A vehicle control system using a time synchronized Hybrid VANET to reduce road accidents caused by human error

Accepted Manuscript A vehicle control system using a time synchronized Hybrid VANET to reduce road accidents caused by human error

Dahlia Sam, Dr.V. Cyril Raj, T. Esther Evangelin

PII: DOI: Reference:

S2214-2096(15)30013-9 http://dx.doi.org/10.1016/j.vehcom.2016.11.001 VEHCOM 57

To appear in:

Vehicular Communications

Received date: Revised date: Accepted date:

30 October 2015 29 September 2016 8 November 2016

Please cite this article in press as: D. Sam et al., A vehicle control system using a time synchronized Hybrid VANET to reduce road accidents caused by human error, Veh. Commun. (2016), http://dx.doi.org/10.1016/j.vehcom.2016.11.001

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Highlights • This paper elaborates a vehicle control system that integrates a pedestrian unit with the vehicular ad hoc network (VANET) to create a Hybrid VANET. The new control system known as the TSH vehicle control system aims at reducing the major cause of road crash around the globe i.e. human error. The pedestrian body unit constantly broadcasts its presence to the vehicles in its communication range. On receiving a pedestrian message, the TSH vehicle controller checks it GPS coordinates, the pedestrians’ position, its speed, acceleration and distance. If there is a possibility of accident occurring, it will send a control signal to the AEBS (Advanced emergency braking system). In this way the accidents can be avoided to a great extent.

A vehicle control system using a time synchronized Hybrid VANET to reduce road accidents caused by human error Dahlia Sam1, Dr. V. Cyril Raj2, T. Esther Evangelin3 1, 2, 3

Dr. M.G.R Educational and Research Institute, University, Chennai. [email protected] [email protected] [email protected]

Abstract -- With the continuous growth of the automotive market, road safety is becoming a popular area of research. The demand for road safety with respect to both drivers and the pedestrians has been a focus area in the last few years. Vehicular Ad-Hoc Networks (VANETS) have been the key technology under research due to its numerous application possibilities related to road safety. A study on road accidents points out to human error as the cause of more than 90% of the accidents. The main reason for human error is the limited information processing ability of humans which in turn leads to increased reaction time in unexpected situations. The history of traffic accident cases has repeatedly shown clear dependencies between the accident and the reaction time of the pedestrian or driver. In such situations, a machine intervention would prove to be an effective alternative. Here a solution is provided by developing a pilot model of a fully automated Time Synchronized Hybrid (TSH) Vehicle Control System. The system is designed using a hybrid VANET that includes a pedestrian body unit. The signals from the pedestrian unit are passed on to the vehicular nodes via the VANET. It is received by the vehicular unit and is given as an input to the TSH vehicle control system. The TSH vehicle control system uses this information and checks the chances of an accident occurring. It then gives a control signal to the automated braking system to choose an appropriate response like braking or deceleration. This system was mathematically evaluated and simulated. Different experiments were carried out to check out how far the system is able to avoid accidents. It was seen that accidents could be avoided in very higher car speeds when the vehicle control system takes over, as opposed to when driven with manual vehicle control. The system clearly outperforms the existing safety models that rely on the reaction of the drivers or pedestrians. Keywords -- Vehicular Ad Hoc Networks (VANETs), Hybrid VANET, Pedestrian safety system, Driving Safety, TSH Vehicle Control System.

1. INTRODUCTION The process of rapid urbanization has resulted in an unparalleled revolution in the growth of motor vehicles world-wide. The increase of vehicles in the last decade has put lots of pressure on the existing highways thereby increasing the number of road accidents. Road crashes are the 9th leading cause of death around the world and are predicted to become the 5th leading cause by 2030. Globally, nearly 1.3 million deaths and 50 million injuries occur annually due to road crashes. This accounts to 2.2% of all the types of deaths and 3,287 deaths in a day. This alarming increase in morbidity and mortality has become a matter of great concern around the world. Traffic accidents cost about 518$ billion USD globally. India has earned a distinction of being the leading country in the number of road accidents. In India, since 2001 there is an increase of 202 % in the number of two wheelers and 286 % in the number of four wheelers. However there has hardly been any road expansion to accommodate this increase. As a result, more than 70,000 1

people are being killed in road accidents every year. In US, pedestrian fatalities have gone up by 15% in the last 10 years. Among the total number of road accidents, 57% was caused by human error and it has also been the contributing factor in more than 90% of accidents. The other causes include environmental factors, mechanical faults etc. The main reason for this human error is the limitations in the information processing abilities in human beings. In critical accident scenarios, the human limitations are exceeded by the situation which leads to an accident. The reason for the accident in such cases is listed as “Human Error” or “Driver Error”. Taking into account all of this, the need for a real time intelligent vehicle communication system has become very important. One promising technology for this is the Vehicular Ad Hoc Network (VANET). Researchers have been continuously working on VANET based applications like driving safety, intelligent speed control, lane changing, safe highway entry and exiting, timely warning during hard braking etc. All these applications aid to improve the safety of the highway system in some way. The same VANET can also be used to prevent road accidents caused due to the above mentioned “Human Error”. 2. LITERATURE REVIEW 2.1 Causes of Human Error The drivers of the vehicle have to process a continuous flow of information which comes in the form of visual input. This includes the highway, traffic signals, pedestrians, other cars, surroundings etc. In addition, the driver will have loads of thoughts going on in his mind like trying to remember the tasks of the day, remembering directions, worrying about something etc. All this adds up to the internal input. There are also high chances that the driver is exposed to other auditory inputs like music, mobile phone, chatting with fellow passengers etc. As a result, the human brain processing capacity is drained out. However, under normal circumstances, the drivers manage to process and respond to all these inputs. In precarious situations, more attention is needed for e.g.: during low visibility evening time or night times, high traffic in highways, snowfall or when many pedestrians are walking on the road. This causes the driver to react to only a subset of the available inputs. The brain does not process the rest of the information. In such cases, the driver is most likely to respond in a wrong way. Research on road accidents caused by human error points out to three main types of errors made by drivers (Treat et. al. 1972, Road Accidents n.d., Stopping Distance n.d., Stopping Sight Distance n.d.): 1. Perceptual Error: In some situations like glare, low lights or when the driver is tired, sleepy or drunk, the driver is unable to see some crucial details. This also happens in Accident Black Spots which include sharp corners in straight road, steep slopes, a hidden junction or concealed warning signs. 2. Distraction Error: When the drivers mind is concentrating on something else, the driver often fails to notice a clearly visible pedestrian or car. This is referred to as “Blindness 2

due to Inattention”. This will cause a delay in his reaction or an error in reaction (Khairunnisa & Syah 2014). Studies show that more than half the drivers do not step the brakes hard enough and quick enough during emergency situations. 3. Response Error: Even though the driver gets the information correctly, his response may sometimes be a wrong one. e.g.: Hitting the accelerator instead of the brakes, making a sharp turn or hard braking to avoid one accident which may lead to another. 2.2 VANET Vehicular Ad Hoc Network (VANET) is a mobile network, in which the moving cars act as nodes and is referred to as “computer network on wheels”. These nodes communicate with each other as well as with the roadside equipment’s forming an Intelligent Transport System (ITS). The communication can take place between vehicles or between the vehicle and roadside units, known as RSUs within short ranges of 100 to 300 meters. Fixed RSUs connected to the backbone network, must be distributed in the highways. At any point, the vehicles may or may not have wireless access to the roadside units. As vehicles fall out of the signal range, other cars may join in, connecting vehicles to one another to form a mobile inter network. (Forian, 2006; Zeadally et. al. 2012; VANETs n.d., Chandrasekaran 2009) In 1999, the Federal Communications Commission (FCC) allocated 75 MHz of spectrum at 5.850-5.925GHz for DSRC (Dedicated Short Range Communications). The allotted frequency spectrum enabled wireless communication between vehicle-vehicle and vehicle-roadside beacons without central access point. In intelligent transportation systems, each vehicle acts as a sender, receiver and a router to broadcast information. For VRC communication to happen between vehicles and RSUs, vehicles must be equipped with a Global Positioning System (GPS) and an On Board Unit (OBU) that enables short-range wireless ad hoc networks to be formed. Automotive companies like General Motors, Toyota, Nissan, Chrysler, BMW and Ford have already started manufacturing cars equipped with such devices which will pave the way for developing smarter communication technologies. This will be helpful for many real time innovative applications such as (a) Safe smart driving, which focuses on giving timely alerts to the drivers about collisions, poor road conditions, traffic jams etc. They also include providing real time guidance to drivers while merging, driving uphill/downhill or in curvy roads. (b) Postaccident investigation, where the roadside devices can store information about accidents that can be used later. This will be helpful for investigators in forensic reconstruction and for insurance companies. (c) Media Applications, including web browsing, accessing emails, video streaming etc. during the road travel. The major characteristics that distinguish VANETs from other mobile ad hoc networks can be summarized as below. • •

VANET Nodes – All nodes in the VANET act as both transmitters and receivers. Topology – Vehicles are in constant movement which leads to rapid change in topology. 3

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Mobility Pattern – Vehicles run on pre-built highways and roads. Hence the motion pattern of the vehicles can be predicted based on the layout of the road. Speed – The nodes in VANET moves at a very high average speed as compared to MANETs. Node Density – The number of nodes in a VANET can be very high in busy highways and very sparse in remote highways. Similarly in a particular place, the traffic may be at peak during busy office hours and minimum during midnight hours. Frequent Disconnections – Since vehicles are constantly moving, the communication links between them are constantly established and broken. In remote highways where the vehicle density is low, existing links can break before the new links are formed. This may lead to temporary disconnections of the network. No energy constraints – Since the nodes in a VANET are vehicles, they have batteries that gets constantly recharged. Due to this abundant resource, vehicles can be equipped with GPS or other devices. No infrastructure – The communication between nodes in VANET is direct and does not rely on any underlying infrastructure. However, it can be connected with the infrastructure too. Unbounded network size – VANETs are highly scalable as it can span through regions of one city or several cities. Better security – VANET nodes are more secure than nodes of other wireless networks.

2.3 Work done in Road Safety In order to reduce the accidents, a system named pre-crashing detection system using laser and radar sensors was proposed by Bhumkar, Deotare & Babar (2012). Shinar (2012) has also discussed about the safety and mobility of vulnerable pedestrians. They suggested using sensing technologies to help detect the obstacles on the road. This system has long detection range and low interference but the computation and manufacturing costs still remain high. It is not affordable for home and commercial automobiles but useful for military vehicles. There has been some work done to study the pedestrian-vehicle interaction behavior (Sun, Benekohal, & Waller 2003; Markowski 2008; Agarwal 2011; Waizman 2012; Waizman & Aviv 2015). None of the proposed systems are fully developed and is yet to be tried out with VANET. The low cost reliable solution proposed by Dahlia & CyrilRaj (2014) integrates wireless roadside sensors with VANET to constantly detect events in the road and communicate to the vehicles. The optimal placement of the RSUs and sensor nodes has been discussed by Rebai et al., (2012). The system uses a long-range passive infrared (PIR) sensor which has a 90-100° wide detection cone. It can detect the presence of humans within a 20-30 feet (about 6-9 meters) detection range and detect vehicles within 50-150 feet (15- 45 meters) detection range depending on the size. The sensor has a rectified acoustic envelope output. The sensor is directly plugged in to the IEEE 802.15.4 / Zigbee compatible TelosB motes through the external connectors. The problem with the above 4

system is that it has a small probability that the presence of a human may go unnoticed by the sensor. This could be a threat to careless pedestrians. IR sensors also have non-linear characteristics which depend on the objects’ surface reflectance properties. All surfaces react differently and some surfaces may reflect, scatter or absorb the infrared energy. So the sensor output to measure the distance may not be interpreted properly. Work is also carried out by automobile companies using ultrasonic sensors around the world. Ultrasonic sensors make a perfect choice for companies because of its wide detection range, anti-interference and low price. These are combined with brake controls to prevent high speed collision. However its accuracy in obstacle detection is the lowest and its valid radius of detection is limited, which is its weakness. To overcome these issues, a human body unit is integrated with the Hybrid VANET. This will ensure that the presence of any human will never go undetected. This integrated system will detects any obstacle on the road or the presence of a pedestrian and send signals to the vehicle. On the pedestrian side, he receives an audio alert which cautions him. On the vehicle side, the signal is fed to a controller which estimates the possibility of an accident. If there is a chance of an accident occurring, it will send a control signal to the advanced emergency braking system (AEBS). This helps to avoid the collision or diminish its effects like the property damage and human fatality. 3. VEHICLE STOPPING DISTANCE AND TIME Before getting into the details of the automated vehicle control system, it is important to understand the process of manual controlling of vehicle during crucial situations. When a driver sees some obstacle on the road like a pedestrian in the crosswalk, another stopped vehicle, a wandering animal or any road debris, his immediate response would be to apply the brakes. The Drivers Reaction Time or the Stopping Time to bring the vehicle to a complete stop is usually split into three phases: (a) the mental processing time (b) the body movement time and (c) the mechanical response time. During this time, a certain distance is travelled by the vehicle which is unavoidable. This distance is known as the Stopping Distance and is also discussed below. 3.1 The Driver Reaction (Stopping) Time The human reaction time can be defined as a measure of the time taken to respond to any stimulus. There are many factors that could affect the reaction time, some of which are age, gender, personal characteristics, distraction, sickness, tiredness or intoxication level etc. The reaction time of the drivers has been widely studied due to the wide possible consequences. This is because a slower reaction time can lead to fatal accidents. While on the road, the driver comes across multiple stimuli and he has a choice to make from multiple possible responses. The reaction time of the drivers can be split up into the following components.

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(a) Mental Processing Time This is the time taken by the driver to perceive an incident on the road and to come up with a response. First the sensory organs sense the input which may be auditory ones or visual ones, the nerve impulses then pass from the receptor to the brain where the brain processes and recognizes with the help of stored information from memory, interprets the situation, decides on a response to be taken and finally programs the body movement mentally. For example, if a driver comes across a pedestrian, he first sees with his eyes and recognizes the situation. If his brain realizes that his driving speed and distance from the pedestrian could lead to a collision, the brain will select an appropriate response like turning the steering or applying the brakes. According to studies, the average mental processing time ranges from 0.5 seconds to 2 seconds (Triggs & Harris, 1982). Some studies also show brain reaction times as high as 7 seconds. The standard time adopted in United States is 1.5 seconds and in Australia it is 1.5 seconds. This time accommodates more than 90% of the different types of drivers who face simple and moderate level situations. In complex and unexpected situations, this time is definitely higher. (b) Body Movement Time Once the brain decides on an appropriate maneuver, the body muscles react accordingly. For the above scenario, the response would be to apply the brakes. This time taken for the driver’s body to carry out the selected action is called body movement time. In the above case, the driver has to lift his foot off the accelerator and depress the brake pedal. There is also a small time required for the brakes to engage. This time is highly variable again and depends on the braking style, urgency, vehicle condition etc. It varies from 0.3s to 1s. (c) The Mechanical Response Time Finally, it is the time taken by the mechanical device to respond and perform the maneuver after the driver has acted. Even after the driver has applied the brakes, the vehicle takes some time to complete the maneuver i.e. to come to a complete stop. This time is referred to as the mechanical response time or the maneuver time. This time varies for every vehicle and depends on numerous factors like the size of the vehicle, its type, gravity, road surface, weather conditions, average deceleration of the car, the condition of the car, its braking capacity, weight of the car, condition of the tyres, the incline of the road, the available traction etc. A standard deceleration rate adopted is 3.4 m/s2. 3.2 Stopping Distance With an idea about the vehicle stopping time, the stopping distance can be calculated. Stopping distance is the distance travelled by the vehicle during the three phases of vehicle stopping. For road safety applications, it can be said as the approximate distance before which a driver needs to see an obstacle in order for him to stop the vehicle without colliding.

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In general, given the acceleration and the velocity of a vehicle, the time taken for a driver to stop the vehicle is given by Ts = V/a. Where V Æ Velocity of the vehicle (km/h) a Æ deceleration rate (m/s2) The distance travelled is calculated by s = vit + ½ a t2. Since the initial velocity of a vehicle will be 0, the equation becomes s = ½ a t2. The stopping distance of a vehicle can thus be given by D = ½ aTs2 = V2/2a = ½ *V*Ts. According to the results of many tests and experiments conducted by the American Association of State Highway and Transportation Officials (AASHTO), the driver thinking distance is calculated by a standard equation given by DT = 1.47 Vt where t Æ brake reaction time (seconds) As a result of many studies conducted in many places, 2.5 seconds has been adopted as a standard for driver reaction time. This includes 1.5 seconds for his mental processing and 1 seconds for the body movement. Similarly, the braking distance is calculated by DB = 1.075 V2/ a As mentioned above, the deceleration rate of 3.4 m/ s2 is used by many as a standard to calculate the stopping distance. However, in more than 90% of the cases, the drivers are able to decelerate at a higher rate. Finally, the stopping distance is the sum of the driver’s thinking distance and the braking distance given by SD = DT + DB = 1.47 Vt + 1.075 V2/ a Suppose a person is driving a car or a light truck at a speed of 60 km/h. If the road surface is dry and the vehicle is well maintained with tires in good condition, an average alert driver can safely decelerate at the rate of about 3.4 m/s2. The friction can be assumed to be 0.75. In this case, it will take almost 5s for the vehicle to come to a complete halt. If 1.5s delay for driver’s thinking time is included, then the driver will be able to stop the vehicle only after 6.5s. By then the vehicle would have travelled a distance of more than 45 m. However, the braking skills of the driver improves with experience. The time taken and the distance taken to stop the 7

vehicle can be drastically reduced with more practise. Practically, all drivers are not so experienced. The road and vehicle condition also are not always the best. The above example clearly illustrates the severity of the situation. The average vehicle stopping distance assuming a well maintained car with an alert driver on a dry road for different car speeds is tabulated below in Table 1. It shows the stopping distance for drivers with 1.5 seconds reaction time and 2.5 seconds reaction time. It is obvious however that the actual stopping distances may vary considerably. Table 1: Vehicle stopping distances Vehicle Speed km/h 40 45 50 55 60 65 70 75 80 85 90

Reaction Distance 1.5 s m 17 19 21 23 25 27 29 31 33 35 37

Reaction Distance 2.5 s m 28 31 35 38 42 45 49 52 56 59 62

Braking Distance m 9 12 15 18 21 25 29 33 38 42 48

Total Stopping Distance 1.5 s m 26 31 36 41 46 52 58 64 71 78 85

Total Stopping Distance 2.5 s m 37 43 49 56 63 70 78 85 93 101 110

Crashing Speed 1.5 s km/h No crash No crash No crash No crash 2 32 46 57 66 73 79

4. THE TSH VEHICLE CONTROL SYSTEM As seen in the previous section, a few seconds could be the margin of safety that could save many lives. A small delay in the perception time of drivers could lead to fatal results. Today, intelligent automated systems are becoming part of our everyday life. If the most complicated task of perception-reaction while driving could be controlled with technology, it could reduce the chances of accident to a great extent. Continuous research has been going on to automate cars since 1920. After the first autonomous car was developed in 1980’s, major automobile companies like Mercedes-Benz, Nissan, Ford, General Motors, Toyota, Renault, Hyundai, Volvo etc. have started developing prototypes of autonomous vehicles. Even research organizations and universities are working in this area. The self-driving car developed by Google is one such autonomous car which is under experimentation (Driverless Car n.d., Autonomous Car n.d.).

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The autonomous vehicles are classified into 5 levels where level 0 vehicles have no automated system. Level 1 and 2 vehicles have limited automation and the drivers can take control any time. In level 3 and 4 vehicles, full automation can be given to the vehicle in limited safe environments like highways, good weather etc. The level 5 vehicles are fully automated and require no human intervention at all. The anticipated advantages of having an automated vehicle control system are obvious and are listed below: • • • •

Smooth and safe traffic flow in highways. Better driving ability for the physically challenged. Tireless and stress free driving experience even during long drives. Reduction in collisions.

In this work, a VANET based vehicle control system is proposed. Here a Hybrid VANET system is developed in which the vehicle nodes, pedestrian body unit and the Road side unit can communicate wirelessly with each other. The vehicular unit has a controller which constantly communicates with the pedestrians on the road to check for potential accidents. Whenever it senses trouble, it sends a control signal to the automated braking system to apply the brakes. Here the level 1 autonomous vehicle with only braking control is sufficient. The system has an edge over the reaction time of even the most experienced drivers. 4.1 Integrating Pedestrian Body unit with VANET The first part of this work is to develop an integrated system in which the VANET can communicate with a pedestrian body unit. This will improve the detection of human walking on the sides of the road and on crosswalks. This integrated system will give an extra level of protection to careless or vulnerable pedestrians like kids, old age or handicapped people with limited vision or slow mobility and intoxicated pedestrians. The model of the integrated system is shown in Fig 1. It is comprised of vehicle nodes that communicate with roadside units and wireless pedestrian body units.

Fig 1: System Model 9

The framework of the entire system is shown in Fig 2. It can be seen in the diagram that the system has a vehicular unit and a pedestrian unit that communicates via the VANET. Each unit has a controller that takes care of the major tasks. The vehicle controller is implemented using an Arduino microcontroller that acts as the brain behind the whole system and controls all the actions. This is referred to as the TSH (time synchronized hybrid) vehicle control system, the details of which are explained below.

Fig 2: Schematic representation of the vehicle control system using VANET In the pedestrian side, the communication controller frequently broadcasts its position to all the oncoming vehicles that are in its communication range via the wireless transmitter. Concurrently, the wireless receiver on the vehicular side keeps listening for the broadcast messages. When a message is received, the TSH vehicle control system obtains its current location from its GPS. It then compares both the GPS coordinates with the pedestrian GPS coordinates that comes in the message. Along with the vehicle speed, acceleration, steering angle, pedestrians’ movement direction and distance information, the controller would check if an accident is bound to happen. If there is an accident possibility in the black spot, then the vehicle and pedestrian are said to be in the Bad Set boundary. Within the Bad Set, there is a Trap Set where the occurrence of collision cannot be prevented by any human initiated controls. The Bad Set boundary is pre-programmed into the microcontroller and depends on factors like vehicle speed, acceleration, steering angle, size, road conditions, pedestrians’ movement direction and distance. The TSH microcontroller then sends a control signal through its logic circuits, to the advanced emergency braking system (AEBS). The TSH also simultaneously sends 10

an alert message to the communication controller in the pedestrian unit. When an alert is received by the receiver, the communication controller sends an audio alert to the pedestrian via a speaker. AEBS is also known as the autonomous emergency braking system and is a road vehicle safety system that is beginning to get mandatory for all new vehicles, starting with heavy vehicles, as per the United Nations Economic Commission for Europe’s (UNECE) announcement. AEBS automatically applies the emergency brakes in situations of impending collision. In most cases these may prevent crashes fully while in other unavoidable cases the speed of the vehicle is reduced after applying the brakes. The resulting low speed crash may prevent fatality and property damage caused by the collision. Some of these systems activate independent of any driver input while others provide braking assistance to the driver. In the proposed system, the controller of the TSH vehicle control system is connected with the AEBS, which in turn is connected to the vehicle braking unit. This is represented in Fig. 3.

1 ÆTSH Vehicle Control System 2 Æ Advanced Emergency Braking System (AEBS) 3 ÆBraking Unit Fig 3: Car with the TSH Vehicle Control System The block diagram of the TSH vehicle control system which was implemented using an Arduino microcontroller board is shown below in Fig. 4. The other microcontroller that could be used is the PIC 8051. The microcontroller constantly waits to receive the broadcast messages from the pedestrians. The vehicle sensors measures the speed, acceleration and 11

steering angle of the vehicle. The Arduino board gets this as input from the vehicle and its position information from its own GPS, which is the NEO6MV2 GPS module. It then calculates to find if the pedestrian is within the Bad Set boundry as mentioned above. If yes, a control signal is immediately sent to the AEBS which in turn activates the braking unit.

Fig 4: Block diagram of the TSH vehicle control system The working of the TSH vehicle control system is represented in the form of a flowchart in Fig 5.

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Fig 5: Process flow diagram of the TSH vehicle control system 4.2. Time Synchroization Algorithm An important criterion for the whole system to work accurately is that the clock time of all the nodes in the hybrid VANET system should be synchronized. The Hybrid Clock 13

Synchronization (HCS) protocol for time synchronization in a hybrid VANET network has been proposed by Dahlia S & CyrilRaj V (2014). It is assumed that the nodes are synchronized according to the HCS protocol. The same algorithm can be used for this hybrid VANET with pedestrian nodes. Each of the nodes that forms part of the network maintains a data that includes its own unique ID, a list of nodes that it is synchronized with, also called the ‘synch scale’ and a list of neighbouring nodes within its coverage area. The neighbouring nodes will include the vehicle nodes, RSUs and the pedestrian nodes. The neighbour list is maintained by periodically broadcasting its unique ID. The algorithm modified to accommodate the pedestrian node in the network can be explained as follows: Step 1: Watch for Initialization Any vehicle node or RSU randomly initiates the synchronization process. The pedestrian node does not initiate the process. This is because, when neither the RSU or the vehicle node has initiated the synchronization process in one full synchronization interval, then that implies there are no vehicles in the area. The pedestrian nodes have no reason to synchronize the clocks. Step 2: Synch initialization. Next the initiator will broadcast a Collection Message (CM) that has the collection request, the neighbouring node IDs and the reply message saquence. The other nodes will know that it does not have to initiate and will go on to send the information. Step 3: Send reply message. On receiving the collection message, the node will check the reply sequence and find its time slot. It will then set a timer. When the time expires it will send a Reply Message (RM) to the initiator. The reply message contains the synch scale, unique ID and the time difference of the node. The time difference is the deviation of the nodes clock with respect to a standard clock e.g., GMT. The format of the reply message is shown in Fig. 6. Synch Scale Unique ID Clock Deviation Fig. 6. Reply message format Step 4: Reply collection. The initiator waits for a time period, Treply to get the reply message from all its neighbouring nodes. Treply = [(N+1)*R] where, N Æ The number of neighbours, R Æ The duration of one reply message

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Step 5: Selecting the synchronizer. The initiator will compare the synch scales of all the neighbuors with its own synch scale. If any vehicle node has a higher scale than its own synch scale, then that becomes the synchronizer. The initiator will then send a message to that node informing that it is the new synchronizer. It will also send a list of all the vehicles’ IDs.On the other hand, if the initiator itself is the node with the highest synch scale then it will continue and take up the role as synchronizer. Step 6: Synchronization. The synchronizer will edit its synch scale by updating the list of synchronized group members. It will then send a Clock Adjustment Message to all its group members. The message consists of the synchronizers time difference and all receivers IDs. Step 7: Clock adjustment. Finally the individual nodes will adjust their own clock time and also its synch scale. 5. Black Spot Management In this section, the hybrid VANET described above is used to manage and prevent accidents at Black Spots. Accidents Black Spot is an area where drivers cannot predict any oncoming obstacle or sharp curve or crossing from the backside of a stationery object because it is visually hidden. The statistical reports on traffic accidents show a regular and continuous occurrence of crashes in black spots. The scenario considered here is that of a pedestrian crossing an intersection with no traffic controls. This is illustrated in Fig 7. A large truck parked in the side of the highway conceals the pedestrian’s view of the oncoming vehicles. Suppose a vehicle approaches as shown in the figure, it creates a “black spot”. There are high chances of the occurrence of accident. Similar case occurs during severe snowfall when a pile of snow is deposited in the sidewalk and obscures the pedestrian’s view. The situation is considered for a single lane road where the vehicles cross the intersection one at a time.

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Fig 7: An example of a black spot As seen in the previous section, using the TSH vehicle control system, the approaching vehicle gets the position information about the pedestrians present in the vicinity. It also gathers other details and calculates the possibility of a crash. If there is a chance of accident, it sends a control signal to the AEBS as well as an alert message to the pedestrian. However, in all the level 1 autonomous vehicles, the driver has the ability to override the automatic braking control by manually pushing the brakes. In such cases the TSH vehicle control system cannot guarantee collision avoidance. In the next sections, the two different scenarios will be discussed in detail. First, it is assumed that the driver overrides the automated braking control and takes manual control. In this case, the driver and the pedestrian get the audio alerts via the speaker. The accident avoidance completely depends on the decision making capability of the driver and the pedestrian on being alerted. This decision making is explained using a game structure in section 5.1. Secondly, the scenario when the TSH vehicle control system automatically applies the brakes will be discussed. The vehicle control system was evaluated mathematically and analysed using different experiments which is explained in section 5.2. 5.1 The Game Structure In the previous section it was seen that the driver reaction time includes the mental processing time, body movement time and mechanical response time. The body movement time is the time taken to react to the visual stimulus. In this case, the body movement time for the pedestrian is the time taken to stop or move backwards. In the absence of the automatic vehicle control, the drivers’ reaction is the time taken to lift his foot from the accelerator and apply the brakes. When the brain perceives the situation, it has to take a decision on how to react. The decision taken plays a major role in avoiding or causing a collision as well as the severity of 16

collision. This strategic decision-making can be represented as a game in normal-form game theory, which is explained next. The prevention of collision depends on the strategy of these two players. The game play is started when the driver and pedestrian come into the communication range of each other. The driver of the vehicle gets an alert about the presence of the pedestrian as soon as the vehicle controller receives the broadcast message. Under manual control, there are high chances that he reacts to the alert and applies the brake. There are also chances that he is distracted and does not get alerted. Also if he ignores the alert he would not apply the brakes. Similar is the case with the pedestrian when he receives the alert. The players here can be defined as, player 1 the vehicle and player 2 the pedestrian. In situations where there are two vehicles at the intersection, the players will be vehicle A and Vehicle B. The actions of the players can be given as below. The vehicle has the option to neglect the alert and continue to go with the same speed in which case it is not cooperating. On the other hand it can reduce its speed and gradually come to a stop at the crosswalk giving way for the pedestrian. This is the case of cooperation. The pedestrian also has few options. He can continue walking without bothering about the vehicle i.e. not cooperating or wait for the vehicle to stop and then accept the offer to cross the road (cooperating). The pedestrian will also not cooperate if he rejects the vehicles offer and does not cross the road. The strategies adopted here are, Player 1 has to reduce its speed and come to a stop to offer the crosswalk for player 2. If it does not slow down, it will not cooperate. Similarly, player 2 has to accept the right of way and cooperate in order to cross the road without any risk. If he does not accept the offer, he is not cooperating. The information shared between the two players is the exact time and the location on a real time basis, the actions of the other player, the strategies and the payoff functions. Payoff can be defined by the time delay by the actions that directly relates to the occurrence of collision. In all traffic related scenarios, time is the most crucial factor that has to be taken into account. When both the vehicle and the pedestrian adopt a cooperative strategy, it consumes some time but it ultimately prevents deadly collisions. Suppose a vehicle travelling at 50 km/h gets an alert about a pedestrian presence before 100 m. Then for the player 1, the actual time to cross the intersection from the time it receives the alert will be about 8 s. Similarly the average walking speed of a pedestrian is 5 km/hr. Here the average reaction time of the driver is taken as 2 s and that of the pedestrian is 1 s. If the road width of the single lane road is taken as 3.75 m, then the actual time for player 2 to cross the intersection after receiving the alert will be 10 s. The following calculations show the time delay for both the players under both actions. For player 1 (The vehicle) Actual time, T = 8 s Action1: Cooperation (Slows and stops)

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Stopping time delay = Actual time – (Deceleration time + Reaction time) = 8-(4+2) = 2 s Action 2: Non cooperation (Does not stop) Running time delay = Actual time – Actual time = 8-8 = 0 s For player 2 (The pedestrian) Actual time, T = 10 s Action1: Cooperation (Stops and accepts the right of way) Stopping time delay = Actual time – (Reaction time + Walking time) = 10-(1+3) = 6 s Action 2: Non cooperation (Does not stop) Running time delay = Actual time – Actual time = 10-10= 0 s The payoff table is shown in Table 2. It can be seen from the table that a delay is caused in the situation where both the vehicle and the pedestrian cooperates. However, this delay can eliminate the possibility of a collision and hence is the most ideal solution. In situations where either one of the player cooperates, the delay is not as much. In these cases the chance of accident is reduced considerably but not completely eliminated. Finally, in situations where both the players do not cooperate, there is no extra time consumed at all but it is the worst decision that will lead to a fatal crash. Table 2 Payoff table for the game Player 1/Player 2

Pedestrian stops and Pedestrian does not accepts the path stop 2,6 2,0 Vehicle stops 0,6 0,0 Vehicle does not stop 5.2 Mathematical Evaluation In this section, a mathematical formalism to describe the vehicle pedestrian scenario in the presence of the TSH vehicle control system is described. The main idea is that the accident caused between the vehicle and the pedestrian can be prevented by controlling the velocity and 18

displacement of the vehicle. Similarly an alert is given to the pedestrian to speed up or stop via the controller. All VANET equipped vehicles will have vehicle sensors for measuring the state of the vehicle which gives the position, velocity, acceleration, brake torque and pedal position of the vehicle. With the development of Intelligent Transportation System (ITS), it can be assumed that all vehicles will have the ability to automatically actuate the brake and throttle. In highways, the vehicles usually follow the predefined route and thus the trajectory of the vehicle can be defined. The collision scenario considered in this work is given in Fig 8. The vehicle emerges at the cross road where a pedestrian is coming from the other direction. Due to limited visibility there is a possibility of collision. The location of the potential crash near the intersection is marked in the figure.

Fig 8: Collision scenario To model the dynamic state of a single vehicle, a state space X × V is used, where X Æ the set of all possible longitudinal displacements V Æ the set of all possible longitudinal velocities The state of the vehicle is denoted by the vector (x, v) ∈ X×V, where x Æ the longitudinal displacement of the vehicle center of mass v Æ the tangential velocity of the vehicle center of mass The control space U: = [ uL , uH ] is used to represent the scalar combination of all possible pedal and brake torque inputs, where

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uH Æ the maximum throttle torque command uL Æ the maximum brake torque command The set of control signals for the vehicle is denoted as S (U). So u ∈ S (U) is called a control signal, where U ⊂ R , the set of real numbers. S (U) is the set of all functions

f : R → U ∪ {0} such that f (t) = 0 for all t < 0. Similarly, the control space of the pedestrian is

given by U: = [ uL , uH ] which represents the combination of all possible brain speed or stop commands. Here ‫ݑ‬௅ is the brain stop command and ‫ݑ‬ு is the brain speed command. The flow of the system is defined as the evolution of the vehicle state as time proceeds. It is given by the function ࢥ: R+ ×X×V× S (U) Æ X×V, where R+ Æ non-negative real numbers. This flow is generated by a controlled dynamic system f: X×V×U ĺ X×V. For the initial state (x,v) ∈ X×V, the control signal u ࣅ S(U) and time t • 0, the state of the vehicle at time t is given as ࢥ (t, (x,v), u) ∈ X×V. Here ࢥ1 (t, (x, v), u) ∈ X is the longitudinal displacement and ࢥ2 (t, (x, v), u) ∈ V is the longitudinal velocity. The flow of the vehicle over an interval of time can be given by ࢥ ([0, t], (x, v), u) ∈ X×V. This will represent the trajectory of the vehicle and is shown in Fig 9.

Fig 9: A sample trajectory of a single vehicle Next, the modeling formalism is developed for the vehicle-pedestrian system using parallel composition. Here the state vector can be defined for the entire system as (x,v) := (x1, x2, v1, v2) ∈ X×V := X1 × X2 × V1 × V2 , where 20

(x1, v1) ∈ X1 × V1 is the state of the vehicle (x2, v2) ∈ X2 × V2 is the state of the pedestrian The control signal for the vehicle-pedestrian system can be given as u: = (‫ݑ‬ଵ ǡ ‫ݑ‬ଶ ) ‫א‬ ܵሺܷሻ ؔ ܵሺܷଵ ሻ ൈ ܵሺܷ ଶ ሻ where ‫ݑ‬ଵ  ‫ܵ א‬ሺܷଵ ሻ is the control signal of the vehicle and ‫ݑ‬ଶ ‫ܵ א‬ሺܷ ଶ ሻ is the brain control signal of the pedestrian. The flow of the entire system is given by ‫׎‬ሺ‫ݐ‬ǡ ሺ‫ݔ‬ǡ ‫ݒ‬ሻǡ ࢛ሻ ‫ ܺ א‬ൈ ܸ. The vector of displacements is represented by ‫׎‬ଵ ሺ‫ݐ‬ǡ ሺ‫ݔ‬ǡ ‫ݒ‬ሻǡ ࢛ሻ ‫ ܺ א‬and the vector of velocities is represented by ‫׎‬ଶ ሺ‫ݐ‬ǡ ሺ‫ݔ‬ǡ ‫ݒ‬ሻǡ ࢛ሻ ‫ܸ א‬Ǥ The flow of the vehicle is represented by ‫׎‬ଵ ሺ‫ݐ‬ǡ ሺ‫ݔ‬ǡ ‫ݒ‬ሻǡ ࢛ሻ ‫ ܺ א‬ଵ ൈ ܸ ଵ and the flow of the pedestrian is represented by ‫׎‬ଶ ሺ‫ݐ‬ǡ ሺ‫ݔ‬ǡ ‫ݒ‬ሻǡ ࢛ሻ ‫ ܺ א‬ଶ ൈ ܸ ଶ . The problem of collision avoidance can be formalized as avoiding a Bad Set of vehiclepedestrian states, B ⊂ X where B represents the set of displacements in the path that leads to a collision. The Bad Set can be defined with path geometry as: B: = {x ∈ X ° x1 ∈ ] L1, H1[ and x2 ] L2, H2 [} Here, L1 and H1 are the lower and upper displacements along the vehicle path. Similarly, L2, H2 are the lower and upper displacements along the path of the pedestrian. The interval notation ] L, H [ to represent an open interval i.e. x ‫  א‬ሿ‫ܮ‬ǡ ‫ܪ‬ሾ if and only if L < x < H. If the vehicle and the pedestrian are within these bounds, then there will be a collision. This bad set is shown in Fig 10.

Fig 10: Bad Set within the lower and upper bounds of displacements

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The solution to this problem will be to design a controller to control the flow of the system and prevent it from entering the bad set B. For this, first a Trap Set is constructed given by T (v) ⊂ X. The velocity of the vehicle-pedestrian system parameterizes this Trap Set. The trap set corresponds to the set of vehicle and pedestrian displacements x ∈ X such that with the current velocities of the vehicle and the pedestrian v ∈ V, there cannot be a control signal u ∈ S(U) that will be able to prevent the accident eventually. This can mathematically be expressed as: ‫ܵ א ݑ׊‬ሺܷሻǡ ‫ ݐ׌‬൒ Ͳ T (v): = ܶሺ‫ݒ‬ሻ ؔ  ൜‫ ܺ א ݔ‬ฬ ൠ ‫ݏ‬Ǥ ‫ݐ‬Ǥ ߮ଵ ሺ‫ݐ‬ǡ ሺ‫ݔ‬ǡ ‫ݒ‬ሻǡ ‫ݑ‬ሻ  ‫ܤ א‬ This trap set is computed online and a control is applied if either the vehicle or the pedestrian enters the trap set boundary. If suppose the vehicle enters boundary of the trap set ଵ ǡ ‫ݑ‬௅ଶ ሻ is applied. This implies that the vehicle before the pedestrian, the control signal u* = ሺ‫ݑ‬ு accelerates and the pedestrian stops. Thus collision can be prevented. If on the other hand, the ଶሻ pedestrian enters the boundary of the trap set, then the control signal u* = ሺ‫ݑ‬௅ଵ ǡ ‫ݑ‬ு is applied to avoid the crash i.e. the vehicle brakes and the pedestrian speeds. As a result, the control prevents the flow from entering the bad set. For the sake of this experiment, only the second case is considered where the pedestrians’ position is checked to see if he enters the trap set. The control signal is then given by the TSH to the AEBS to apply brakes. 6. Simulation The hybrid VANET system with pedestrian unit was simulated using the GrooveNet simulator. In the simulation model, the vehicles were assumed to be running in a single lane highway. The incoming traffic flow was considered as approximately 1000 vehicles per hour. The distance between the crosswalk and the obstacle in our setup was assumed to be 0.75 m. The other parameters that were fixed for the simulation are shown in Table 3. Table 3: Simulation parameters Length of the Highway

18900×20m

Distance between two sensors

80 m

Transmission range of sensor node

100 m

Transmission range of vehicle nodes

250 m

Average packet loss ratio

15%

Average speed of vehicles

100 km/h

Simulation time

60 min 22

In the first experiment, the proposed system was designed and the possibility of accident was analysed for two different situations. As seen in the previous section, it is important that all the nodes are time synchronized, in order for the system to work properly. The first situation is when no alert is given and the driver reacts normally. The second one is when the drivers are alerted. The pedestrians speed considered here are from a minimum of 1 km/h to 6 km/h. The vehicle speeds taken are between 10 km/h to a maximum of 60 km/h. This speed is the vehicle speed when it nears the pedestrian when trying to avoid the crash. The pedestrian receives an alert when the vehicle is atleast 80 m away. The results of how the alert given to pedestrian could save a crash are shown in Fig 11.

Fig 11: Reduced accident possibility with the H-VANET alert system As can be seen from the figure, with no alert the pedestrians are in danger when the oncoming vehicle speed is as low as 11 km/h. On the other hand, when the drivers are alerted using the hybrid VANET system, the situation improves drastically. The pedestrians are in danger only when the vehicle speed is above 36 km/h. Let us take a particular case when the pedestrian speed is 5 km/h. It can be seen that with no alert system, the accident takes place even when the car is coming at a speed of 14 km/h. However, when an alert is given the accident occurrence can be prevented for speeds up to 40 km/h. This shows that the proposed system of sensing the situation and giving an alert ahead of time to the drivers can surely help the drivers to react in a smoother way. An alert given before few seconds can improve the scenario drastically by changing the way the driver reacts to the same situation.

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In the second set of experiments, signals given to the pedestrian were evaluated separately depending on the type of pedestrian. A case where the pedestrian attempts to cross the road in a non-regulated intersection is considered. Four types of situations were considered i.e. the pedestrian reacting slowly to his visual stimuli, pedestrian reacting fast to visual stimuli, pedestrian reacting slowly to audio alert and the pedestrians reacting fast to audio input. With the vehicle control system, the audio alert comes to the pedestrian at least 2 seconds before the vehicle reaches the crosswalk. When the pedestrian is distracted or if there is an obstacle blocking his vision, he does not look around before crossing. In the vehicle control system described above, the control to the pedestrian comes in the form of an audio input. The pedestrian has to react to the audio input and salvage the situation. The average reaction time according to different studies is set as 0.33 seconds for a visual input while for an audio input it is reduced to 0.28 seconds. There is always a ± difference between the fast reactors and the slow reactors. So for this simulation, the estimates used were 0.33+5= 0.38s and 0.33-5=0.28s as the reaction time of fast and slow pedestrian to visual stimuli. Similarly, 0.28+5= 0.33s and 0.285=0.23s are the reaction times for the fast and slow pedestrians to audio input. The scenario was evaluated for different vehicle speeds. The results of simulation are shown in Table 4. The maximal safe speeds for the car in the different cases of fast and slow reacting pedestrians with/without the alert are given. It can be seen that giving an auditory alert to the pedestrian drastically reduces the possibility of accident as opposed to as not giving an alert. Table 4: Maximum safe car speeds for different reaction times Pedestrian Speed (km/h) 2 3 4 5 6

Slow Pedestrian without alert

Safe Car Speed (km/h) Fast Pedestrian Slow Pedestrian without alert with alert

No crash 23 19 14 11

No crash No crash 40 27 20

No crash 25 22 17 13

Fast Pedestrian with alert No crash No crash No crash 42 36

In the next experiment, the game strategy followed by the driver and pedestrian is analysed. As seen in the game theory, the decisions taken by the driver and the pedestrian determine the occurrence of crash. There are four cases that can happen depending on whether the driver or the pedestrian cooperate. In the first two cases, the driver does not cooperate which is represented as DN. When the pedestrian cooperates, it is given as PC and for non-cooperation it is given as PN. It can be seen that when the driver cooperates (DC), as in the third and fourth cases, accidents can be avoided to a greater extent. The simulation results of this evaluation are shown in the Table 5 below. 24

Table 5: Decision making and the occurrence of crash Vehicle Speed DNPN DNPC DCPN (km/h) 40 No Crash No Crash No Crash 45 No Crash No Crash No Crash 50 No Crash No Crash No Crash 55 Crash No Crash No Crash 60 Crash No Crash No Crash 65 Crash Crash Close Stop 70 Crash Crash Crash 75 Crash Crash Crash

DCPC No Crash No Crash No Crash No Crash No Crash No Crash No Crash Crash

7. Experiments A set of real time experiments were conducted in a highway using a test vehicle. Table 6 lists the detailed system specification of the TSH vehicle control system that was used for the experiments. Table 6. Specifications Processor Memory External Memory Microcontroller Power Supply Transceiver Network Interface Operating System

64bits MIPS, 266 MHz 16KB 32KB flash Arduino Breadboard5V 250 kbit/s 2.4 GHz IEEE 802.15.4 ESP 8266 WiFi Module μC/OS-II

For experimental purpose, the pedestrian was assumed to be in a static position. Vehicles were driven by volunteers at different velocities in a stretch of highway. The distance considered was 100 m. A dummy doll was placed at one end which represented the pedestrian trying to cross the road. The roads were in dry condition. The vehicles were driven at different speeds above 60 km/h. This is because it is above this speed that there is a probability of accident even when the driver cooperates, as seen in the simulation results in section 6. The vehicle receives a message about the obstacle when it is at a distance of 100 m from the dummy doll. The distance covered for the vehicle to come to a full stop was measured. With manual control, after the driver sees the pedestrian, he applies the brakes manually. With the vehicle control system, the vehicle receives information about the pedestrian even before the driver can actually see. The brakes are automatically applied. Since the TSH vehicle control 25

system is connected with the AEBS, it applies the maximum power boost. Thus the stopping distance of the car is reduced as compared to the driver operated cars. The values are compared with the stopping distance of the manually operated cars. Table 7 shows the results observed. Table 7: Accident possibility with different vehicle speeds

VehicleSpeed (km/hr) 60 65 70 75 80 85 90 95 100

Manual Control Stopping Stopping Time Distance (s) (m) 6.3 Exactly touches 6.7 6 7.1 12 7.5 18 7.9 24 8.3 30 8.7 36 9.0 41 9.5 47

TSH vehicle control system Stopping Stopping Time distance (s) (m) 4.8 -51 5.2 -43 5.6 -35 6 -26 6.4 -17 6.9 7 7.2 Exactly touches 7.7 15 8 27

The first columm shows that with manual control at 60 km/h the vehicle comes to a stop exactly touching the obstacle. In all the other cases, it hits the obstacle and comes to a stop beyond the obstacle. In real time, accident could occur anytime above the speed of 60 km/h whereas according to the simulation results, accidents could be avoided for even higher speeds when the driver cooperates. It is purely dependent on the different responses of the driver and pedestrian. The distance beyond the obstacle at which the vehicle comes to a stop is given in the table. Using the TSH vehicle control system, the accident occurs for speeds above 90 km/h. For all speeds below that, accidents can be surely prevented. The values shown in the second columm shows the distance at which the vehicle comes to a full stop before the obstacle. The stopping distance of the vehicle both under manual control and the TSH vehicle control system are shown in Fig. 12 and Fig. 13. The point where the vehicle comes to a stop without hitting the pedestrian under manual control and the autonomous control is shown in the figures. With the TSH vehicle control system, for all vehicle speeds below 90 km/h, the vehicle is able to come to a stop before hitting the pedestrian. In all other cases, where the speed of the vehicle is higher, the vehicle stops beyond the pedestrian after hitting the pedestrian.

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50

Vehicle Speed (km/hr)

55 60 65 70 75 80 85 0

20

40

60

80

100

120

140

160

Stopping Distance (m)

Fig 12: Stopping distance of the vehicle with manual control

60

Vehicle Speed (km/hr)

65 70 75 80 85 90 95 100 0

20

40

60

80

100

120

140

Stopping Distance (m)

Fig 13: Stopping distance with TSH Vehicle Control System When hitting the pedestrian, the speed of the vehicle after decelaration determines the impact of the accident. The time taken for the pedestrian to react after receiving the audio alert could not be tested due to safety reasons.

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8. CONCLUSION In this work, a very novel TSH vehicle control system has been proposed that integrates a pedestrian unit with the vehicular unit using a vehicular ad hoc network (VANET). This hybrid VANET aims to reduce a major cause of road crash around the globe caused by. human error. The drivers travelling on the road may be in different situations – some inexperienced, tired, sick, physically challenged or aged, which will increase the time taken by them to perceive the scenario on the road and react to it. It is a well-known fact that even a small delay in reaction could lead to fatal accidents. The proposed system replaces this human thinking and reacting time by automatically sensing the road scenario and activating the braking system. The proposed system was simulated and the results prove that the road accidents can be reduced to a great extent. The autonomous vehicle control system was mathematically evaluated to see if collisions could be avoided. Field experiments were also conducted and the observations were quite impressive. As much as possible, simulations and experiments were conducted with the limited assumptions and implementations possible. The real traffic scenario has many more factors that need to be considered. The traffic regulations and patterns differ for each country. Considering all the factors is beyond the scope of this work. In future, more experiments can be done with steering actuation. Also the pedestrian movement can be analyzed in real time under safe conditions. With the development of wireless technology and vehicular communication in the last decade, the above proposed system is surely a feasible solution to reduce traffic deaths caused by human error in the near future. REFERENCES [1] Agarwal, N 2011, Estimation of pedestrian safety at intersections using simulation, Dissertation, University of Kentucky Doctoral Dissertations, viewed 20 March 2013 [2] Bhumkar, SP, Deotare, VV & Babar, RV 2012, ‘Accident Avoidance and Detection on Highways’, International Journal of Engineering Trends and Technology, vol. 3, no. 2, pp. 247-252 [3] Chandrasekaran, G 2009, ‘VANETs: The Networking Platform for Future Vehicular Applications’, Mobile Networking for Vehicular Environments, Los Angeles [4] Dahlia Sam & Cyril Raj, V 2014, ‘A time synchronized Vehicular Ad Hoc Network (HVANET) of roadside sensors and vehicles for safe driving’, Journal of Computer Science, vol. 10, no. 10, pp. 1617-1627 [5] Forian, D 2006, ‘Privacy Issues in Vehicular Ad Hoc Networks’, Proceedings of the 5th international conference on Privacy Enhancing Technologies, Heidelberg [6] Khairunnisa, S & Syah, I 2014, ‘Stressௗ; the vulnerability and association with driving performance’, International Journal of Cancer, vol. 11, no. 3, pp. 448–454

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[7] Markowski, MJ 2008, Modeling behavior in vehicular and pedestrian traffic flow by modeling behavior in vehicular and pedestrian traffic flow, thesis, University of Delaware Doctoral Dissertations, viewed 2 September 2013 [8] Rebai, M, Khoukhi, L, Snoussi, H & Hnaien, F 2012, Wireless Advanced Conference, June 25-27, Optimal placement in hybrid VANETs-sensors networks, London [9] Shinar, D 2012, ‘Safety and mobility of vulnerable road users: pedestrians, motorcyclists, and bicyclists’, Accident Analysis & Prevention, vol. 44, no. 1, pp. 1-2 [10] Sun, D, Benekohal, R & Waller, S 2003, Proceedings of 82nd Transportation Research Board Meeting, Modeling of motorist-pedestrian interaction at uncontrolled mid-block crosswalks, Washington [11] Treat, JR, Tumbas, NS, McDonald, ST, Shinar, D, Hume, RD, Mayer, RE, Stanisfer, RL & Castellan, NJ 1977, ‘Tri-level study of the causes of traffic accidents’, Report No. DOT-HS-034-3-535-77 (TAC), UMTRI Library Database [12] Triggs, T & Harris, W 1982, ‘Reaction Time of Drivers to Road Stimuli’, Human Factors Report No. HFR-12, Australia [13] Waizman, G 2012, Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, June 4-8, SAFEPEDௗ: Agent-Based Environment for Estimating Accident Risks at the Road Black Spots, Valencia, Spain [14] Waizman, G. & Aviv, T 2015, ‘Micro-Simulation Model for Assessing the Risk of Car-Pedestrian Road Accidents’, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, vol. 19, no. 1, pp. 63-77 [15] Zeadally, S, Hunt, R, Chen, Y, Irwin, A & Hassan, A 2012, ‘Vehicular ad hoc networks (VANETs): status, results, and challenges’, Telecommunication Systems, vol. 50, no. 4, pp 217–241 [16] Road Accidents http://www.visualexpert.com/Resources/roadaccidents.html [17] Stopping Distance [18] Stopping Sight Distance [19] VANETs http://en.wikipedia.org/wiki/Vehicular_ad-hoc_network [20] Driverless Car http://en.wikipedia.org/wiki/Google_driverless_car [21] Autonomous Car https://en.wikipedia.org/wiki/Autonomous_car

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