Research on the Vehicle-Behavior Boundary of Intersection Traffic Based on Naturalistic Driving Data Study
Source abstract
Abstract
With the development and application of vehicle-infrastructure cooperative technology, the traffic regional safety related to intelligent connected vehicles (ICVs) has become the hotspot of the intelligent transportation system (ITS), and the integration of mixed autonomous and non-autonomous vehicles that are not cooperative in intersection areas has become a significant challenge due to the rapid advancement of autonomous vehicle technology. Autonomous vehicles in intersections with strong-structure and weak-rule characteristics pose a potential hazard in complex traffic situations. Studying the driving behavior of vehicles in intersections is of great significance due to the complex traffic environment, frequent traffic signals, and traffic violations, which can optimize the vehicle driving behavior and improve the safety and efficiency of intersection traffic. By using naturalistic driving data from the DAIR V2X-Seq dataset and general vehicle dynamic parameters, it is possible to obtain the joint-probability-density distribution of the bivariate dynamic parameters of a vehicle. This distribution represents the driving characteristics of vehicles in intersection traffic. The three vehicle dynamic parameters that have an impact on vehicles driving through the intersection area are velocity, angular velocity, and acceleration. The driving behavior characteristics of human-driven vehicles (HVs) and autonomous vehicles (AVs) were analyzed using the multivariate kernel density estimation (MKDE) method to establish the vehicle-behavior boundary. The assessment of the boundary model showed that it accurately characterizes the driving characteristics of HVs and AVs. This boundary can be used to improve the safety detection of intersection areas, enhancing the performance of autonomous vehicles and optimizing intersection traffic.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic Prediction and Management Techniques · Autonomous Vehicle Technology and Safety · Traffic control and management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automotive Studies, Tongji University, Shanghai 201804, China
Shanghai Jinqiao Intelligent Connected Automobile Development Limited Company, Shanghai 201206, China
Funding
National Key Research and Development Plan of China
References (53)
Show source-provided references
1. and Yumaganov, A. (2020, January 26–29). 3D Objects Detection in an Autonomous Car Driving Problem. Proceedings of the 2020 International Conference on Information Technology and Nanotechnology (ITNT), Samara, Russia.10.1109/ITNT49337.2020.9253253 ↗
2. Zumd, J., Sener, I.N., and Wagner, J. (2016). Revolutionizing Our Roadways Consumer Acceptance and Travel Behavior Impacts of Automated Vehicles, Texas A&M Transportation Institute.
3. V2I based environment perception for autonomous vehicles at intersections10.23919/JCC.2021.07.001 ↗
4. Guneshka, S., Daniel, B., and Zöllner, J.M. (2022). Proceedings of the European Conference on Computer Vision, Springer Nature.
5. Bogdoll, D., Breitenstein, J., Heidecker, F., Bieshaar, M., Sick, B., Fingscheidt, T., and Zöllner, J.M. (2021, January 11–17). Description of corner cases in automated driving: Goals and challenges. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada.10.1109/ICCVW54120.2021.00119 ↗
6. Safety boundary condition model based on vehicle intersection conflict and collision
7. Federal Highway Administration (2020, August 18). About Intersection Safety, Available online: https://safety.fhwa.dot.gov/intersection/about/index.cfm.
8. United States Department of Transportation (2023, May 02). Intersection Safety, Available online: https://highways.dot.gov/research/research-programs/safety/intersection-safety.
9. National Highway Traffic Safety Administration (2016). National Automotive Sampling System (NASS) General Estimates System (GES) Analytical User’s Manual 1988–2015, National Highway Traffic Safety Administration (NHTSA).
10. China Automotive Technology and Research Center Co., Ltd. (CATARC) (2021). White Paper on Traffic Safety of Autonomous Vehicles, China Automotive Technology and Research Center Co., Ltd. (CATARC).
11. (2019, May 01). Insurance Institute for Highway Safety, Highway Loss Data, Fatality Facts 2018 Urban/rural Comparison. Available online: https://www.iihs.org/topics/fatality-statistics/detail/urban-rural-comparison.
12. (2019). 2019 Road Vehicles—Safety of the Intended Functionality (Standard No. ISO/PAS 21448).
13. ECE TRANS WP.29 GRVA (2019, September 01). Proposal for the Future Certification of Automated/Autonomous Driving Systems [EB/OL]. Available online: https://www.unece.org/trans/main/wp29/wp29wgs/wp29grva/grva2019.html.
14. Rinaldi, G., Menon, P.P., and Edwards, C. (2021, January 25–28). Suboptimal Sliding Mode-based Heading and Speed Guidance Scheme for Boundary Tracking with Autonomous Vehicle. Proceedings of the 2021 American Control Conference, Virtual.10.23919/ACC50511.2021.9483297 ↗
15. Mao, L., Li, W., Hu, P., Zhou, G., Zhang, H., and Dai, J. (2020). Design of Real-Time Dynamic Reversible Lane in Intelligent Cooperative Vehicle Infrastructure System. J. Adv. Transp., 2.10.1155/2020/8838896 ↗
16. Optimization of variable lane considering reduction of capacity
17. A Back-Pressure-Based Model with Fixed Phase Sequences for Traffic Signal Optimization Under Oversaturated Networks
18. Zhou, X. (2013). Design and Development of Vehicle Navigation System Based on Intersection Safety. [Master’s Thesis, Shandong University of Technology].
19. Van Schagen, I., Welsh, R., Backer-Grøndahl, A., Sagberg, F., Tsippy, L., Hoedemae, M., and Morris, A. (2011). Towards a Large-Scale European Naturalistic Driving study: Main Findings of PROLOGUE. PROLOGUE Deliverable D4.2, SWOV Institute for Road Safety Research.
20. Welsh, R., Reed, S., Talbot, R., and Morris, A. (2010). PROLOGUE Deliverable D2.1, Loughborough University.
21. Backer, G., Lotan, T., and Van, I. (2011). PROLOGUE Deliverable D3.7, TOI Institute of Transport Economics.
22. The Potential Benefits of Naturalistic Driving for Road Safety Research: Theoretical and Empirical Considerations and Challenges for the Future10.1016/j.sbspro.2012.06.1047 ↗
23. Liu, L., Zhu, X., Chen, M., and Ma, Z. (2018). WCX World Congress Experience, SAE International.
24. Najm, W.G., Smith, J.D., and Yanagisawa, M. (2007). Pre-Crash Scenario Typology for Crash Avoidance Research, United States National Highway Traffic Safety Administration.
25. Google (2023, December 01). Waymo [EB/OL]. Available online: www.waymo.com.
26. Carmen, R., Joris, D., Felix, F., and AdaptIVe subprojects (2017, May 01). AdaptIVe Final Report Deliverable D1.0. Available online: https://www.adaptive-ip.eu/files/adaptive/content/downloads/AdaptIVe-SP1-v1-0-DL-D1-0-Final_Report.pdf.
27. Jensen, M.B., Møgelmose, A., and Moeslund, T.B. (2020, January 20–23). Presenting the multi-view traffic intersection dataset (MTID): A detailed traffic-surveillance dataset. Proceedings of the IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece.10.1109/ITSC45102.2020.9294694 ↗
28. Schuldt, F., Reschka, A., and Maurer, M. (2008). Automotive Systems Engineering II, Springer.
29. AEB intersection test scenario based on accident data mining
30. A review of key technologies for constructing autonomous vehicle test scenarios
31. Research on intersection test scenarios of AEB system based on NAIS
32. Research on dangerous scenarios at T-intersections based on NAIS accident data clustering
33. Sun, X., Zhu, X., Zhang, K., Li, L., Ma, Z., and Wang, D. (2016, January 1–4). Automatic detection method research of incidents in China-FOT database. Proceedings of the 2016 IEEE 19th International Conference of the Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.
34. Yan, Y., Zhu, X., Liao, M., and Ma, Z. (2018, January 4–7). Danger Degree Evaluation Method of Crossroad Scenario Based on Naturalistic Driving Analysis. Proceedings of the 2018 IEEE Intelligent Transportation Systems Conference (ITSC), Maui, HI, USA.10.1109/ITSC.2018.8569328 ↗
35. Xuehan, M., Xichan, Z., and Zhixiong, M. (2017, January 1). Analysis of intersection traffic classification and violation behavior in natural driving research. Proceedings of the 14th International Forum of Automotive Traffic Safety, Geneva, Switzerland.
36. Dynamic dilemma zone based on driver behavior and car-following model at highway–rail intersections10.1016/S0191-2615(02)00014-0 ↗
37. Location-Based Analysis of Car-Following Behavior During Braking Using Naturalistic Driving Data10.1139/cjce-2019-0314 ↗
38. Human-like Speed Modelling for Autonomous Vehicles during Car-Following at Intersections10.1139/cjce-2020-0761 ↗
39. Identification of Visual Cues and Quantification of Drivers’ Perception of Proximity Risk to the Lead Vehicle in Car-Following Situations10.1299/jmtl.1.170 ↗
40. Gettman, D., and Head, L. (2003). Surrogate Safety Measures from Traffic Simulation Models, FHWA/RD-03-050; Federal Highway Administration, Department of Transportation, U.S.
41. Analysis of traffic conflicts and collisions
42. Safety evaluation for intergreen intervals at signalized intersections based on probabilistic methodology10.3141/2128-23 ↗
43. Safety evaluation of right-turn smart channels using automated traffic conflict analysis10.1016/j.aap.2011.11.015 ↗
44. Wu, B., Ma, Z., Wei, X., and Zhou, X. (2022). SAE Technical Paper, SAE International.
45. The Integration of Conflict Probability and Severity for the Safety Assessment of Intersections10.1007/s13369-014-1553-1 ↗
46. Vehicle-to-everything (V2X) services supported by LTE-based systems and 5G10.1109/MCOMSTD.2017.1700015 ↗
47. Yu, H., Luo, Y., Shu, M., Huo, Y., Yang, Z., Shi, Y., Guo, Z., Li, H., Hu, X., and Yuan, J. (2022, January 19–24). DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.10.1109/CVPR52688.2022.02067 ↗
48. Yu, H., Yang, W., Ruan, H., Yang, Z., Tang, Y., Gao, X., Hao, X., Shi, Y., Pan, Y., and Sun, N. (2023, January 17–24). V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.00531 ↗
49. A Naturalistic Study of Vehicle Acceleration and Deceleration at an Intersection10.4271/2022-01-0825 ↗
50. Driver Response to Right Turning Path Intrusions at Signalized Intersections10.4271/2020-01-0884 ↗
51. Ziraldo, E., Attalla, S., Kodsi, S., and Oliver, M. (2020, January 21–24). Driver Response to Left Incurring Path Intrusions at Sign-Controlled Intersections. Proceedings of the WCX SAE World Congress Experience, Detroit, MI, USA.10.4271/2020-01-0886 ↗
52. Personalized Driver/Vehicle Lane Change Models for ADAS10.1109/TVT.2014.2369522 ↗
53. Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer.