporting crashes independently from involved human participants. For this reason, the definition of a conflict may be widened to any collision e even quite harmless e to help auto manufacturers and government improve the new technology. It is quite possible that multiple operational definitions of conflict will be in use depending on the context. The presented failure-related risk analysis based on traffic conflicts may be used by mechanical and software engineers to perfect automated navigation. A critical first step toward improving the automated navigation system is identifying conditions under which the risk of crash is considerable. Identifying conditions present during the navigation failure and absent during failure-free operation helps pinpoint the true causes and the corresponding improvements. On the other hand, a navigation system might be certified for conditions free of the safety problems, a determination which must be included in Operational Design Domain (ODD) (Thorn et al., 2018). Civil engineers and highway administrators form another group of potential users of traffic conflict data. Roadside instrumentation at road intersections will be able to track all road users within intersection areas, identify traffic conflicts, and estimate the risk of crashes. The location-specific safety analysis supplemented with traffic and weather conditions provides a traffic flow-level perspective that differs from a vehicle-centered analysis. Although the idea that autonomous vehicles can operate successfully in the current environment designed for human road users is attractive, the truth is that an instrumented transportation infrastructure can help accomplish safe and efficient travel by adjusting the roads and traffic control to the presence of automated driving. To do so, traffic engineers must use a proactive method of analyzing safety in order to be timely in updating their knowledge about changes in safety dependence on factors such as road geometry, signage, traffic signals, and interactions between road users, including humans and machines. The flowlevel perspective on safety and its management requires quick feedback in response to equally fast changes in safety and its relationships to these factors. New protocols of cooperation between enforcement and transportation agencies and manufacturers, owners, operators, and users of autonomous
220 Measuring Road Safety with Surrogate Events
vehicles will require access to safety data and to results of safety analysis, while the privacy, legal rights, and entrepreneurship of all involved are protected (USDOT, 2018a). One obvious need is to preserve and make accessible data pertaining to traffic conflicts recorded by involved vehicles. This could be facilitated by exchanging the data in real time between the involved vehicles and the roadside component. The rules of anonymity and non-disclosure would guarantee proper protection of interests of involved road users and automanufacturers. In return, the participants would benefit through safety improved in real time, and through the enhanced understanding of the local and general safety dependencies, which helps avoid new navigation failures by improving both the navigation systems and the roadside support.
Autonomous navigation and its potential shortcomings A successful execution of a driving task by a human driver must include the following four elementary operations: 1. Seeing road users present in the general area being approached by the subject vehicle, and in the areas from which other road users may arrive along conflicting trajectories, 2. Observing behavior of the detected road users and anticipating their movements to confirm the space available for driver’s own motion, 3. Continuing or modifying driver’s own movement to maintain comfort by keeping sufficient separation from obstructions and other road users while avoiding rapid changes of speed and direction, 4. Rapidly modifying driver’s own movement to avoid a crash when the comfortable level of separation is violated due to some type of failure. Similarly to a human driver, an autonomous vehicle must sense the area ahead that is reachable within a certain time horizon, and the areas from which other road users may arrive within the same time horizon. The entire resulting area must be scanned to identify all road users present within it in order to estimate their motions and to identify their possible positions within the time horizon. The next step is to identify the reachable area that is expected to be free of the detected road users. This area is defined with the assumption of comfortable trajectories of the subject vehicle given its current position and motion, the intended destination, and the preferences of its occupants. Eventually, an optimal trajectory is selected among all comfortable trajectories. The selected trajectory is executed until the moment when an updated trajectory is generated with new data obtained from the sensors. Even if the updated trajectory differs from the previous trajectory, the initial portion of the new trajectory must be similar to the just executed portion of the old trajectory. Trajectories are generated and updated frequently enough to keep the resulting transition smooth and within the limits of users’ preferred acceleration rates. User preferences also include the shortest acceptable separation between
Traffic conflicts of autonomous vehicles Chapter | 12
221
themselves and other road users and obstructions (including the road and lane edges). The separation constraint may take the form of a threshold distance or a threshold instantaneous time to collision. Traffic conflicts and crashes are caused by human drivers after they fail to see other road users or misunderstand the other users’ intentions. Drivers, for example, do not look in the right direction due to distraction, fatigue, lack of experience, or confusion. Drivers look but do not see due to poor visibility, occlusion by another vehicle or visual obstruction, or simply due to lack of focus while the brain processes something irrelevant to the driving task. Drivers fail to anticipate properly because of their lack of experience, their misperception of speed, direction of traffic, or rules of traffic, because of confusion, or because of unusual behavior of another road user. In the cases of both failed scanning/detection and anticipation, drivers may respond with delay, which necessitates a rapid avoidance maneuver manifested by strong acceleration and shortened separation between road users or between the road user and an obstruction. With modifications, this failure mechanism and its remedies may be applied to autonomous vehicles. Scanning failure due to insufficient scanning area or scanning focused in wrong direction may be considerably reduced by 360-degree scanning (LiDAR) or by multiplicity of sensors installed in strategic points of a vehicle’s body with focus on the forward and backward directions with some additional fields of view at the corners. Some risk of failure may be introduced by the insufficient scanning range, or by the excessive speed of another road user or poor visibility. A Tesla vehicle controlled by autopilot hit a white truck crossing the road. It was not clear if the white truck was missed against the background of a sunny sky in Florida or if the brakes were not properly engaged during the crash evasion attempt (Boudette, 2016). A woman crossing a road at night was hit by an Uber truck due to both the truck’s malfunctioning LiDAR sensor and the limited ability of the working sensor to detect the woman wearing black clothes on a poorly lit road (Efrati, 2019). Yet multiple types and redundancy of sensors make the information flow robust under various conditions. Communicating the vehicle’s own position on the road also helps avoid collisions, and the much shorter reaction time of machines, as compared to humans, is always beneficial. Another potentially serious problem may be incorrect anticipation of the motion of other vehicles in the vicinity of the subject vehicle. Human drivers and pedestrians are difficult to predict due to their inconsistency, complexity, and propensity to make mistakes. This challenge may be partially addressed by the shorter reaction time of machines and their better designed and trained navigation “brains.” In the long run, when many machine-driven vehicles are on the road, inter-vehicle communication may include each vehicle’s planned movements for the next one or 2 s, and their consequent modifications based on mutual resolution of a potential conflict.
222 Measuring Road Safety with Surrogate Events
Although the shortcomings of the navigation systems in their early stages may be eventually eliminated, certain uncontrollable safety factors will remain. Braking requires distance, which may be particularly long at high speeds and on slippery roads. All sensors are subject to aging, their performance decreases in adverse conditions, and random noise and the physical imperfections of receivers may obscure signals. Video sensors are inferior in nighttime conditions, while radar detectors have low resolution and are blind to color. Pedestrians and bicyclists will remain the most challenging objects to detect, particularly in rain, snow, and darkness.
Application of traffic conflict analysis It is hoped that considering traffic conflicts for safety management on public roads will facilitate a comprehensive safety evaluation that potentially captures nearly all possible crashes and their consequences. In application to autonomous vehicles, the expectations of traffic conflict analysis will be determined by objectives that may be narrower than the general goal of evaluating traffic safety. For instance, a vehicle manufacturer may be interested in learning about its product’s ability to move safely in a given traffic scenario, that is, in particular conditions or when performing a particular maneuver. Estimating the probability of such scenarios and their potential consequences helps focus resources on the most serious issues, and also helps identify the causes and the improvements relevant to addressing the causes. In other words, traffic conflict analysis could be applied to evaluate safety performance of a certain product in particular traffic, road, and weather conditions. Traffic conflicts and crashes can be analyzed only if they are properly documented with sensor data saved and navigation system actions recorded. Conflict or crash may be caused by internal factors such as malfunctioning sensors, faulty interpretation of the current traffic situation, incorrect anticipation of the traffic’s near-future development, faulty selection of action, faulty execution of that action, or by external factors such as objects occlusion, poor visibility, undetectable slippery pavement, or by any combination of these causes. If the conflict or crash was caused by malfunctioning sensors, the data acquired with these sensors has a questionable value for the post-analysis. Road sensors may be invaluable for the post-event analysis in such cases. The following example illustrates possible ways of extracting the needed traffic conflict information from the data likely to be collected and stored by autonomous vehicles. Let us consider a subject autonomous vehicle that interacts with another vehicle. The subject vehicle is moving along its trajectory and is also tracking the position of the other vehicle. The subject vehicle’s position relative to the other vehicle at time t is represented by range R(t) (distance between the two vehicles) and the angle q(t) (between the subject vehicle’s current orientation toward north and toward the tracked vehicle) (Fig. 12.3). The continuously changing ranges and angles are saved until the
Traffic conflicts of autonomous vehicles Chapter | 12
Path of other vehicle predicted at t
223
N Area of potential collision
D(t) v(t)
T(t) R(t)
Path of autonomous vehicle designed at t
y x
FIG. 12.3 Reference system of positioning subject and tracked vehicles.
two vehicles move to new positions that preclude any risk of collision between them. The sequence of known positions of the subject vehicle is represented by the two vectors of latitude xo(t) and longitude yo(t). The positions of the tracked vehicle are represented by vectors x(t) and y(t) obtained as: xðtÞ ¼ xo ðtÞ þ RðtÞsinqðtÞ yðtÞ ¼ yo ðtÞ þ RðtÞcosqðtÞ
(12.1)
The instantaneous time to collision s can be calculated at any time t based on the current range to the other vehicle (Fig. 12.3): sðtÞ ¼
2d$RðtÞ Rðt dÞ Rðt þ dÞ
(12.2)
or current distance to collision D and vehicle speed: sðtÞ ¼
DðtÞ VðtÞ
(12.3)
The rapidity of the evasive maneuver can be evaluated by inspecting the rate at which sðtÞ is changing. A low sðtÞ value and a large ds=dt value indicate that the event could be a conflict. A rapid reversal of the sðtÞ trend may be effected either by the subject vehicle’s response or by the tracked vehicle’s response. Violation of the user-preferred settings by the updated motion of the responding vehicle is a strong indication of a conflict. The necessary condition is that the originally planned motion, if executed, would lead to a collision. The expected policy for application to autonomous vehicles will require preserving and analyzing the conflict data to help improve safety.
224 Measuring Road Safety with Surrogate Events
From the perspective of a single autonomous vehicle, a traffic event can be claimed a conflict if the navigation system is forced to violate its preferred motion boundaries, such as the maximum longitudinal or lateral acceleration, to avoid a collision with another road user or a fixed obstruction. The motion of vehicles during the post-incident phase will help confirm if a crash would have been inevitable if no evasion had been executed. Saved data should be sufficient to identify the cause: scrambled signal, malfunctioning sensor, incorrect interpretation of the signal, or faulty anticipation. It is also possible that a failure of the tracked object, and not of the autonomous unit, caused the conflict. Evaluating the autonomous unit’s performance in response to a failure of another road user is also beneficial. An alternative case is the absence of conflict because the autonomous unit performed an unnecessary evasion maneuver. The data collected during the post-incident phase should help detect this false alarm. In such a case, there is no danger of crash, and discomfort of the vehicle’s occupants is the only price to pay. Even then, the cause of the event must be identified in order to prevent future failures that reduce travel comfort and eventually may cause a crash. Another interesting question is how to treat the evasion actions performed by two interacting vehicles. Undoubtedly, the action of two vehicles affects the response time, measured from the time when the separation threshold is violated until the time the separation starts to grow. In an analysis focused on a single vehicle, the behavior of the other involved object may be considered as an external or environmental factor that adds to the unexplained heterogeneity. This treatment is justified if there is no communication between the units and the subject vehicle must respond independently. From the public road point of view, the case of evasion actions by two interacting road users should be somehow considered in the safety analysis if an explicit representation of such a traffic conflict is available. Otherwise, the perspective of a single vehicle is maintained but a rule for selecting the primary vehicle for analysis is needed. Selection of the primary participant in a traffic conflict could be based on a certain criterion such as order of responding, response strength, traffic priority, or type of road user. A consistent selection of a primary participant would most likely reduce the additional heterogeneity introduced by the evasion maneuvers performed by two involved road users. The last element to consider is the measure of separation between the conflicting road users. The previous chapters made a distinction between the instantaneous time to collision s and the actual time to collision T. Both of the alternatives are possible if the trajectory information of the primary road user is known, together with the relative trajectory of the secondary road user derived from the tracking data. A simple conversion of the coordinates puts both trajectories in the same position referencing system. Nevertheless, convenience and simplicity prompt using instantaneous separations, either a temporal separation through instantaneous time to collision, or a spatial
Traffic conflicts of autonomous vehicles Chapter | 12
225
separation through range. Tarko (2012) compared three alternative measures of nearness to road departure: time to departure along a straight path, time to departure based on fixed lateral speed, and simple spatial lateral clearance to the pavement edge. Although all three measures produced reasonable results, the simplest third measure was found the most promising. It is important to note that the lateral clearance to road edge may be considered less intuitive than the travel distance remaining to leave the road. This finding indicates that the crash nearness measure does not need to be the most intuitive. Nevertheless, two fundamental conditions must be met by the measure of separation to be applied: (1) it must be consistent, and decrease if the separation decreases, and (2) it must take a single and definite value at a crash, typically zero. The range and the instantaneous time to collision meet the two conditions. They are either measured by the autonomous unit or can be calculated from the tracking data. Claiming a traffic conflict may be entirely based on the measurements and their processing in the autonomous unit. The information required for traffic conflict analysis may be transmitted right after a confirmed incident or be communicated later when the unit is not in operation, based on an established data sharing protocol. The obtained traffic conflict data are then analyzed to determine the proper separation threshold and to estimate the underlying distribution of response times. Although estimation of the Lomax distribution accounts for heterogeneity, reducing the heterogeneity by limiting the variability of external factors improves the statistical confidence and interpretation of the results. Thus, to reduce the unknown heterogeneity of response times and to estimate the expected frequency of crashes under certain conditions, the collected traffic conflicts should be grouped by certain “pre-crash” scenarios. A taxonomy of claimed traffic conflicts should be based on the taxonomy of crashes and the factors whose effects are to be studied. Another important consideration that must be accounted for when comparing the safety level in various conditions is the exposure to crash risk. It should be noted that a vehicle-focused application defines traffic conflict analysis as longitudinal with objects observed along the vehicle’s travel. Unlike roadside observations, longitudinal observations better support estimating the effects of temporal and situational factors rather than locationrelated factors. Consequently an exposure could be measured by traveled distance or traveled time when following another vehicle, or when driving freely. Other possible exposure measures include the number of certain types of maneuvers such as changing lanes or crossing roads. Measuring the exposure as distance, time, or frequency of events must again rely on the functions of an autonomous unit. The most appealing event-based exposure measure is the number of all interactions of a certain type: conflict or conflict-free. For example, regular interactions with pedestrians (the number of pedestrians to whom the vehicle reacted) that involved the possibility of
226 Measuring Road Safety with Surrogate Events
collision (though not necessarily avoidance) seems to be an excellent exposure measure for a collision with a pedestrian. Estimating the crash involvement rate of autonomous vehicles and the effects of various conditions requires collecting and recording data by these vehicles. The data can be summarized as follows: l l
l
l
l
Absolute trajectories (x, y) of trips made by an autonomous vehicle. Relative trajectories (R, q) of other road users tracked by autonomous vehicles during collision avoidance and possibly during other safetyrelevant traffic events when R or s values are small. Optionally, all native signals generated by sensors during collision avoidance episodes and during randomly sampled risk-free periods. State of sensors and other safety-relevant components and processes during collision avoidance and possibly other safety-relevant traffic events. Relevant environmental data (pavement conditions, visibility, precipitation, etc.).
The data recorded by autonomous vehicles could be periodically transmitted for safety analysis to dedicated centers operated by public agencies or private entities. Certain regulations must be observed to protect the privacy of the vehicles’ users, owners, and operators. The initial processing would include data quality diagnosis, cleaning, repairing, and annotating. The assumption that an imminent danger of collision is present right before and during the triggering of a navigation system’s collision avoidance mode allows claiming a traffic conflict without processing the trajectory data offline. This shortcut is justified only if the detection of the collision immanency in real time is fully trustworthy. Otherwise, the condition of imminent collision must be checked based on the recorded data for all cases where the instantaneous time to collision s is low enough to warrant the off-time processing, and particularly, if the off-line processing is believed to be more trustworthy than the algorithm implemented on-line. This condition of imminent collision stage should produce a list of traffic conflicts. Each traffic conflict should include its conflict ID, minimum s, trip ID, vehicle ID, time, and location (GIS coordinates). The IDs, times, and locations included in the conflicts table are sufficient to link the road and weather data pertinent to each conflict. In addition, the path information included in the tracking data combined with the road geometry data reveals the types of maneuvers made by the involved road users and the relation of the maneuvers to the road geometry. Consequently, the linked list of traffic conflicts conveys comprehensive information about the conflicts and their spatial and temporal circumstances. In the proposed method of estimating the conditional probability of a crash, the interdependency of the traffic conflict observations introduced by sorting observed response delays does not allow estimating the safety effects at the level of individual conflicts. Instead, a two-step analysis is proposed:
Traffic conflicts of autonomous vehicles Chapter | 12
227
(1) Observing conflicts in certain exogenous conditions such as road type, road conditions, traffic conditions, maneuver type, weather conditions, light conditions, and autonomous vehicle type, and estimating the expected number of crashes in each condition-defined scenario with the Lomax-based method, (2) Estimating the effects of exogenous factors on the expected number of crashes in each scenario with Gamma or log-normal regression (fixed effects are consistently estimated, creating a potential need for random effects or panel data structure). The Lomax-based estimation of the expected number of crashes requires identification of the minimum separation in each traffic conflict of the conflict group. It is postulated in Chapter 3, reasoned under certain conditions in Chapter 7, and confirmed in Chapter 11 that instantaneous time-tocollision data may be sufficient for this purpose. The proper separation threshold under which the estimate is unbiased is determined from the sequential estimation of the expected number of crashes for the same conflict, continued until the estimated expected number of crashes is stable regardless of the threshold. The traffic conflict method provides an estimate of the expected number of crashes and the estimate’s variance. The exposure, expressed by total miles traveled by all autonomous vehicles in the conflict group, is useful for calculation of the standardized crash rate. Trajectory data for estimating the exposure may be recorded at lower level of detail and resolution than trajectories saved to analyze traffic conflicts and navigation failures. The results of the analysis may be used to determine the operation domain for an autonomous vehicle, and corresponding conditions where the risk of crash is acceptable. On the other hand, conditions associated with an excessive crash risk will properly help focus further improvement effort. Identifying the conditions in which the risk of collision is excessive will also be beneficial to the road administrators and transportation engineers who may develop relevant countermeasures to support the navigation task of autonomous vehicles by, for example, improving and instrumenting the road infrastructure for locations and traffic conditions identified as particularly challenging to autonomous vehicles. The traditional crash maps could be replaced with high-risk maps in a more proactive safety management. The real-time application of probability estimates is another opportunity to utilize the Lomax-based method. Current modern vehicles issue warnings and often even adjust their motion if certain conditions and thresholds are violated. Setting proper thresholds and modifying them to individual drivers can help avoid late or premature warnings and actions. Similar problems may be faced by autonomous vehicles. In this case, the autonomous vehicle’s motion should be acceptable to passengers who may be aware of both the pre-crash situations and the autonomous vehicle’s responses.
228 Measuring Road Safety with Surrogate Events
The extent to which the comfortable limits of speed and directional changes in motion violate passenger preferences is an important element of crash avoidance; yet estimated probabilities of a crash in various traffic interactions could be a critical input for triggering the warning and eventually the crash avoidance mode. The crash probabilities, such as those presented in Chapters 9e11, could be determined in real time based on the current traffic situation and external factors by either selecting the probabilities from prepared reference tables or calculating them from equations. The probability triggers could be modified to accommodate the preferences of the car’s occupants.
References Boudette, N.E., July 29, 2016. Tesla faults brakes, but not autopilot, in fatal crash. The New York Times. https://www.nytimes.com/2016/07/30/business/tesla-faults-teslas-brakes-butnot-autopilot-in-fatal-crash.html. Efrati, A., March 23, 2019. Waymo’s cars play it safer after incidents and ‘driver fatigue. The Information, on-line magazine. https://www.theinformation.com/articles/waymos-cars-play-itsafer-after-incidents-and-driver-fatigue. Flannagan, C., LeBlanc, D., Bogard, S., Nobukawa, K., Narayanaswamy, P., Leslie, A., Kiefer, R., Marchione, M., Beck, C., Lobes, K., February 2016. Large-Scale Field Test of Forward Collision Alert and Land Departure Warning Systems. DOT HS 812 247 final report. NHTSA, US DOT. https://www.transportation.gov/av/research. Ford, 2019. A brief history of autonomous vehicle technology. Wired e Sponsor Content by Ford. https://www.wired.com/brandlab/2016/03/a-brief-history-of-autonomous-vehicle-technology/. Mitchell, R., March 31, 2018. Self-driving cars may ultimately be safer than human drivers. But after a pedestrian’s death, will the public buy it? Los Angeles Times. https://www.latimes. com/business/autos/la-fi-hy-robot-car-safety-pr-20180321-story.html. Rumar, K., 1985. The role of perceptual and cognitive filters in observed behavior. In: Evans, L., Schwing, R. (Eds.), Human Behavior in Safety. Plenum Press. Tarko, A.P., 2012. Use of crash surrogates and exceedance statistics to estimate road safety. Accident Analysis & Prevention 45, 230e240. Tarko, A.P., Ariyur, K.B., Romero, M.A., Bandaru, V.K., Lizarazo, C.G., 2016. TScan: Stationary LiDAR for Traffic and Safety StudiesdObject Detection and Tracking (Joint Transportation Research Program Publication No. FHWA/IN/JTRP-2016/24). Purdue University, West Lafayette, IN. https://doi.org/10.5703/1288284316347. Thorn, E., Kimmel, S., Chaka, M., September 26, 2018. A Framework for Automated Driving Systems Testable Cases and Scenarios. DOT HS 812 623 final report, NHTSA, US DOT. https://www.transportation.gov/av/research. USDOT, November 22, 2018. Data for Automated Vehicle Integration. https://www.transportation. gov/av/data. USDOT, November 22, 2018. Preparing for the Future of Transportation. Automated Vehicles 3.0. https://www.transportation.go/av. Weber, M., 2014. Where to? A History of Autonomous Vehicles, Computer History Museum. www.computerhistory.org/atchm/where-to-a-history-of-autonomous-vehicles/.
Traffic conflicts of autonomous vehicles Chapter outline Autonomous vehicles and traffic safety 217 Autonomous navigation and its potential shortcomings 220
Application of traffic conflict analysis 222 References 228
Abstract Emergence of autonomous vehicles and their profound impact on road safety are expected in the foreseeable future. The transition period to roads with all machine-driven vehicles will require efficient and effective safety analysis and management to identify hazards and to make timely responses to them with vehicle and road improvements. The role of traffic conflicts is explained with regard to the potential sources of data, possible challenges, and required changes in data handling and sharing.
The invention of the autonomous vehicle has a long history. Some believe that it started around 1500 with Leonardo da Vinci who designed a cart pushed by a high-tension spring along a pre-set path. Others may argue that the actual progress toward autonomous vehicles began much later, in 1945, with the designing of the first cruise control that was introduced to the market in 1958 (Ford, 2019). Since then, great strides in technological progress have led to the current autonomous vehicles. Some of the vehicles may drive for hundreds and thousands of miles without requiring a human driver’s involvement. Safety and convenience of road travel are appealing justifications for this progress. Communication between autonomous vehicles to enable their cooperative behavior is an additional way to further improve safety. Crashes occur mainly as the result of human failures (Rumar, 1985). Although failures of vehicles and the various elements of road infrastructure cannot be eliminated, human traffic participants are considered responsible in nearly all crash cases. Experts point out this fact to claim that replacing human drivers with machines would dramatically improve safety; some even hope for almost complete elimination of crashes. General Motors is the first company known for sharing with the public its idea of driverless cars at the “Futurama” exhibition in New York in 1939. Instrumented roads in the exhibit generated Measuring Road Safety with Surrogate Events. https://doi.org/10.1016/B978-0-12-810504-7.00012-4 © 2020 Elsevier Inc. All rights reserved.
215
216 Measuring Road Safety with Surrogate Events
an electromagnetic field that propelled the radio-controlled electric cars. This idea might have been an inspiration in the 1950s for a Saturday Evening Post advertisement of “America’s Electric Light and Power Companies” (Fig. 12.1). The envisioned driverless car technology utilized a magnetic strip (dashed lines in Fig. 12.1) to help maintain the desired path and provide a continuous supply of energy. Although the vision was daring, the magnetic strip constrained freedom of movement. The presence of a steering wheel reminds the viewer that humans were expected to drive when leaving equipped roads. Safety implications were not mentioned and they might not have been considered. Today’s technology offers a much more attractive future for autonomous vehicles than was available in the ’50s. Modern visions include unconstrained vehicles’ freedom of motion, elimination of the necessity for human drivers, wireless charging of batteries during motion, and inter-vehicle exchange of information. Nowadays visionaries anticipate that human drivers will be replaced with practically error-free navigation units that will handle even complex road and traffic situations. Self-learning automata in autonomous vehicles fed with information about surroundings scanned with laser, radar, and video sensors may allow driverless vehicles to move with or without human occupants. The pace at which these visions emerge into reality promotes an optimism about the possibility of automated human-like navigation in the foreseeable future. There are already vehicles that may
FIG. 12.1 Driverless Car of the Future, advertisement for “America’s Electric Light and Power Companies” in the ’50s. Saturday Evening Post, 1950s. Credit: The Everett Collection (Weber, 2014).
Traffic conflicts of autonomous vehicles Chapter | 12
217
FIG. 12.2 Waymo vehicle Credit: By Dllu - Own work, CC BY-SA 4.0, https://commons. wikimedia.org/w/index.php?curid=64517567.
drive thousands of miles in complex urban environments without human driver engagement (Fig. 12.2).
Autonomous vehicles and traffic safety In spite of the optimistic anticipation voiced by some experts, the safety of autonomous vehicles is still a matter of research and exploration. While adding machine-driven vehicles to the traffic of pedestrians and humandriven vehicles is sure to increase safety considerably in the near future, the risk of failure, even if lower than today, still looms. A high-profile death caused by a tested autonomous vehicle in March 2018 raised such a concern (Mitchell, 2018). In the interest of safety, the transportation service company that introduced the autonomous Waymo vehicles on public roads decided, two weeks before the initiation of their service, to put their safety backup drivers behind the wheeldafter a year-long period of testing when those drivers sat in the passenger or back seat (Efrati, 2019). Safety concerns and hopes will determine the growth rate of autonomous vehicles’ presence on the road. The perceived lack of safety of autonomous vehicles, regardless if justified or not, may stifle the demand and consequently, the progress. This is a real possibility since risk, when humans are not in control, is perceived by them as higher than the same risk when it is believed to be under their control. Most of the time drivers feel in control of their safety on the road and they ignore tragic crash statistics as not applying to them. Their belief in their invincibility is strengthened over a crash-free period. The same individuals, when removed from the driver’s position in autonomous vehicles, may feel at much higher risk than they are. This effect is expected to be particularly strong in the early stage of introducing autonomous vehicles, when individuals’ experience with these vehicles is low, while any failure of an autonomous vehicle is strengthened by news media coverage.
218 Measuring Road Safety with Surrogate Events
The ultimate success of autonomous vehicles’ emergence into the traffic of human-driven vehicles hinges on developers making quick progress in traffic safety and on the public’s desire for the convenience of travel without the necessity of driving. The initial automated vehicles must be designed to rely on a navigation system fed with data delivered only by their own sensors, with neither another instrumented vehicle nor roadside instrumentation nearby to provide additional information. A reliable navigation system is crucial yet challenging. Due to safety concerns, the system’s initial development must be based on traffic simulation and controlled laboratory experiments (Thorn et al., 2018; USDOT, 2018b). Once a sufficient confidence about the safety performance of the navigation system is reached, carefully designed tests of growing complexity are needed on open roads to confirm the design in actual traffic (Flannagan et al., 2016). The eventual perfection of the system will happen during regular use on the road. The presence of partly automated and, eventually, autonomous vehicles on the road is anticipated to grow. Although connected vehicles and other road users will help each other by revealing their position and motion, the initial scarce presence of this technology makes self-reliance of automated and autonomous units critical for their initial success. Enhanced road instrumentation is another way of improving the safety of all road users (for example, Tarko et al., 2016), including the vulnerable pedestrians and bicyclists who are the most difficult to detect by vehicle sensor. Even with these improvements, the ways a complex system may fail are countless, and failures may occur. The most important strategy in promoting the new technology to its future consumers is refining its ability to avoid crashes. The current methods of safety evaluation and management based on crash data are in conflict, however, with the demand for more efficient and proactive improvements in automated driving technology. Although pre-crash scenarios will be much better documented than they are today, thanks to the navigation data preserved in “black boxes,” the negative effect of a crash on the directly involved road users and on other road users’ perception of safety will not be prevented by current crashbased methods. The more frequent events of navigation failure even when the crash is avoideddthose failures to maintain safe motion that lead to departure from “risk-free” to “crash avoidance” navigationdmust be utilized as well if we are to learn about the causes of these events. The frequency of these events, called here traffic conflicts, and the corresponding probability of crashes associated with these events provide substantial additional knowledge about the severity of the potential problems and the causes of the risk. This approach constitutes a proactive crash risk analysis that should replace the reactive approach to safety improvement based on crashes. For clarity of discussion, let us agree that a navigation failure is manifested by a vehicle’s motion that violates the limits determined by user-comfortable longitudinal and lateral acceleration rates. Such a violation may only occur when justified by the necessity of a crash avoidance maneuver. In other words,
Traffic conflicts of autonomous vehicles Chapter | 12
219
continuation of the vehicle’s motion within comfortable limits would lead to a collision or road departure. This qualification puts the discussed events involving autonomous vehicles in the application domain of the introduced failure-based traffic conflict analysis. A potential for “recordable” crash is included in some definitions of traffic conflict. A crash is recordable if its outcome is not acceptable to those involved in the crash and the police is likely called to investigate and document the event. Considering crash recordability strengthens the link between conflicts and reported crashes. Yet, the number of crashes reported and the expected number of crashes estimated from conflicts are expected to be different due to randomness and underreporting. Autonomous vehicles will reduce this gap by documenting and reporting crashes independently from involved human participants. For this reason, the definition of a conflict may be widened to any collision e even quite harmless e to help auto manufacturers and government improve the new technology. It is quite possible that multiple operational definitions of conflict will be in use depending on the context. The presented failure-related risk analysis based on traffic conflicts may be used by mechanical and software engineers to perfect automated navigation. A critical first step toward improving the automated navigation system is identifying conditions under which the risk of crash is considerable. Identifying conditions present during the navigation failure and absent during failure-free operation helps pinpoint the true causes and the corresponding improvements. On the other hand, a navigation system might be certified for conditions free of the safety problems, a determination which must be included in Operational Design Domain (ODD) (Thorn et al., 2018). Civil engineers and highway administrators form another group of potential users of traffic conflict data. Roadside instrumentation at road intersections will be able to track all road users within intersection areas, identify traffic conflicts, and estimate the risk of crashes. The location-specific safety analysis supplemented with traffic and weather conditions provides a traffic flow-level perspective that differs from a vehicle-centered analysis. Although the idea that autonomous vehicles can operate successfully in the current environment designed for human road users is attractive, the truth is that an instrumented transportation infrastructure can help accomplish safe and efficient travel by adjusting the roads and traffic control to the presence of automated driving. To do so, traffic engineers must use a proactive method of analyzing safety in order to be timely in updating their knowledge about changes in safety dependence on factors such as road geometry, signage, traffic signals, and interactions between road users, including humans and machines. The flowlevel perspective on safety and its management requires quick feedback in response to equally fast changes in safety and its relationships to these factors. New protocols of cooperation between enforcement and transportation agencies and manufacturers, owners, operators, and users of autonomous
220 Measuring Road Safety with Surrogate Events
vehicles will require access to safety data and to results of safety analysis, while the privacy, legal rights, and entrepreneurship of all involved are protected (USDOT, 2018a). One obvious need is to preserve and make accessible data pertaining to traffic conflicts recorded by involved vehicles. This could be facilitated by exchanging the data in real time between the involved vehicles and the roadside component. The rules of anonymity and non-disclosure would guarantee proper protection of interests of involved road users and automanufacturers. In return, the participants would benefit through safety improved in real time, and through the enhanced understanding of the local and general safety dependencies, which helps avoid new navigation failures by improving both the navigation systems and the roadside support.
Autonomous navigation and its potential shortcomings A successful execution of a driving task by a human driver must include the following four elementary operations: 1. Seeing road users present in the general area being approached by the subject vehicle, and in the areas from which other road users may arrive along conflicting trajectories, 2. Observing behavior of the detected road users and anticipating their movements to confirm the space available for driver’s own motion, 3. Continuing or modifying driver’s own movement to maintain comfort by keeping sufficient separation from obstructions and other road users while avoiding rapid changes of speed and direction, 4. Rapidly modifying driver’s own movement to avoid a crash when the comfortable level of separation is violated due to some type of failure. Similarly to a human driver, an autonomous vehicle must sense the area ahead that is reachable within a certain time horizon, and the areas from which other road users may arrive within the same time horizon. The entire resulting area must be scanned to identify all road users present within it in order to estimate their motions and to identify their possible positions within the time horizon. The next step is to identify the reachable area that is expected to be free of the detected road users. This area is defined with the assumption of comfortable trajectories of the subject vehicle given its current position and motion, the intended destination, and the preferences of its occupants. Eventually, an optimal trajectory is selected among all comfortable trajectories. The selected trajectory is executed until the moment when an updated trajectory is generated with new data obtained from the sensors. Even if the updated trajectory differs from the previous trajectory, the initial portion of the new trajectory must be similar to the just executed portion of the old trajectory. Trajectories are generated and updated frequently enough to keep the resulting transition smooth and within the limits of users’ preferred acceleration rates. User preferences also include the shortest acceptable separation between
Traffic conflicts of autonomous vehicles Chapter | 12
221
themselves and other road users and obstructions (including the road and lane edges). The separation constraint may take the form of a threshold distance or a threshold instantaneous time to collision. Traffic conflicts and crashes are caused by human drivers after they fail to see other road users or misunderstand the other users’ intentions. Drivers, for example, do not look in the right direction due to distraction, fatigue, lack of experience, or confusion. Drivers look but do not see due to poor visibility, occlusion by another vehicle or visual obstruction, or simply due to lack of focus while the brain processes something irrelevant to the driving task. Drivers fail to anticipate properly because of their lack of experience, their misperception of speed, direction of traffic, or rules of traffic, because of confusion, or because of unusual behavior of another road user. In the cases of both failed scanning/detection and anticipation, drivers may respond with delay, which necessitates a rapid avoidance maneuver manifested by strong acceleration and shortened separation between road users or between the road user and an obstruction. With modifications, this failure mechanism and its remedies may be applied to autonomous vehicles. Scanning failure due to insufficient scanning area or scanning focused in wrong direction may be considerably reduced by 360-degree scanning (LiDAR) or by multiplicity of sensors installed in strategic points of a vehicle’s body with focus on the forward and backward directions with some additional fields of view at the corners. Some risk of failure may be introduced by the insufficient scanning range, or by the excessive speed of another road user or poor visibility. A Tesla vehicle controlled by autopilot hit a white truck crossing the road. It was not clear if the white truck was missed against the background of a sunny sky in Florida or if the brakes were not properly engaged during the crash evasion attempt (Boudette, 2016). A woman crossing a road at night was hit by an Uber truck due to both the truck’s malfunctioning LiDAR sensor and the limited ability of the working sensor to detect the woman wearing black clothes on a poorly lit road (Efrati, 2019). Yet multiple types and redundancy of sensors make the information flow robust under various conditions. Communicating the vehicle’s own position on the road also helps avoid collisions, and the much shorter reaction time of machines, as compared to humans, is always beneficial. Another potentially serious problem may be incorrect anticipation of the motion of other vehicles in the vicinity of the subject vehicle. Human drivers and pedestrians are difficult to predict due to their inconsistency, complexity, and propensity to make mistakes. This challenge may be partially addressed by the shorter reaction time of machines and their better designed and trained navigation “brains.” In the long run, when many machine-driven vehicles are on the road, inter-vehicle communication may include each vehicle’s planned movements for the next one or 2 s, and their consequent modifications based on mutual resolution of a potential conflict.
222 Measuring Road Safety with Surrogate Events
Although the shortcomings of the navigation systems in their early stages may be eventually eliminated, certain uncontrollable safety factors will remain. Braking requires distance, which may be particularly long at high speeds and on slippery roads. All sensors are subject to aging, their performance decreases in adverse conditions, and random noise and the physical imperfections of receivers may obscure signals. Video sensors are inferior in nighttime conditions, while radar detectors have low resolution and are blind to color. Pedestrians and bicyclists will remain the most challenging objects to detect, particularly in rain, snow, and darkness.
Application of traffic conflict analysis It is hoped that considering traffic conflicts for safety management on public roads will facilitate a comprehensive safety evaluation that potentially captures nearly all possible crashes and their consequences. In application to autonomous vehicles, the expectations of traffic conflict analysis will be determined by objectives that may be narrower than the general goal of evaluating traffic safety. For instance, a vehicle manufacturer may be interested in learning about its product’s ability to move safely in a given traffic scenario, that is, in particular conditions or when performing a particular maneuver. Estimating the probability of such scenarios and their potential consequences helps focus resources on the most serious issues, and also helps identify the causes and the improvements relevant to addressing the causes. In other words, traffic conflict analysis could be applied to evaluate safety performance of a certain product in particular traffic, road, and weather conditions. Traffic conflicts and crashes can be analyzed only if they are properly documented with sensor data saved and navigation system actions recorded. Conflict or crash may be caused by internal factors such as malfunctioning sensors, faulty interpretation of the current traffic situation, incorrect anticipation of the traffic’s near-future development, faulty selection of action, faulty execution of that action, or by external factors such as objects occlusion, poor visibility, undetectable slippery pavement, or by any combination of these causes. If the conflict or crash was caused by malfunctioning sensors, the data acquired with these sensors has a questionable value for the post-analysis. Road sensors may be invaluable for the post-event analysis in such cases. The following example illustrates possible ways of extracting the needed traffic conflict information from the data likely to be collected and stored by autonomous vehicles. Let us consider a subject autonomous vehicle that interacts with another vehicle. The subject vehicle is moving along its trajectory and is also tracking the position of the other vehicle. The subject vehicle’s position relative to the other vehicle at time t is represented by range R(t) (distance between the two vehicles) and the angle q(t) (between the subject vehicle’s current orientation toward north and toward the tracked vehicle) (Fig. 12.3). The continuously changing ranges and angles are saved until the
Traffic conflicts of autonomous vehicles Chapter | 12
Path of other vehicle predicted at t
223
N Area of potential collision
D(t) v(t)
T(t) R(t)
Path of autonomous vehicle designed at t
y x
FIG. 12.3 Reference system of positioning subject and tracked vehicles.
two vehicles move to new positions that preclude any risk of collision between them. The sequence of known positions of the subject vehicle is represented by the two vectors of latitude xo(t) and longitude yo(t). The positions of the tracked vehicle are represented by vectors x(t) and y(t) obtained as: xðtÞ ¼ xo ðtÞ þ RðtÞsinqðtÞ yðtÞ ¼ yo ðtÞ þ RðtÞcosqðtÞ
(12.1)
The instantaneous time to collision s can be calculated at any time t based on the current range to the other vehicle (Fig. 12.3): sðtÞ ¼
2d$RðtÞ Rðt dÞ Rðt þ dÞ
(12.2)
or current distance to collision D and vehicle speed: sðtÞ ¼
DðtÞ VðtÞ
(12.3)
The rapidity of the evasive maneuver can be evaluated by inspecting the rate at which sðtÞ is changing. A low sðtÞ value and a large ds=dt value indicate that the event could be a conflict. A rapid reversal of the sðtÞ trend may be effected either by the subject vehicle’s response or by the tracked vehicle’s response. Violation of the user-preferred settings by the updated motion of the responding vehicle is a strong indication of a conflict. The necessary condition is that the originally planned motion, if executed, would lead to a collision. The expected policy for application to autonomous vehicles will require preserving and analyzing the conflict data to help improve safety.
224 Measuring Road Safety with Surrogate Events
From the perspective of a single autonomous vehicle, a traffic event can be claimed a conflict if the navigation system is forced to violate its preferred motion boundaries, such as the maximum longitudinal or lateral acceleration, to avoid a collision with another road user or a fixed obstruction. The motion of vehicles during the post-incident phase will help confirm if a crash would have been inevitable if no evasion had been executed. Saved data should be sufficient to identify the cause: scrambled signal, malfunctioning sensor, incorrect interpretation of the signal, or faulty anticipation. It is also possible that a failure of the tracked object, and not of the autonomous unit, caused the conflict. Evaluating the autonomous unit’s performance in response to a failure of another road user is also beneficial. An alternative case is the absence of conflict because the autonomous unit performed an unnecessary evasion maneuver. The data collected during the post-incident phase should help detect this false alarm. In such a case, there is no danger of crash, and discomfort of the vehicle’s occupants is the only price to pay. Even then, the cause of the event must be identified in order to prevent future failures that reduce travel comfort and eventually may cause a crash. Another interesting question is how to treat the evasion actions performed by two interacting vehicles. Undoubtedly, the action of two vehicles affects the response time, measured from the time when the separation threshold is violated until the time the separation starts to grow. In an analysis focused on a single vehicle, the behavior of the other involved object may be considered as an external or environmental factor that adds to the unexplained heterogeneity. This treatment is justified if there is no communication between the units and the subject vehicle must respond independently. From the public road point of view, the case of evasion actions by two interacting road users should be somehow considered in the safety analysis if an explicit representation of such a traffic conflict is available. Otherwise, the perspective of a single vehicle is maintained but a rule for selecting the primary vehicle for analysis is needed. Selection of the primary participant in a traffic conflict could be based on a certain criterion such as order of responding, response strength, traffic priority, or type of road user. A consistent selection of a primary participant would most likely reduce the additional heterogeneity introduced by the evasion maneuvers performed by two involved road users. The last element to consider is the measure of separation between the conflicting road users. The previous chapters made a distinction between the instantaneous time to collision s and the actual time to collision T. Both of the alternatives are possible if the trajectory information of the primary road user is known, together with the relative trajectory of the secondary road user derived from the tracking data. A simple conversion of the coordinates puts both trajectories in the same position referencing system. Nevertheless, convenience and simplicity prompt using instantaneous separations, either a temporal separation through instantaneous time to collision, or a spatial
Traffic conflicts of autonomous vehicles Chapter | 12
225
separation through range. Tarko (2012) compared three alternative measures of nearness to road departure: time to departure along a straight path, time to departure based on fixed lateral speed, and simple spatial lateral clearance to the pavement edge. Although all three measures produced reasonable results, the simplest third measure was found the most promising. It is important to note that the lateral clearance to road edge may be considered less intuitive than the travel distance remaining to leave the road. This finding indicates that the crash nearness measure does not need to be the most intuitive. Nevertheless, two fundamental conditions must be met by the measure of separation to be applied: (1) it must be consistent, and decrease if the separation decreases, and (2) it must take a single and definite value at a crash, typically zero. The range and the instantaneous time to collision meet the two conditions. They are either measured by the autonomous unit or can be calculated from the tracking data. Claiming a traffic conflict may be entirely based on the measurements and their processing in the autonomous unit. The information required for traffic conflict analysis may be transmitted right after a confirmed incident or be communicated later when the unit is not in operation, based on an established data sharing protocol. The obtained traffic conflict data are then analyzed to determine the proper separation threshold and to estimate the underlying distribution of response times. Although estimation of the Lomax distribution accounts for heterogeneity, reducing the heterogeneity by limiting the variability of external factors improves the statistical confidence and interpretation of the results. Thus, to reduce the unknown heterogeneity of response times and to estimate the expected frequency of crashes under certain conditions, the collected traffic conflicts should be grouped by certain “pre-crash” scenarios. A taxonomy of claimed traffic conflicts should be based on the taxonomy of crashes and the factors whose effects are to be studied. Another important consideration that must be accounted for when comparing the safety level in various conditions is the exposure to crash risk. It should be noted that a vehicle-focused application defines traffic conflict analysis as longitudinal with objects observed along the vehicle’s travel. Unlike roadside observations, longitudinal observations better support estimating the effects of temporal and situational factors rather than locationrelated factors. Consequently an exposure could be measured by traveled distance or traveled time when following another vehicle, or when driving freely. Other possible exposure measures include the number of certain types of maneuvers such as changing lanes or crossing roads. Measuring the exposure as distance, time, or frequency of events must again rely on the functions of an autonomous unit. The most appealing event-based exposure measure is the number of all interactions of a certain type: conflict or conflict-free. For example, regular interactions with pedestrians (the number of pedestrians to whom the vehicle reacted) that involved the possibility of
226 Measuring Road Safety with Surrogate Events
collision (though not necessarily avoidance) seems to be an excellent exposure measure for a collision with a pedestrian. Estimating the crash involvement rate of autonomous vehicles and the effects of various conditions requires collecting and recording data by these vehicles. The data can be summarized as follows: l l
l
l
l
Absolute trajectories (x, y) of trips made by an autonomous vehicle. Relative trajectories (R, q) of other road users tracked by autonomous vehicles during collision avoidance and possibly during other safetyrelevant traffic events when R or s values are small. Optionally, all native signals generated by sensors during collision avoidance episodes and during randomly sampled risk-free periods. State of sensors and other safety-relevant components and processes during collision avoidance and possibly other safety-relevant traffic events. Relevant environmental data (pavement conditions, visibility, precipitation, etc.).
The data recorded by autonomous vehicles could be periodically transmitted for safety analysis to dedicated centers operated by public agencies or private entities. Certain regulations must be observed to protect the privacy of the vehicles’ users, owners, and operators. The initial processing would include data quality diagnosis, cleaning, repairing, and annotating. The assumption that an imminent danger of collision is present right before and during the triggering of a navigation system’s collision avoidance mode allows claiming a traffic conflict without processing the trajectory data offline. This shortcut is justified only if the detection of the collision immanency in real time is fully trustworthy. Otherwise, the condition of imminent collision must be checked based on the recorded data for all cases where the instantaneous time to collision s is low enough to warrant the off-time processing, and particularly, if the off-line processing is believed to be more trustworthy than the algorithm implemented on-line. This condition of imminent collision stage should produce a list of traffic conflicts. Each traffic conflict should include its conflict ID, minimum s, trip ID, vehicle ID, time, and location (GIS coordinates). The IDs, times, and locations included in the conflicts table are sufficient to link the road and weather data pertinent to each conflict. In addition, the path information included in the tracking data combined with the road geometry data reveals the types of maneuvers made by the involved road users and the relation of the maneuvers to the road geometry. Consequently, the linked list of traffic conflicts conveys comprehensive information about the conflicts and their spatial and temporal circumstances. In the proposed method of estimating the conditional probability of a crash, the interdependency of the traffic conflict observations introduced by sorting observed response delays does not allow estimating the safety effects at the level of individual conflicts. Instead, a two-step analysis is proposed:
Traffic conflicts of autonomous vehicles Chapter | 12
227
(1) Observing conflicts in certain exogenous conditions such as road type, road conditions, traffic conditions, maneuver type, weather conditions, light conditions, and autonomous vehicle type, and estimating the expected number of crashes in each condition-defined scenario with the Lomax-based method, (2) Estimating the effects of exogenous factors on the expected number of crashes in each scenario with Gamma or log-normal regression (fixed effects are consistently estimated, creating a potential need for random effects or panel data structure). The Lomax-based estimation of the expected number of crashes requires identification of the minimum separation in each traffic conflict of the conflict group. It is postulated in Chapter 3, reasoned under certain conditions in Chapter 7, and confirmed in Chapter 11 that instantaneous time-tocollision data may be sufficient for this purpose. The proper separation threshold under which the estimate is unbiased is determined from the sequential estimation of the expected number of crashes for the same conflict, continued until the estimated expected number of crashes is stable regardless of the threshold. The traffic conflict method provides an estimate of the expected number of crashes and the estimate’s variance. The exposure, expressed by total miles traveled by all autonomous vehicles in the conflict group, is useful for calculation of the standardized crash rate. Trajectory data for estimating the exposure may be recorded at lower level of detail and resolution than trajectories saved to analyze traffic conflicts and navigation failures. The results of the analysis may be used to determine the operation domain for an autonomous vehicle, and corresponding conditions where the risk of crash is acceptable. On the other hand, conditions associated with an excessive crash risk will properly help focus further improvement effort. Identifying the conditions in which the risk of collision is excessive will also be beneficial to the road administrators and transportation engineers who may develop relevant countermeasures to support the navigation task of autonomous vehicles by, for example, improving and instrumenting the road infrastructure for locations and traffic conditions identified as particularly challenging to autonomous vehicles. The traditional crash maps could be replaced with high-risk maps in a more proactive safety management. The real-time application of probability estimates is another opportunity to utilize the Lomax-based method. Current modern vehicles issue warnings and often even adjust their motion if certain conditions and thresholds are violated. Setting proper thresholds and modifying them to individual drivers can help avoid late or premature warnings and actions. Similar problems may be faced by autonomous vehicles. In this case, the autonomous vehicle’s motion should be acceptable to passengers who may be aware of both the pre-crash situations and the autonomous vehicle’s responses.
228 Measuring Road Safety with Surrogate Events
The extent to which the comfortable limits of speed and directional changes in motion violate passenger preferences is an important element of crash avoidance; yet estimated probabilities of a crash in various traffic interactions could be a critical input for triggering the warning and eventually the crash avoidance mode. The crash probabilities, such as those presented in Chapters 9e11, could be determined in real time based on the current traffic situation and external factors by either selecting the probabilities from prepared reference tables or calculating them from equations. The probability triggers could be modified to accommodate the preferences of the car’s occupants.
References Boudette, N.E., July 29, 2016. Tesla faults brakes, but not autopilot, in fatal crash. The New York Times. https://www.nytimes.com/2016/07/30/business/tesla-faults-teslas-brakes-butnot-autopilot-in-fatal-crash.html. Efrati, A., March 23, 2019. Waymo’s cars play it safer after incidents and ‘driver fatigue. The Information, on-line magazine. https://www.theinformation.com/articles/waymos-cars-play-itsafer-after-incidents-and-driver-fatigue. Flannagan, C., LeBlanc, D., Bogard, S., Nobukawa, K., Narayanaswamy, P., Leslie, A., Kiefer, R., Marchione, M., Beck, C., Lobes, K., February 2016. Large-Scale Field Test of Forward Collision Alert and Land Departure Warning Systems. DOT HS 812 247 final report. NHTSA, US DOT. https://www.transportation.gov/av/research. Ford, 2019. A brief history of autonomous vehicle technology. Wired e Sponsor Content by Ford. https://www.wired.com/brandlab/2016/03/a-brief-history-of-autonomous-vehicle-technology/. Mitchell, R., March 31, 2018. Self-driving cars may ultimately be safer than human drivers. But after a pedestrian’s death, will the public buy it? Los Angeles Times. https://www.latimes. com/business/autos/la-fi-hy-robot-car-safety-pr-20180321-story.html. Rumar, K., 1985. The role of perceptual and cognitive filters in observed behavior. In: Evans, L., Schwing, R. (Eds.), Human Behavior in Safety. Plenum Press. Tarko, A.P., 2012. Use of crash surrogates and exceedance statistics to estimate road safety. Accident Analysis & Prevention 45, 230e240. Tarko, A.P., Ariyur, K.B., Romero, M.A., Bandaru, V.K., Lizarazo, C.G., 2016. TScan: Stationary LiDAR for Traffic and Safety StudiesdObject Detection and Tracking (Joint Transportation Research Program Publication No. FHWA/IN/JTRP-2016/24). Purdue University, West Lafayette, IN. https://doi.org/10.5703/1288284316347. Thorn, E., Kimmel, S., Chaka, M., September 26, 2018. A Framework for Automated Driving Systems Testable Cases and Scenarios. DOT HS 812 623 final report, NHTSA, US DOT. https://www.transportation.gov/av/research. USDOT, November 22, 2018. Data for Automated Vehicle Integration. https://www.transportation. gov/av/data. USDOT, November 22, 2018. Preparing for the Future of Transportation. Automated Vehicles 3.0. https://www.transportation.go/av. Weber, M., 2014. Where to? A History of Autonomous Vehicles, Computer History Museum. www.computerhistory.org/atchm/where-to-a-history-of-autonomous-vehicles/.