Effectiveness of a Risk Predictive Shared Steering Control Based on Potential Risk Prediction of Collision with Vulnerable Road Users

13th IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems 13th IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems 13th IFAC/IFIP/IFORS/IEA Symposium on Aug. 30 - Sept. 2, 2016. Kyoto, Japan Analysis, Design, and Evaluation of Human-Machine Available onlineSystems at www.sciencedirect.com Aug. 30 - Sept. 2, 2016. Kyoto, Japan Analysis, Design, and Evaluation of Human-Machine Systems Aug. 30 - Sept. 2, 2016. Kyoto, Japan Aug. 30 - Sept. 2, 2016. Kyoto, Japan

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IFAC-PapersOnLine 49-19 (2016) 084–089 Effectiveness Effectiveness of of a a Risk Risk Predictive Predictive Shared Shared Steering Steering Control Control Effectiveness of a Risk Predictive Shared Steering Control Based on Potential Risk Prediction of Collision with Vulnerable Road Effectiveness of a Risk Predictive Shared Steering Control Based on Potential Risk Prediction of Collision with Vulnerable Road Users Users Based Risk Prediction of Collision with Vulnerable Road Users Based on on Potential Potential Risk Prediction of Collision with Vulnerable Road Users Yuichi Saito*, Takayuki Mitsumoto**, Pongsathorn Raksincharoensak***

Yuichi Saito*, Takayuki Mitsumoto**, Pongsathorn Raksincharoensak***  Yuichi Saito*, Takayuki Mitsumoto**, Pongsathorn Raksincharoensak***  Yuichi Saito*, Takayuki Mitsumoto**, Pongsathorn Raksincharoensak*** * Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, * Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan (Tel: +81-42-388-7395; e-mail: [email protected]). * Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan (Tel: +81-42-388-7395; e-mail: [email protected]). * Tokyo University of and 2-24-16 Naka-cho, ** Tokyo University ofAgriculture Agriculture andTechnology, Technology, 2-24-16 Naka-cho,Koganei, Koganei, Tokyo, 184-8588, Japan (Tel: +81-42-388-7395; e-mail: [email protected]). ** Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan (Tel: +81-42-388-7395; e-mail: [email protected]). Tokyo, 184-8588, Japan (Tel: +81-42-388-7395; e-mail: [email protected]). ** University Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japanof (Tel: +81-42-388-7395; e-mail: [email protected]). ** Tokyo Tokyo University of Agriculture and 2-24-16 Naka-cho, *** Tokyo University of(Tel: Agriculture andTechnology, Technology, 2-24-16 Naka-cho,Koganei, Koganei, Tokyo, 184-8588, Japan +81-42-388-7395; e-mail: [email protected]). *** Tokyo University of(Tel: Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan +81-42-388-7395; e-mail: [email protected]). Tokyo, 184-8588, Japan (Tel: +81-42-388-7397; e-mail: [email protected]). *** Tokyo184-8588, University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, Japan (Tel: +81-42-388-7397; e-mail: [email protected]). *** Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan (Tel: +81-42-388-7397; e-mail: [email protected]). Tokyo, 184-8588, Japan (Tel: +81-42-388-7397; e-mail: [email protected]). Abstract: Elderly drivers are at particular risk for accidents due to age-related visual, cognitive, and Abstract: Elderly drivers are at particular risk for accidents due to age-related visual, cognitive, and physical impairments. Thisare study risk for predictive shared control based potential risk Abstract: Elderly drivers at proposes particularaarisk accidents duesteering to age-related visual,on and physical impairments. Thisare study risk for predictive shared control based oncognitive, potential risk Abstract: Elderly drivers at proposes particular risk accidents duesteering toon age-related visual, cognitive, and predictionimpairments. of collision withstudy vulnerable road users, such asshared cyclists urban roads. Under the driving physical This proposes a risk predictive steering control based on potential risk prediction of collision with vulnerable road users, such as cyclists on urban roads. Under the driving physical impairments. This study proposes a risk predictive shared steering control based on potential risk simulator experiment, investigated acceptance and the effectiveness of providing the auditory alert prediction of collisionwe with vulnerableits road users, such as cyclists on urban roads. Under the driving simulator experiment, investigated acceptance and the effectiveness of providing the auditory alert prediction of collisionwe with vulnerableitsroad as cyclists onthe urban roads. the driving and visualexperiment, information, andinvestigated implementing the users, shared such steering control in hidden risk Under situation. simulator we its acceptance and the effectiveness of providing the auditory alert and visualexperiment, information,we andinvestigated implementing the shared steering control in the of hidden risk situation. simulator its acceptance and the effectiveness providing the auditory alert and visual information, and the shared steering control in the risk Keywords: Safety, Potential Risk Prediction, Assistance, Shared Control, Driver Acceptance © 2016, IFAC (International Federation of Automatic by Ltd. All situation. rights reserved. and visual information, and implementing implementing theDriver sharedControl) steeringHosting control in Elsevier the hidden hidden risk situation. Keywords: Safety, Potential Risk Prediction, Driver Assistance, Shared Control, Driver Acceptance Keywords: Safety, Potential Risk Prediction, Driver Assistance, Shared Control, Driver Acceptance  Assistance, Shared Control, Driver Acceptance Keywords: Safety, Potential Risk Prediction, Driver  2009; Schall et al., 2013). Their supports assist mainly for  1. INTRODUCTION 2009; Schall et al., 2013). Their supports assist mainly for  driver’sSchall understanding of aTheir givensupports situation. However, 1. INTRODUCTION 2009; et al., 2013). assist mainly the for driver’sSchall understanding of aTheir givensupports situation. However, the 1. INTRODUCTION et al.,and/or 2013). assist mainly for arousing attention warning-type support may failthe to In 2015, the percentage of the population over the age of 65 2009; driver’s understanding of a given situation. However, 1. INTRODUCTION attention and/or warning-type support may failthe to In 2015, the percentage of the population over the age of 65 arousing driver’s understanding of a given situation. However, assure the elderly driver safety. Toyota (2009) investigated the reached 26% in Japan, Japan ispopulation the most progressive elderly arousing attention and/or warning-type support may fail to In 2015, the percentage of the over the age of 65 assure theattention elderly driver safety. Toyota (2009) investigated the reached 26% in Japan, Japan ispopulation the most progressive elderly arousing and/or warning-type support may fail to In 2015, the percentage of the over the age of 65 driver’s response whenToyota a rear-end collision warning society in26% the in world. Elderly humans are highly motivated to elderly assure the elderly driver safety. (2009) investigated the reached Japan, Japan is the most progressive elderly driver’s response whenToyota a rear-end collision warning society in26% the in world. Elderly humans are highly motivated to elderly assure the elderly driver safety. (2009) investigated the reached Japan, Japan is the most progressive elderly was activated. result presented that 30%collision of elderlywarning drivers improve Quality of Life, andhumans hope to continue driving for elderly driver’sThe response when a rear-end society the Elderly are highly motivated to was activated. The result presented that 30%collision of elderlywarning drivers improve of Life, andhumans hope to driving for society in inQuality the world. world. Elderly arecontinue highly motivated to elderly driver’s response when a rear-end could not take proper action to avoid crash. In general, it is not shopping daily necessities, going to hospital, participating in The result presented 30%Inofgeneral, elderly itdrivers improve of Life, and hope to continue driving for couldactivated. not take proper action to avoidthat crash. is not shopping Quality daily necessities, going to hospital, participating in was was activated. The result presented that 30% of elderly drivers improve Quality of Life, and hope to continue driving for easy for elderly drivers to cope with complex traffic situations community. However, elderly drivers are at particular risk for notelderly take proper action to avoid crash. In traffic general, it is not shopping daily necessities, going to hospital, participating in could easy for drivers to cope with complex situations community. However, elderly drivers are at particular risk for could not take proper action to avoid crash. In general, it is not shopping daily necessities, going to hospital, participating in require many stimuli under timesituations pressure. accidents due to age-related as which easy for elderlyprocessing drivers toof cope with complex traffic community. However, elderly functional drivers are limitations, at particular such risk for require processing of many stimuli under timesituations pressure. accidents due to age-related functional limitations, such as which easy for elderly drivers to cope with complex traffic community. However, elderly drivers are at particular risk for Braking systemsunder (AEBS) been visual, cognitive, physicalfunctional impairments (e.g., Horswill et Autonomous requireEmergency processing of many stimuli timehave pressure. accidents due to and age-related limitations, such as Autonomous Emergency Braking systemsunder (AEBS) been visual, cognitive, physicalfunctional impairments (e.g., Horswill et which which processing of many stimuli timehave pressure. accidents due to and age-related such as alreadyrequire introduced to the markets, and humans positively al., 2008). Several studies (e.g., Benglerlimitations, et(e.g., al., 2014) have Autonomous Emergency Braking systems (AEBS) have been visual, cognitive, and physical impairments Horswill et already introduced to the markets, and humans positively al., 2008). Several studies (e.g., Bengler et al., 2014) have systems (AEBS) have been visual, cognitive, anddrivers physical Horswill et Autonomous evaluated the Emergency functions ofBraking AEBS. However, the AEBS may reported thatSeveral elderly tendimpairments toBengler be easyetto(e.g., commit a fault. already introduced to the markets, and humans positively al., 2008). studies (e.g., al., 2014) have the functions of AEBS. the AEBS may reported thatSeveral elderlystudies drivers (e.g., tend toBengler be easyetto al., commit a fault. already introduced to the markets,However, and humans positively al., 2008). 2014) have evaluated not be always powerful enough to cope with any given the functions AEBS. However, the AEBS may reported elderly drivers tend to be easy to commit aa fault. Advancedthat Driver Assistance Systems (ADAS) can be evaluated not be always powerfulof enough to cope with any given evaluated the functions of AEBS. However, the AEBS may reported that elderly drivers tend to be easy to commit fault. Suppose vulnerable roadto users (VRU) suddenly Advanced Driver Assistance Systems (ADAS) can be situation. not be always powerful enough cope with any given categorized Driver into fourAssistance functions: (a) perception enhancement, situation. Suppose vulnerable roadto users (VRU) suddenly Advanced Systems (ADAS) can be not be always powerful enough cope with any given intend to cross the vulnerable narrow roadroad withusers short(VRU) time margin to categorized into fourAssistance functions: (a) perception enhancement, Suppose suddenly Advanced Systems (b) arousingDriver attention to potential risks, (ADAS) (c) enhancement, settingcanoff bea situation. intend to cross the vulnerable narrow roadroad withusers short(VRU) time margin to categorized into four functions: (a) perception situation. Suppose suddenly collision. Such AEBS reach its limit in unexpected events (b) arousing attention to potential risks, (c) setting off a intend to Such cross AEBS the narrow with in short time margin to categorized functions: (a) perception enhancement, warning, andinto (d)four automatic safety control (Inagaki, 2006). collision. reachroad its limit unexpected events (b) arousing attention to potential risks, (c) setting off a intend to cross the narrow road with short time margin to because of of its information acquisitionevents and warning, and attention (d) automatic safety control (Inagaki, (b) arousing tocomplement potential risks, (c) setting 2006). offfora collision. Suchlimitations AEBS reach limit in unexpected because of limitations of information acquisition and ADAS are designed to driver capabilities warning, and (d) automatic safety control (Inagaki, 2006). collision. Such AEBS reach its limit in unexpected events functions by the machine, and a short ADAS areanddesigned to complement driver (Inagaki, capabilities for information warning, (d) automatic safety control because of analysis limitations of information acquisition and perception, situation recognition, action selection, and 2006). action information functions by the machine, and a short because of analysis limitations ofsuch information acquisition and ADAS are designed to complement capabilities for time allowance. Against athebackground, wea short have perception, situation recognition, actiondriver selection, and action ADAS are designed to complement driver capabilities for information analysis functions by machine, and implementation in a recognition, dynamic environment (Inagaki, 2008). time allowance. Against suchby athebackground, wea short have perception, situation action selection, and action information analysis functions machine, and developed the ADAS for elderly drivers with intelligence implementation in a recognition, dynamic environment (Inagaki, 2008). Against such a drivers background, we perception, and action One way to situation reduce number environment ofaction trafficselection, accidents caused by time developed the ADAS for elderly with intelligence time allowance. allowance. Against such background, we ethave have implementation in athe (Inagaki, 2008). on the expert driver model a(Raksincharoensak al., One way to reduce thedynamic number environment of traffic accidents caused by based developed the ADAS for elderly drivers with intelligence implementation in a dynamic (Inagaki, 2008). elderly drivers could be to develop proactive safety based on the expert driver model (Raksincharoensak et al., developed the ADAS for elderly drivers with intelligence One way to reduce the number of traffic accidents caused by 2015). It is often pointed out that expert drivers perform the elderly could be toof develop proactive safety on the expert driver model (Raksincharoensak et al., One waydrivers to reduce the number traffic accidents caused by technologies. Davidse (2006) presented theproactive weaknesses of based 2015). It is often pointed out that expert drivers perform the based on the expert driver model (Raksincharoensak et al., elderly drivers could be to develop safety hazard-anticipatory drivingout based on prior-experience and/or technologies. Davidse (2006) presented theproactive weaknesses of 2015). It is often pointed that expert drivers perform the elderly drivers could be to develop safety hazard-anticipatory drivingout based on prior-experience and/or elderly humans, driving-related difficulties and assistance technologies. Davidse (2006) presented the weaknesses of 2015). It is often pointed that expert drivers perform the in potential risk situations. Thus, the potential risk elderly humans, driving-related difficulties and assistance hazard-anticipatory driving based on prior-experience and/or technologies. Davidse presented weaknesses of knowledge knowledge in potential risk situations. Thus, the potential risk needed, and showed that(2006) can conclude that the the assistance most hazard-anticipatory driving based on and/or elderly humans, driving-related difficulties and assistance based control modifies theprior-experience safe path and/or safe needed, and showed that can conclude that the assistance most prediction knowledge in potential risk situations. Thus, the potential risk elderly humans, driving-related difficulties and assistance based control modifies theThus, safe path and/or safe needed will:showed (1) draw attention to approaching traffic,most (2) prediction the potential risk knowledge in potential risk situations. needed, and that can conclude that the assistance speed in thebased time horizon of approximately 4-5 seconds before needed and will:showed (1) draw attention to approaching traffic,most (2) prediction needed, that can conclude that the assistance control modifies the safe path and/or safe speed in thebased time horizon of approximately 4-5 seconds before signal road users located in the driver’s blind spot,traffic, (3) assist prediction control modifies the safe path and/or safe needed will: (1) draw attention to approaching (2) accident risk becomes imminent. signal road in the driver’s blind spot,traffic, (3) assist needed will:users (1) located drawtheir attention to approaching (2) the approximately 4-5 4-5 seconds seconds before before speed in horizon accident risk becomes imminent. the driver directing to relevant information, speed in the the time time horizon of of approximately signal roadin located in attention the driver’s blind spot, (3) assist the the driver inusers directing their attention to relevant information, the accident risk becomes imminent. signal road users located in the driver’s blind spot, (3) assist study proposes a shared steering control system based on and (4) provide prior knowledge on the next traffic situation. This the accident risk becomes imminent. the in directing their attention to relevant information, study proposes a shared steering control system based on and driver (4) provide prior knowledge on the next traffic situation. This the driver in directing their attention to relevant information, potential risk prediction of collision with VRUsystem on urban roads. Davidse (2006) argues that it is critically important to bear in This study proposes a shared steering control based on and (4) provide prior knowledge on the next traffic to situation. potential risk prediction of collision with VRUsystem on urban roads. Davidse (2006) argues that it is critically important bear in This study proposes a shared steering control based on and (4) provide prior knowledge on the next traffic situation. The assistance system performs the risk predictive steering mind the possibilities and limitations of elderly drivers while potential risk prediction of collision with VRU on urban roads. Davidse arguesand that it is critically important to bear in The assistance system performs thewith riskVRU predictive steering mind the(2006) possibilities of elderly drivers while prediction of collision urban roads. Davidse (2006) argues thatlimitations it is interface. critically important to bear in potential inrisk a situation-adaptive manner before a on moving object, designing the human-machine Caird drivers et al. (1998) The assistance assistance system performs performs the risk risk predictive steering mind the possibilities and limitations of elderly while control control in a situation-adaptive manner before a moving object, designing the human-machine interface. Caird et al. (1998) The system the predictive steering mind the possibilities and limitations of elderly drivers while i.e. cyclist, suddenly intends to cross a narrow road. The and Gardner ethuman-machine al. (1997) presented the functional limitations control in aa situation-adaptive situation-adaptive manner before moving object, designing the interface. Caird et al. i.e. cyclist, suddenly intends manner to cross a narrow road. The and Gardner al. (1997) presented the functional control in before aa moving object, designing theet human-machine interface. Caird principles et limitations al. (1998) (1998) problem with such an early intervention support is that it is not and relevant human-machine interface design for i.e. cyclist, cyclist, suddenly intends to cross cross support a narrow narrow road. The and Gardner et al. (1997) presented the functional limitations problem with such an early intervention is that it is not and relevant human-machine interface design principles for i.e. suddenly intends to a road. The Gardner et al. (1997) presented the functional limitations and clear whether the system is allowed to implement a steering elderly drivers. Today, some studies have made to develop problem with such such an early earlyisintervention intervention support is that that it is is not not and relevant human-machine design principles for clear whether the system allowed to support implement a steering elderly drivers. Today, some interface studies have made to develop problem with an is it and relevant human-machine interface design principles for assistance control autonomously based on potential risk proactive safety technologies which complement thetodegraded clear whether the system is allowed to implement a steering elderly drivers. Today, some studies have made develop assistance control autonomously based on potential risk proactive safety technologies complement thetodegraded whether the system allowed to implement steering elderly drivers. Today, somewhich studies made develop prediction whether theis driver would theasystem’s driving performance (Entenmann et complement al..have 2000; Davidse et al., clear assistance and control autonomously based accept on potential potential risk proactive safety technologies which the degraded and whether the driver would accept the system’s driving performance (Entenmann et complement al.. 2000; Davidse et al., prediction assistance control autonomously based on risk proactive safety technologies which the degraded prediction and and whether whether the the driver driver would would accept accept the the system’s system’s driving performance (Entenmann et al.. 2000; Davidse et al., prediction driving performance (Entenmann et al.. 2000; Davidse et al.,

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decision and action. This paper aims to answer the following two research questions: (1) How much can the assistance system reduce the risk of accidents, e.g. the collision velocity to VRU and (2) Is the assistance system acceptable to drivers.

85

Dx X

Dy

θ

α

Y

Vc

w

Vo

Cyclist

Host vehicle

2. RISK PREDICTIVE SHARED STEERING CONTROL

Fig. 1. Use case scene Relative distance Dy [m]

Sharing of control can be distinguishable into four type: (a) extension type, (b) protection type, (c) relief type, and (d) partitioning type (Inagaki, 2003). The embodied control is called shared control (Abbink et al., 2012), and it aims to enhance the capabilities of both the human and the ADAS. The driver assistance system with risk predictive steering control has two functions. One is to encourage elderly drivers to pay attention to the potential risk on which the VRU might intend to cross a road. The other is to perform a shared steering control of extension type that guides elderly drivers to ensure a sufficient lateral distance between the host vehicle and the VRU. It is expected that the AEBS can activate normally in time-critical situations because the time to collision would extend by ensuring a sufficient lateral distance.

Vo = 16 m/s

Relative Distance Dy [m]

The mathematical VRU model of sudden crossing was proposed by Tsuyuki et al. (2013) in the previous study. Potential risk prediction scheme is briefly presented. Suppose that the cyclist is traveling on the side of a narrow road and the host vehicle is approaching from behind the cyclist, as shown in Fig. 1. The unsafe behaviour of the VRU is defined as that the VRU intends and initiates crossing the road suddenly without paying attention behind them. Expert drivers tend to consider potential hazard components. In this scene, reducing the velocity and/or ensuring the appropriate lateral distance is desirable in order to prepare for avoiding contacts with the VRU. The VRU model shows that the VRU travels straight along the narrow road boundary, and intends crossing at their timing with keeping the constant velocity. In Fig. 1, V0 indicates the host vehicle velocity when the VRU intends crossing, Vc indicates the VRU velocity, 𝛼𝛼 =180 degrees indicates the view angle of the VRU, the crossing angle, 𝜃𝜃, and the time of onset of crossing, tc, are unpredictable parameters, Dx and Dy indicate the longitudinal and the lateral distances between the host vehicle and the VRU, and w is the width of the host vehicle. Tsuyuki et al. (2013) formulated the conditions of the host vehicle velocity with which the host vehicle is able to avoid the collision with the VRU who initiates crossing suddenly. The conditions of the host vehicle velocity to avoid crash can be classified into two cases: (i) when it is required that the host vehicle passes the VRU at constant speed and (ii) when it is required that the host vehicle implements the AEBS with the maximum deceleration 𝑎𝑎𝑚𝑚𝑚𝑚𝑚𝑚 after a certain recognition time 𝜏𝜏𝑏𝑏 when the VRU initiates crossing. The host vehicle velocity V0 must satisfy their conditions against any combination of crossing angle 𝜃𝜃, e.g. the range from 0 to 30 degrees, and the VRU velocity Vc, e.g. the range from 0 to 20 km/h. The safety velocity is a velocity which is safe enough to avoid the vehicle-cyclist collision, and it can represent as the safety velocity map of the host vehicle based on the previous study (Tsuyuki et al., 2013). Fig. 2 shows the safety velocity on Dx – Dy plane as counter map

12 m/s 10 m/s

vc_max =5 km/h

Vo = 16 m/s 14 m/s

12 m/s

10 m/s

vc_max =10 km/h

Relative distance Dy [m]

2.1 Potential Risk Prediction

14 m/s

Host vehicle velocity

10 m/s

14 m/s

Vo = 18 m/s

vc_max =15 km/h Relative distance Dx [m]

Fig. 2. Safety velocity map under the condition of 𝑎𝑎𝑚𝑚𝑚𝑚𝑚𝑚 =0.7G, 𝜏𝜏𝑏𝑏 =0.25 seconds, w=1.8m, Vc_max=5, 10, and 15km/h, and 𝜃𝜃𝑚𝑚𝑚𝑚𝑚𝑚 =30degrees. The VRU position denotes Dx=0 and Dy=0. These maps mean the potential risk area which collision with the VRU is unavoidable by activating the AEBS with the defined specification when the VRU initiates crossing the road. Thus, drivers are required to reduce the velocity and/or to extend the lateral distance depending on the current velocity and the current lateral distance in order to avoid entering into the potential risk area. 2.2 Path Planning and Shared Steering Control Design The potential risk prediction based reference driver model was constructed. Fig. 3 shows the block diagram of shared steering control which guides the driver to ensure a sufficient lateral distance that the assistance system can avoid the vehicle-VRU collision by activating the AEBS. Desired path of Human driver

Safety Speed Map

Yd Human Driver

Path Generator

V , From Vehicle

Ys

*

Reference Driver Model

Td

 sw* Torque Generator

Ta





 To

Steering

V Steering Assistance System

 sw

TSAT From Vehicle

From Vehicle

Fig. 3. Block diagram of risk predictive shared steering control 1) Path planning Suppose the vehicle is approaching the cyclist at a speed of 10 m/s. The detected cyclist’s velocity is 4.17 m/s. The assistance system has multiple maps depending on cyclist’s velocity, as 91

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shown in Fig. 2, and selects the appropriate map based on the detected cyclist’s velocity. Thus, the driver assistance system can recognize the potential risk area which the AEBS may not be powerful enough to cope when the cyclist initiates crossing the road. When the assistance system detects the VRU, the assistance system determines the reference path based on the selected safety velocity map and depending on the current host vehicle velocity V0, as shown in Fig. 4. The minimum-required lateral distance is d m to avoid entering into the potential risk area. The reference path is taken into account the vehicle width w, and it is offset so as to avoid the potential risk area. Relative distance Dy [m]

10 m/s

Potential risk area

2.3 Human-Machine Interface and Interaction Design Based on the information-processing model (Parasuraman et al., 2000), the system was designed in the following ways: i.

vc_max =15 km/h

d w/2 V0=10 m/s

ys*

Reference path

Relative distance Dx [m]

Caution! Cyclist crossing!

Fig. 4. Reference path

横断注意 !

2) Reference driver model The referenced steering angle is calculated based on the preview control, as similar to the general 1st order look-ahead driver model. The look-ahead driver model with the steering angle input can be expressed as follows: *  sw (t ) 

h y sr (t ), 1  Tn s

Fig. 5. Human-machine interface with a head-up display ii.

(1)

ysr (t )  ys*  ys (t )  ys*  yc (t )  TpV t  t ,

(2)

where ys* is the desired lateral position, ys is the predicted lateral position of the vehicle at the preview point, yc is the lateral position of center of gravity,  is the yaw angle, Tp is the preview time, Tn is the delay time, and h denotes the corrective steering angle gain. 3) Shared control manner The driver’s torque Td, the assistance torque Ta, and the selfaligning torque are input on the steering wheel. The calculated steering angle 𝜃𝜃𝑠𝑠𝑠𝑠 ∗ based on referenced driver model is translated into the assistance torque Ta. In the left-hand traffic, the assistance torque Ta is calculated as follows:  K  * (t )   sw (t )  Ta (t )   a sw 0 

When the assistance system detects a cyclist ahead and the estimated relative distance is less than 60 m, the assistance system gives an auditory alert (beep sounds) at frequency of approximately 1.6 kHz, and the total length of alert is 1 second. In addition, the assistance system provides visual information with a head-up display in order to encourage the driver to pay attention to the potential risk on which the cyclist might intend to cross the narrow road, as shown in Fig. 5. The total length of visual information is 2 seconds. We designed their interface by referencing to design principles for elderly humans, e.g. use white colors on a black background, and use auditory signals in the range of 1.52.5 kHz range (Caird et al., 1998).

if y s  y s (t )  0 otherwise, *

iii.

(3)

where Ka denotes the steering torque gain. If the displacement between the desired lateral position ys* and the predicted lateral position ys of the vehicle at the preview point is a negative value, there is no assistance. The assistance system guides so as to avoid entering into the potential risk area only when the displacement between ys* and ys is a positive value. The assistance system only gives the opportunity to encourage avoiding the potential risk area, and the path-following is not intended. Therefore, the driver needs to take a corrective steering behaviour after the assistance system implemented the steering control.

iv.

If the driver initiates the steering action to ensure or extend the lateral distance between the host vehicle and the cyclist, the assistance system performs the steering control to lead to the reference path in order to avoid entering potential risk area based on the safety velocity map and depending on the current host vehicle velocity. The human should be basically in command because the machine’s decision may not be always correct. In this scene, actions that the human or the machine can take are distinguishable: (a) to reduce the velocity and (b) to ensure or extend the lateral distance. The outcome of the machine’s or the human’s decision depends on situations and intents, and their decisions may conflict each other. The criterion when to initiate the steering control based on human-initiated strategy is over 5 degrees as the driver’s steering amount. In addition, the driver can override the assistance system by adding more steering torque into the steering wheel when he/she determines that the machine’s intention is inappropriate. The assistance torque is limited to 2Nm. Moreover, the steering control is not activated if the driver performs the braking action after the assistance system provided the visual and the auditory information, and is cancelled if the driver performs the braking action after the steering control. When the driver passed the cyclist after the steering control, the steering control is deactivated. 3. DRIVING SIMULATOR EXPERIMENT

We investigated the effectiveness of the risk predictive shared steering control as well as evaluation of the driver acceptance. 92

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The experiment was conducted with the approval of the ethics committee at Tokyo University of Agriculture and Technology.

3.3 Procedure The experiment was conducted in one day for each participant. First, each participant read and signed a written consent forms, and informed consent was obtained from all participants. After receiving written instructions on the purpose, driving tasks, and procedure, the participants did practice drives to familiarize themselves with the simulator. In the training trials, participants drove freely the simulator that is not equipped with the assistance. After the training, the trial driving without assistance was scheduled as baseline (w/o assist (1st trial)), and the trial repeated three times. Next, participants received the introduction about the ADAS by using introduction movies. After receiving the introduction, the participants answered the acceptance questionnaire (pre) developed by Ven der Laan et al. (1997). After the training trial was performed once with assistance, the trial driving with assistance repeated three times (with assist condition), and the participants answered the Ven der Laan questionnaire (post) again. Finally, the trial driving under w/o assistance repeated three times again (w/o assist (2nd trial)).

3.1 Apparatus The experiment was conducted with Hexapod Stewart Platform motion-base driving simulator. The driving simulator consists of a host computer, a visual and an audio system, a steering system and a motion controller. The driving simulator was equipped with the same driver operational interfaces as real vehicle. The vehicle dynamics is precisely calculated in the simulation computer, and the vehicle has an automatic transmission system. Three image-generating computers generate the driving view. The view was shown to the driver using three projectors for the semi-cylindrical front screen. 3.2 Participants Fifteen elderly drivers (participant E1-E15, 8 males and 7 females) between the ages of 66-78 (mean: 68.5, s.d.: 3.07) who periodically drive (once per week - daily) participated. Elderly driver’s corrected visual acuity was over 0.7 (mean: 0.88, s.d.: 0.12) as a Landolt ring vision test.

3.4 Measures

3.3 Driving Tasks

The following measurements were collected at a frequency of 120 Hz during the experiment: speed [km/h], accelerator and brake pedals output [mm], lateral position [m], yaw angle [degrees], steering angle [degrees], estimated driver’s torque [Nm], system’s assisting torque [Nm]. In addition, the following data were calculated for each trial per participant:

A 2km, one lane, straight urban road was used. The width of lane was 5 m. There were buildings, oncoming vehicles, and cyclists around the road, as shown in Fig. 6. In the urban road, four cyclists was traveling while keeping 15 km/h on the left side of the narrow load, as shown in Fig. 7, and each participant approached behind the cyclist. In this urban road scenario, there was no event that the cyclist initiates crossing the road suddenly.

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Maximum lateral distance [m] Predicted value of the time to reach the cyclist in when the driver implemented the steering action [sec] Number of times that the vehicle entered into the potential risk area [-] Collision velocity with virtual cyclist [km/h]: Assuming that the virtual cyclist initiates to cross the road, we calculated the collision velocity under the activated AEBS. Although there is no event that the cyclist initiates crossing the road in this experiment, suppose a virtual cyclist suddenly intends to cross a narrow road at a speed of 15km/h and a crossing angle of 30degrees when the vehicle enters into the view angle of the cyclist, as shown in Fig. 1. We postulate that the assistance system will implement the AEBS to avoid crash when it recognize the darting-out cyclist. The collision velocity is affected by the initial velocity when the assistance system activates the AEBS, and the lateral distance. 4. RESULTS AND DISCUSSIONS

Fig. 7. Scenario Participants were instructed to drive safely while keeping at 40km/h in the left-hand side of the narrow road. The task imposed was to overtake the cyclists safely while paying attention the surroundings. In this experiment, a speed regulator that limits speed so as not to exceed more than 45km/h was used. In addition, the participant were required to take braking action when he/she judges that reducing velocity should be prioritized than overtaking in a given situations.

Figs. 8 and 9 are typical examples of the results for lateral displacement, system states [1: initiating the auditory alert and visual information, 2: implementing the shared steering control], torque [Nm] and steering angle [degrees]. In the lateral position, the dashed line shows the expected lateral gap based on the potential risk prediction. In the steering torque, the solid line shows the estimated driver’s torque, and the dashed line shows the assisting torque. The zero time instant indicates the time instant that the vehicle passed the cyclist, and blue color circle indicates the cyclist’s lateral position. As 93

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2016 IFAC/IFIP/IFORS/IEA HMS 88 Aug. 30 - Sept. 2, 2016. Kyoto, Japan

Fig. 14. Acceptance

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conditions (p < .05). In Fig. 11, the positive value denotes the participant ensured the lateral distance more than the expected lateral gap. The mean value of the lateral distance was 0.10 (s.d.: 0.52) with assist. One-way repeated measures ANOVA with the experimental condition on the predicted value of the time to reach the cyclist in when the driver implemented the steering action (Fig. 12) showed that the main effect of the experimental condition was statistically significant (F (2, 297) = 16.44, p < .05). Tukey’s HSD test found statistically significant differences between all conditions (p < .05). We performed the chi-square test with the experimental condition on the number of times that the vehicle entered into the potential risk area (Fig. 13), and the significant difference was found (X-squared = 28.221, p < .05). According to Holm multiple comparison test, significant differences were found between the w/o assist (1st trial) and with assist conditions and between the w/o assist (1st trial) and the w/o assist (2nd trial) conditions (p < .05). This means that the assistance system encouraged elderly drivers to pay attention to the cyclist and the shared steering control of the extension type can be

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can be noticed from Figs. 8 and Time 9, [sec]we can confirm that the participant implemented steering action early as compared with w/o assist condition and could ensure the sufficient lateral distance so that the assistance system leads the vehicle to the expected lateral gap. On the other hand, Fig. 10 shows a typical example that the participant overrode the assistance by adding more force into the steering wheel. From this result, we confirmed that the elderly driver can override the assistance when he/she is giving priority to himself/herself judgment although the vehicle enters into the potential risk area. One-way repeated measures ANOVA with the experimental condition on the maximum lateral distance (Fig. 11) showed that the main effect of the experimental condition was statistically significant (F (2, 433) = 82.01, p < .05). Tukey’s HSD test found statistically significant differences between all 94

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effective for guiding elderly drivers to ensure a sufficient lateral distance. However, there is a trade-off relation between the control performance and the override characteristics under the shared control manner. In this study, we set at low gain (Ka =4) to the steering torque gain in order to prioritize the driver’s input torque or workload in consideration of limitations of the machine’s information acquisition capability. Therefore, the driver can always override the assistance. In Fig 13, among the 150 times that the participant passed the cyclist, 66 instances (44.0 %) occurred that the vehicle entered into the potential risk area the with assist condition. Fig 15 shows the collision velocity with the virtual cyclist, and Y-axis is the lateral distance between the desired and the actual lateral positions. As can be seen in Fig. 15, the collision velocity occurred in the assist condition because 66 instances were cases that the host vehicle entered into the potential risk area. However, the collision velocity occurred cases were reduced as compared with the w/o assist condition. This means that the safety was improved as it can potentially prevent the vehicle-cyclist collision. Further studies are necessary to investigate the appropriate system’s workload under the shared steering control manner in the hidden risk situation.

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assistance system was useful for ensuring or extend the lateral distance between the host vehicle and the cyclist. ACKNOWLEDGEMENTS This study has been conducted as a part of the research project “Autonomous Driving Intelligence System to Enhance Safe and Secured Traffic Society for Elderly Drivers” granted by Japan Science and Technology Agency. The authors would like to thank the agency for providing financial support. REFERENCES Abbink, D. A., Mulder, M. and Boer, E. R. (2012). Haptic Shared Control: Smoothly Shifting Control Authority? Cognition, Technology, & Work, 14 (1), pp. 19-28. Bengler, K., Dietmayer, K., Farber, B., Maurer, M., Stiller, C. and Winner, H. (2014). Three Decades of Driver Assistance Systems: Review and Future Perspectives. IEEE Intelligent Transportation Systems Magazine, 6 (4), pp. 6-22. Caird, J. K., CHUGH, J. S., Wilcox, S. and Dewar, R. E. A Design Guidelines and Evaluation Framework to Determine the Relative Safety of In-vehicle Intelligent Transportation Systems for Older Drivers. TP 13349E. Transport Canada, Transportation Development Centre TDC, Montreal. Davidse, R. J. (2006). Older Drivers and Adas: Which Systems Improve Road Safety? IATSS Research, 30 (1), pp. 6-20. Davidse, R. J., Hagenzieker, M. P., van Wolffelaar, P. C. and Brouwer, W. H. (2009). Effects of In-car Support on Mental Workload and Driving Performance of Older Drivers. Hum Factors, 51 (4), pp. 463-476. Entenmann, V and Kuting, H. J. (2000). Safety Deficiencies of Elderly Drivers and Options Provide by Additional Digital Map Content. Proc. of the 7th World Congress on Intelligent Systems, Italy, 6-9 November. Gardner-Bonneau, D. and Gosbee, J. (1997). Health care and rehabilitation. In: Fisk, A.D. & Rogers, W.A. (eds.), Handbook of human factors and the older adult. Academic Press, San Diego, pp. 231-255. Horswill, Mark S., Marrington, Shelby A., McCullough, Cynthia M., Joanne Wood, Pachana, Nancy A. Jenna McWilliam and Raikos, Maria K. (2008). The Hazard Perception Ability of Older Drivers. The Journals of Gerontology, 63(4), pp. 212-218. Inagaki, T. (2006). Design of Human-machine Interactions in Light of Domain-dependence of Human-centered Automation, Cognition, Technology & Work, 8 (3), pp. 161-167. Inagaki, T. (2008). Smart Collaborations between Humans and Machines with Mutual Understanding, Annual Reviews in Control, 32, pp. 253-261. Inagaki, T. (2003). Adaptive Automation: Sharing and Trading of Control. In: Hollnagel, E. (ed.), Chapter 8 of the Handbook of Cognitive Task Design. LEA, pp. 147-169. Parasuraman, R., Sheridan, T. and Wickens, C. (2000). A Model for Types and Levels of Human Interaction with Automation. IEEE Transactions on Systems, Man, and Cybernetics. 30, pp. 286-297. Raksincharoensak, P. and et al., “Vehicle Motion Planning and Control for Autonomous Driving Intelligence System Based on Risk Potential Optimization Framework,” Proc. 24th Int. Symp. on Dynamics of Vehicles on Roads and Tracks, Austria, 2015, 10 pages. Schall, M.C., Rusch, M. L., Lee, J.D., Dawson, J. D., Thomas, G., Aksan, N. and Rizzo, M. (2013). Augmented reality cues and elderly driver hazard perception. Hum Factors, 55 (3), pp. 643-658. Satomi, Y., Murano, T., Aga, M. and Yonekawa, T. (2009). A Characteristic Analysis of Driving Behavior to Rear-end Collision Warning Using a Driving Simulator. Proc. The TRANSLOG2009, pp. 283-286 (in Japanese) Tsuyuki, H., Hayashi, R. and Nagai, M. (2013). Hazard-Anticipative Driving Mechanism at Overtaking Pedestrians in Narrow Roads. SAE Technical Paper. No.2013-01-0091, Pages 7. Van Der Laan, J. D., Adriaan Heino, Dick De Waard. (1997). A Simple Procedure for the Assessment of Acceptance of Advanced Transport Telematics. Transportation Research Part C: Emerging Technologies. 5 (1), pp. 1-10.

On the other hand, the participant’s pre and post scores for both the usefulness and the satisfaction dimensions (ranging from -2 to 2) are shown in Fig. 14. We performed a t-test with pre and post conditions on both the usefulness and the satisfaction scores. No significant differences were found in the mean value of each score. The mean value of the usefulness score (post) was 1.51 (s.d.: 0.57) and the mean value of the satisfaction score (post) was 1.42 (s.d.: 0.64). The proposed assistance system performed the risk predictive steering control in a situation-adaptive and a shared control manners before a cyclist suddenly intends to cross a narrow road as well as providing an arousing attention to potential hazard. In general, the acceptance of an action type support tends to be low as compared with a warning type support. However, the acceptance scores appears to be high as compared with the past studies (Van Der Laan et al., 1997). It appears that the participant positively evaluated the usefulness and the satisfaction of the assistance system because the assistance system was activated in the human-initiated strategy and he/she could always override the steering control in the extension-typed shared control manner. The driver was basically in command in the proposed human-machine system, because whether the cyclist intends to cross the road was uncertain (hidden risk situation). We conclude that both the assistance performance based on the shared steering control manner and the elderly driver’s acceptance can be compatible in the hidden risk situation. 5. CONCLUSIONS Under the driving simulator experiment, we investigated elderly driver’s acceptance and the system’s effectiveness of implementing the shared steering control in the hidden risk situation. The assistance system performs the action type support in a situation-adaptive manner before a moving cyclist suddenly intends to cross a narrow road. As for the research questions, elderly drivers positively evaluated the usefulness and satisfaction for the proposed assistance system, and the

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