Skip to main content
SpringerLink
Log in

A probabilistic risk assessment framework considering lane-changing behavior interaction

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Understanding the dynamic characteristics of surrounding vehicles and estimating the potential risk of mixed traffic can help reliable autonomous driving. However, the existing risk assessment methods are challenging to detect dangerous situations in advance and tackle the uncertainty of mixed traffic. In this paper, we propose a probabilistic driving risk assessment framework based on intention identification and risk assessment of surrounding vehicles. Firstly, we set up an intention identification model (IIM) via long short-term memory (LSTM) networks to identify the intention possibility of the surrounding vehicles. Then a risk assessment model (RAM) based on the driving safety field is employed to output the potential risk. Specifically, driving safety field can reflect the coupling relationship of drivers, vehicles, and roads by analyzing their interaction. Finally, an integrated risk evaluation model combining both IIM and RAM is developed to form a dynamic potential risk map considering multi-vehicle interaction. For example, in a typical but challenging lane-changing scenario, an intelligent vehicle can assess its driving status by calculating a risk map in real time that represents the risk generated by the estimated intentions of surrounding vehicles. Furthermore, simulations and naturalistic driving experiments are conducted in the extracted lane-changing scenarios, and the results verify the effectiveness of the proposed model considering lane-changing behavior interaction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. González D, Pérez J, Lattarulo R, et al. Continuous curvature planning with obstacle avoidance capabilities in urban scenarios. In: Proceedings of 17th International IEEE Conference on Intelligent Transportation Systems (ITSC14), 2014. 1430–1435

  2. González D, Pérez J, Milanes V, et al. A review of motion planning techniques for automated vehicles. IEEE Trans Intell Transp Syst, 2016, 17: 1135–1145

    Article  Google Scholar 

  3. Tas O S, Kuhnt F, Zollner J M, et al. Functional system architectures towards fully automated driving. In: Proceedings of 2016 IEEE Intelligent Vehicles Symposium (IV), Gotenburg, 2017. 304–309

  4. Mayfield H J, Smith C S, Lowry J H, et al. Predictive risk mapping of an environmentally-driven infectious disease using spatial Bayesian networks: a case study of leptospirosis in Fiji. PLoS Negl Trop Dis, 2018, 12: e0006857

    Article  Google Scholar 

  5. Guo H, Shen C, Zhang H, et al. Simultaneous trajectory planning and tracking using an MPC method for cyber-physical systems: a case study of obstacle avoidance for an intelligent vehicle. IEEE Trans Ind Inf, 2018, 14: 4273–4283

    Article  Google Scholar 

  6. Wu C, Peng L, Huang Z, et al. A method of vehicle motion prediction and collision risk assessment with a simulated vehicular cyber physical system. Transpation Res Part C-Emerg Technol, 2014, 47: 179–191

    Article  Google Scholar 

  7. Kim J, Kum D. Collision risk assessment algorithm via lane-based probabilistic motion prediction of surrounding vehicles. IEEE Trans Intell Transp Syst, 2018, 19: 2965–2976

    Article  Google Scholar 

  8. Katrakazas C, Quddus M, Chen W H, et al. Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions. Transpation Res Part C-Emerg Technol, 2015, 60: 416–442

    Article  Google Scholar 

  9. Lee K Q, Peng H. Evaluation of automotive forward collision warning and collision avoidance algorithms. Vehicle Syst Dyn, 2005, 43: 735–751

    Article  Google Scholar 

  10. van Winsum W. The human element in car following models. Transpation Res Part F-Traffic Psychol Behaviour, 1999, 2: 207–211

    Article  Google Scholar 

  11. Li Y, Zheng Y, Wang J Q, et al. Crash probability estimation via quantifying driver hazard perception. Accident Anal Prevention, 2018, 116: 116–125

    Article  Google Scholar 

  12. Archibald J K, Hill J C, Jepsen N A, et al. A satisficing approach to aircraft conflict resolution. IEEE Trans Syst Man Cybern C, 2008, 38: 510–521

    Article  Google Scholar 

  13. Minderhoud M M, Bovy P H L. Extended time-to-collision measures for road traffic safety assessment. Accident Anal Prevention, 2001, 33: 89–97

    Article  Google Scholar 

  14. Allen C, Ewing M, Keshmiri S, et al. Multichannel sense-and-avoid radar for small UAVs. In: Proceedings of IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC), 2013

  15. Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. In: Proceedings of IEEE International Conference on Robotics and Automation, 1985. 500–505

  16. Reichardt D, Shick J. Collision avoidance in dynamic environments applied to autonomous vehicle guidance on the motorway. In: Proceedings of the Intelligent Vehicles’94 Symposium, Paris, 1994. 74–78

  17. Huang Y, Ding H, Zhang Y, et al. A motion planning and tracking framework for autonomous vehicles based on artificial potential field elaborated resistance network approach. IEEE Trans Ind Electron, 2020, 67: 1376–1386

    Article  Google Scholar 

  18. Hu X, Chen L, Tang B, et al. Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles. Mech Syst Signal Process, 2018, 100: 482–500

    Article  Google Scholar 

  19. Wang J Q, Wu J, Zheng X J, et al. Driving safety field theory modeling and its application in pre-collision warning system. Transpation Res Part C-Emerg Technol, 2016, 72: 306–324

    Article  Google Scholar 

  20. Aoude G S, Luders B D, Lee K H, et al. Threat assessment design for driver assistance system at intersections. In: Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, 2010. 1855–1862

  21. Goerlandt F, Reniers G. On the assessment of uncertainty in risk diagrams. Saf Sci, 2016, 84: 67–77

    Article  Google Scholar 

  22. Xie G T, Zhang X, Gao H B, et al. Situational assessments based on uncertainty-risk awareness in complex traffic scenarios. Sustainability, 2017, 9: 1582

    Article  Google Scholar 

  23. Belkhouche F. Modeling and calculating the collision risk for air vehicles. IEEE Trans Veh Technol, 2013, 62: 2031–2041

    Article  Google Scholar 

  24. Havlak F, Campbell M. Discrete and continuous, probabilistic anticipation for autonomous robots in urban environments. 2013. ArXiv: 1309.0766

  25. Deo N, Trivedi M M. Multi-modal trajectory prediction of surrounding vehicles with maneuver based LSTMs. In: Proceedings of IEEE Intelligent Vehicles Symposium (IV), 2018. 1179–1184

  26. Khosroshahi A, Ohn-Bar E, Trivedi M M. Surround vehicles trajectory analysis with recurrent neural networks. In: Proceedings of IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, 2016. 2267–2272

  27. Tijerina L, Garrott W R, Stoltzfus D, et al. Eye glance behavior of van and passenger car drivers during lane change decision phase. Trans Res Rec, 2005, 1937: 37–43

    Article  Google Scholar 

  28. Federal Highway Administration. Next Generation Simulation (NGSIM) Program. 2006. http://ngsimcommunity.org/

  29. Yoshitake H, Shino M. Risk assessment based on driving behavior for preventing collisions with pedestrians when making across-traffic turns at intersections. IATSS Res, 2018, 42: 240–247

    Article  Google Scholar 

  30. Zou Y, Qu X B. On the impact of connected automated vehicles in freeway work zones: a cooperative cellular automata model based approach. J Intell Connected Veh, 2018, 1: 1–14

    Article  Google Scholar 

  31. Zheng X J, Huang H Y, Wang J Q, et al. Behavioral decision-making model of the intelligent vehicle based on driving risk assessment. Comput-Aided Civil Infrastruct Eng, 2019, 16: 1–18

    Google Scholar 

  32. Zheng X J, Huang B, Ni D H, et al. A novel intelligent vehicle risk assessment method combined with multi-sensor fusion in dense traffic environment. J Intell Connected Veh, 2018, 1: 41–54

    Article  Google Scholar 

  33. Seddon N, Bearpark T. Observation of the inverse Doppler effect. Science, 2003, 302: 1537–1540

    Article  Google Scholar 

  34. Zhang W, Dai J, Pei Y, et al. Drivers’ visual search patterns during overtaking maneuvers on freeway. Int J Environ Res Public Health, 2016, 13: 1159

    Article  Google Scholar 

  35. Chen T, Wen H, Hu H, et al. On-orbit assembly of a team of flexible spacecraft using potential field based method. Acta Astronaut, 2017, 133: 221–232

    Article  Google Scholar 

  36. Guo H, Liu J, Dai Q, et al. A distributed adaptive triple-step nonlinear control for a connected automated vehicle platoon with dynamic uncertainty. IEEE Internet Things J, 2020, 7: 3861–3871

    Article  Google Scholar 

  37. Krajewski R, Bock J, Kloeker L, et al. The highD Dataset: a drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems. 2018. ArXiv: 1810.05642

Download references

Acknowledgements

This work was supported by the Major Project of National Natural Science Foundation of China (Grant No. 61790561), National Science Fund for Distinguished Young Scholars (Grant No. 51625503), Intel Collaborative Research Institute on Intelligent and Automated Connected Vehicles (ICRI-IACV), the Joint Laboratory for Internet of Vehicle, and Ministry of Education — China Mobile Communications Corporation. We would also like to express our great thanks to the Ph.D. candidates, Hui XIONG and Yang LI, who participated in the discussion and optimized the study.

Author information

Authors and Affiliations

  1. The State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China

    Heye Huang, Jianqiang Wang, Cong Fei, Xunjia Zheng, Yibin Yang, Jinxin Liu & Qing Xu

  2. Intel, Beijing, 100084, China

    Xiangbin Wu

Authors
  1. Heye Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianqiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cong Fei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xunjia Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yibin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jinxin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiangbin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Qing Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xunjia Zheng or Qing Xu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, H., Wang, J., Fei, C. et al. A probabilistic risk assessment framework considering lane-changing behavior interaction. Sci. China Inf. Sci. 63, 190203 (2020). https://doi.org/10.1007/s11432-019-2983-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-019-2983-0

Keywords

Search

Navigation