Driver Response Mechanism in Connected Environment—A Case Study of Qualitative Effect Analysis and Quantitative Driving Pattern Extraction of Intersection-approaching Behavior

doi:10.3901/JME.2024.10.022

Abstract

Abstract: The development of intelligent connected technology has provided great opportunities for the improvement of traffic safety and efficiency. However, the existing research fails to elaborate the driver’s cognitive response mechanism to the environment in the connected environment, and lacks the quantitative analysis of driving patterns in the connected environment. A method for studying the intersection-approaching behavior process and response mechanism is proposed, and the traffic behavior mechanism of drivers in the connected environment is explored. Two driving scenarios are designed in the driving simulator, namely the benchmark traditional environment and the controlled connected environment. In the connected environment, the driver is provided with the traffic light phase and the remaining time of the current phase state. Parameters such as visual interaction information, vehicle kinematics, and driver operating behavior characteristics of 34 drivers are collected. The interaction frequency and cumulative time percentage of the human-machine interface, the first interaction time and response time, and the behavioral characteristics of drivers approaching intersections are analyzed. A driving pattern extraction model based on bayesian non-parametric method combined with text clustering algorithm is established to achieve quantitative description of driving patterns. The results show that there are significant differences in the human-machine interaction characteristics under red and green light phases, and the first interaction time and response time are highly correlated. The connected environment can significantly improve the efficiency of intersection traffic and improve driving behavior. The proposed driving model can effectively describe the six driving patterns of intersection-approaching behaviors, and the connected environment can reduce the acceleration behavior by 23.7%, and increase the smooth driving ratio by 25.0%.

Key words: automotive engineering, driving behavior, human-machine interaction, driving pattern, intelligent connected vehicle

CLC Number:

B842
U461

ZHANG Hailun, XU Qing, GAO Bolin, WANG Jianqiang, LI Keqiang. Driver Response Mechanism in Connected Environment—A Case Study of Qualitative Effect Analysis and Quantitative Driving Pattern Extraction of Intersection-approaching Behavior[J]. Journal of Mechanical Engineering, 2024, 60(10): 22-47.

References

[1] YANG B，ZHENG R，SHIMONO K，et al. Evaluation of the effects of in-vehicle traffic lights on driving performances for unsignalised intersections[J]. IET Intelligent Transport Systems，2017，11(2):76-83.
[2] LIU Z，KIRCHER K. Comparison of a time-and a speed-based traffic light assistance system[J]. Cognition，Technology & Work，2018，20(1):93-103.
[3] ALI Y，SHARMA A，HAQUE M M，et al. The impact of the connected environment on driving behavior and safety:A driving simulator study[J]. Accident Analysis & Prevention，2020，144:105643.
[4] MONSAINGEON N，CAROUX L，MOUGINÉ A，et al. Impact of interface design on drivers' behavior in partially automated cars:An on-road study[J]. Transportation Research Part F:Traffic Psychology and Behaviour，2021，81:508-521.
[5] MAAG C，KRAFT A K，NEUKUM A，et al. Supporting cooperative driving behaviour by technology-HMI solution，acceptance by drivers and effects on workload and driving behaviour[J]. Transportation Research Part F:Traffic Psychology and Behaviour，2022，84:139-154.
[6] CALVERT S C，KLUNDER G，STEENDIJK J L L，et al. The impact and potential of cooperative and automated driving for intelligent traffic signal corridors:A field-operational-test and simulation experiment[J]. Case Studies on Transport Policy，2020，8(3):901-919.
[7] CALVERT S C，VAN AREM B. Cooperative adaptive cruise control and intelligent traffic signal interaction:A field operational test with platooning on a suburban arterial in real traffic[J]. IET Intelligent Transport Systems，2020，14(12):1665-1672.
[8] QIAO F，JIA J，YU L，et al. Drivers' smart assistance system based on radio frequency identification:Enhanced safety and reduced emissions in work zones[J]. Transportation Research Record，2014，2458(1):37-46.
[9] YAN X，ZHANG Y，MA L. The influence of in-vehicle speech warning timing on drivers' collision avoidance performance at signalized intersections[J]. Transportation Research Part C:Emerging Technologies，2015，51:231-242.
[10] KENNEY J B. Dedicated short-range communications (DSRC) standards in the United States[J]. Proceedings of the IEEE，2011，99(7):1162-1182.
[11] LIU J，ZHAO W，XU C. An efficient on-ramp merging strategy for connected and automated vehicles in multi-lane traffic[J]. IEEE Transactions on Intelligent Transportation Systems，2021，23(6):5056-5067.
[12] LI X，VAEZIPOUR A，RAKOTONIRAINY A，et al. Exploring drivers' mental workload and visual demand while using an in-vehicle HMI for eco-safe driving[J]. Accident Analysis & Prevention，2020，146:105756.
[13] KRAFT A K，MAAG C，BAUMANN M. Comparing dynamic and static illustration of an HMI for cooperative driving[J]. Accident Analysis & Prevention，2020，144:105682.
[14] KOHL J，GROSS A，HENNING M，et al. Driver glance behavior towards displayed images on in-vehicle information systems under real driving conditions[J]. Transportation Research Part F:Traffic Psychology and Behaviour，2020，70:163-174.
[15] YAN W，WONG S C，LOO B P Y，et al. An assessment of the effect of green signal countdown timers on drivers' behavior and on road safety at intersections，based on driving simulator experiments and naturalistic observation studies[J]. Journal of Safety Research，2022，82:1-12.
[16] PAUL M，GHOSH I，HAQUE M M. The effects of green signal countdown timer and retiming of signal intervals on dilemma zone related crash risk at signalized intersections under heterogeneous traffic conditions[J]. Safety Science，2022，154:105862.
[17] ISLAM M R，HURWITZ D S，MACUGA K L. Improved driver responses at intersections with red signal countdown timers[J]. Transportation Research Part C:Emerging Technologies，2016，63:207-221.
[18] LONG K，LIU Y，HAN L D. Impact of countdown timer on driving maneuvers after the yellow onset at signalized intersections:An empirical study in Changsha，China[J]. Safety Science，2013，54:8-16.
[19] MA W，LIU Y，YANG X. Investigating the impacts of green signal countdown devices:Empirical approach and case study in China[J]. Journal of Transportation Engineering，2010，136(11):1049-1055.
[20] 李文博，刘羽婧，张峻铖，等. 驾驶员情绪-驾驶风险机理分析[J]. 机械工程学报，2022，58(22):379-394. LI Wenbo，LIU Yujing，ZHANG Juncheng，et al. Analysis of the influence mechanism of driver's emotion on driving risk[J]. Journal of Mechanical Engineering，2022，58(22):379-394.
[21] HUANG Z, WU J, LÜ C. Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2021，23(8):10239-10251.
[22] XU C，ZHAO W，WANG C，et al. Driving behavior modeling and characteristic learning for human-like decision-making in highway[J]. IEEE Transactions on Intelligent Vehicles，2022，8(2):1994-2005.
[23] GUO Y，ZHANG H，WANG C，et al. Driver lane change intention recognition in the connected environment[J]. Physica A:Statistical Mechanics and its Applications，2021，575:126057.
[24] VAEZIPOUR A，RAKOTONIRAINY A，HAWORTH N，et al. Enhancing eco-safe driving behaviour through the use of in-vehicle human-machine interface:A qualitative study[J]. Transportation Research Part A:Policy and Practice，2017，100:247-263.
[25] STRAYER D L，COOPER J M，GOETHE R M，et al. Assessing the visual and cognitive demands of in-vehicle information systems[J]. Cognitive Research:Principles and Implications，2019，4:1-22.
[26] KIRCHER K，FORS C，AHLSTROM C. Continuous versus intermittent presentation of visual eco-driving advice[J]. Transportation Research Part F:Traffic Psychology and Behaviour，2014，24:27-38.
[27] KIRCHER K，FORS C，AHLSTROM C. Continuous versus intermittent presentation of visual eco-driving advice[J]. Transportation Research Part F:Traffic Psychology and Behaviour，2014，24:27-38.
[28] CHEN T，SZE N N，NEWNAM S，et al. Effectiveness of the compensatory strategy adopted by older drivers:Difference between professional and non-professional drivers[J]. Transportation Research Part F:Traffic Psychology and Behaviour，2021，77:168-180.
[29] YU B，BAO S，FENG F，et al. Examination and prediction of drivers' reaction when provided with V2I communication-based intersection maneuver strategies[J]. Transportation Research Part C:Emerging Technologies，2019，106:17-28.
[30] 国家住房和城乡建设部. 城市道路交通标志和标线设置规范:GB 51038—2015[S]. 北京:中国规划出版社，2015. Ministry of Housing and Urban-Rural Development of China. Specification for layout of highway traffic signs and markings:GB 51038—2015[S]. Beijing:China Planning Press，2015.
[31] 国家交通运输部. 公路交通标志和标线设置规范:JTG D82—2009[S]. 北京:人民交通出版社，2009. Ministry of Transport of China. Specification for layout of urban road traffic signs and markings:JTG D82—2009[S]. Beijing:People's Communications Press，2009.
[32] IWATSUKI A，HIRAYAMA T，MORITA J，et al. Skilled gaze behavior extraction based on dependency analysis of gaze patterns on video scenes[C]//Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications. 2016:299-302.
[33] SATO R，KAMEZAKI M，SUGANO S，et al. Gaze pattern analysis in multi-display systems for teleoperated disaster response robots[C]//2016 IEEE International Conference on Systems，Man，and Cybernetics (SMC). IEEE，2016:3534-3539.
[34] SHARMA A，ZHENG Z，KIM J，et al. Estimating and comparing response times in traditional and connected environments[J]. Transportation Research Record，2019，2673(4):674-684.
[35] SULLIVAN J M，TSIMHONI O，BOGARD S. Warning reliability and driver performance in naturalistic driving[J]. Human Factors，2008，50(5):845-852.
[36] ZHANG Y，WU C，QIAO C，et al. The effects of warning characteristics on driver behavior in connected vehicles systems with missed warnings[J]. Accident Analysis & Prevention，2019，124:138-145.
[37] ZHENG Z，WASHINGTON S. On selecting an optimal wavelet for detecting singularities in traffic and vehicular data[J]. Transportation Research Part C:Emerging Technologies，2012，25:18-33.
[38] ZHENG Z，AHN S，CHEN D，et al. Applications of wavelet transform for analysis of freeway traffic:Bottlenecks，transient traffic，and traffic oscillations[J]. Transportation Research Part B:Methodological，2011，45(2):372-384.
[39] CAIRD J K，CHISHOLM S L，EDWARDS C J，et al. The effect of yellow light onset time on older and younger drivers' perception response time (PRT) and intersection behavior[J]. Transportation Research Part F:Traffic Psychology and Behaviour，2007，10(5):383-396.
[40] EVANS L. Traffic safety and the driver[M]. New York:Science Serving Society，1991.
[41] LI Y C，SZE N N，WONG S C，et al. A simulation study of the effects of alcohol on driving performance in a Chinese population[J]. Accident Analysis & Prevention，2016，95:334-342.
[42] LI G，CHEN Y，CAO D，et al. Extraction of descriptive driving patterns from driving data using unsupervised algorithms[J]. Mechanical Systems and Signal Processing，2021，156:107589.
[43] CHEN Y，LI G，LI S，et al. Exploring behavioral patterns of lane change maneuvers for human-like autonomous driving[J]. IEEE Transactions on Intelligent Transportation Systems，2021，23(9):14322-14335.
[44] CHEN Y，LI G，LI S，et al. Exploring behavioral patterns of lane change maneuvers for human-like autonomous driving[J]. IEEE Transactions on Intelligent Transportation Systems，2021，23(9):14322-14335.
[45] FOX E B，SUDDERTH E B，JORDAN M I，et al. A sticky HDP-HMM with application to speaker diarization[J]. The Annals of Applied Statistics，2011:1020-1056.
[46] CHATZISAVVAS K C，MOUSTAKIDIS C C，PANOS C P. Information entropy，information distances，and complexity in atoms[J]. The Journal of Chemical Physics，2005，123(17):174111.
[47] GULER S I，MENENDEZ M，MEIER L. Using connected vehicle technology to improve the efficiency of intersections[J]. Transportation Research Part C:Emerging Technologies，2014，46:121-131.