A Graph-Based Method for Tactical Planning of Lane-Level Driving Tasks in the Outlook Region
Source abstract
Abstract
Road traffic regulations usually require that a vehicle can only move one lane during one lane change and must turn on the turn signal before changing lanes. Under such constraints, if automated vehicles can plan multiple lane-change maneuvers at one time, then not only adjacent lanes but also farther lanes can be selected as target lanes when making decisions. This would help improve the driving performance in multi-lane scenarios. Many current lane-selection or lane-change methods focus on the surrounding region of the ego vehicle, usually only considering adjacent lanes as potential target lanes. This paper proposes a new tactical functional model that attempts to perform lane-level driving task planning and decision-making over a road area far beyond the surrounding region of the ego vehicle. We refer to this road area as the “outlook region”. In this functional model, the decision-making of lane-level driving tasks will take the overall performance within the outlook region as the goal, rather than pursuing the optimal single lane-change maneuver. The proposed method is implemented using a directed graph-based approach and simulation tests are conducted. The results show that the proposed method helps improve the driving performance of automated vehicles in multi-lane scenarios.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Autonomous Vehicle Technology and Safety · Human-Automation Interaction and Safety · Traffic control and management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China
References (40)
Show source-provided references
1. Highly automated driving on freeways in real traffic using a probabilistic framework10.1109/TITS.2012.2196273 ↗
2. Towards connected autonomous driving: Review of use-cases10.1080/00423114.2018.1492142 ↗
3. A model of lane-changing intention induced by deceleration frequency in an automatic driving environment10.1016/j.physa.2022.127905 ↗
4. Tian, X., Gu, J., Li, B., Liu, Y., Wang, Y., Zhao, Z., Zhan, K., Jia, P., Lang, X., and Zhao, H. (2024). DriveVLM: The convergence of autonomous driving and large vision-language models. arXiv.
5. Michon, J.A. (1979). Dealing with Danger, Verkeerskundig Studiecentrum, Rijksuniversiteit Groningen.
6. Michon, J.A. (1985). A critical view of driver behavior models: What do we know, what should we do?. Human Behavior and Traffic Safety, Springer.10.1007/978-1-4613-2173-6_19 ↗
7. SAE On-Road Automated Vehicle Standards Committee (2021). Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE Stand. J., 3016, 1.
8. Analysis of distribution, frequency, and duration of naturalistic lane changes10.1177/154193120204602203 ↗
9. Modeling duration of lane changes10.3141/1999-08 ↗
10. Cao, X., Young, W., and Sarvi, M. (2013, January 2–4). Exploring duration of lane change execution. Proceedings of the Australasian Transport Research Forum, Brisbane, Australia.
11. Examining lane change gap acceptance, duration and impact using naturalistic driving data10.1016/j.trc.2019.05.024 ↗
12. Analysing and modelling of discretionary lane change duration considering driver heterogeneity
13. Real-time data sharing, path planning and route optimization in urban traffic management10.1007/s11042-023-15148-9 ↗
14. Spatial-temporal attention graph convolution network on edge cloud for traffic flow prediction10.1109/TITS.2022.3185503 ↗
15. Real-time traffic state measurement using autonomous vehicles open data10.1109/OJITS.2023.3298893 ↗
16. Ma, W., and Qian, S. (2021). High-resolution traffic sensing with probe autonomous vehicles: A data-driven approach. Sensors, 21.10.3390/s21020464 ↗
17. A hybrid deep learning model for urban expressway lane-level mixed traffic flow prediction10.1016/j.engappai.2024.108242 ↗
18. A model for the structure of lane-changing decisions10.1016/0191-2615(86)90012-3 ↗
19. Aoki, S., Jan, L.E., Zhao, J., Bhat, A., Chang, C.F., and Rajkumar, R.R. (2021, January 11–17). Multicruise: Eco-lane selection strategy with eco-cruise control for connected and automated vehicles. Proceedings of the 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan.10.1109/IV48863.2021.9575585 ↗
20. Yang, Z.C. (2022). Research on Intelligent Vehicle Autonomous Lane Changing System Considering Traffic Situation. [Ph.D. Dissertation, Jilin University].
21. Zhao, W.G. (2022). Research on Human-like Autonomous Lane-Changing Algorithm on Highways. [Master’s Thesis, Jilin University].10.1007/978-981-19-3842-9_59 ↗
22. Humanlike decision and motion planning for expressway lane changing based on artificial potential field10.1109/ACCESS.2022.3141075 ↗
23. An integrated decision and motion planning framework for automated driving on highway10.1109/TVT.2023.3293833 ↗
24. A novel lane change decision-making model of autonomous vehicle based on support vector machine10.1109/ACCESS.2019.2900416 ↗
25. Moghadam, M., and Elkaim, G.H. (2019). A hierarchical architecture for sequential decision-making in autonomous driving using deep reinforcement learning. arXiv.
26. Mirchevska, B., Pek, C., and Werling, M. (2018, January 4–7). High-level decision making for safe and reasonable autonomous lane changing using reinforcement learning. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.10.1109/ITSC.2018.8569448 ↗
27. Wang, J., Zhang, Q., and Zhao, D. (2019, January 14–19). Lane change decision-making through deep reinforcement learning with rule-based constraints. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.10.1109/IJCNN.2019.8852110 ↗
28. Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness10.1016/j.trc.2021.103452 ↗
29. Enhanced intelligent driver model for two-dimensional motion planning in mixed traffic10.1016/j.trc.2020.102780 ↗
30. Efficient driving on multilane roads under a connected vehicle environment10.1109/TITS.2016.2519526 ↗
31. Kamal, M.A.S., Taguchi, S., and Yoshimura, T. (July, January 28). Efficient vehicle driving on multi-lane roads using model predictive control under a connected vehicle environment. Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, Republic of Korea.10.1109/IVS.2015.7225772 ↗
32. Decision-making model for the lane selection of automated vehicles in the connected vehicle environment
33. Wang, W., Xie, J., Hu, C.Y., Zou, H., Fan, J., Tong, W., and Wen, Y. (2023). DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving. arXiv.
34. Liu, C., Jiang, K., Xiao, Z.Y., Cao, Z., and Yang, D. (2017, January 16–19). Lane-Level Route Planning Based on a Multi-Layer Map Model. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.10.1109/ITSC.2017.8317681 ↗
35. A Flexible Multi-Layer Map Model Designed for Lane-Level Route Planning in Autonomous Vehicles10.1016/j.eng.2018.11.032 ↗
36. He, K., Ding, H.T., Xu, N., and Guo, K.H. (2023). Accelerated and Refined Lane-Level Route-Planning Method Based on a New Road Network Model for Autonomous Vehicle Navigation. World Electr. Veh. J., 14.10.3390/wevj14040098 ↗
37. Lane-Level Route Planning for Autonomous Vehicles10.1177/02783649231225474 ↗
38. A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach10.1109/TIE.2019.2898599 ↗
39. Reconstructing the Spatio-Temporal Traffic Dynamics from Stationary Detector Data
40. Modelling of Driver/Vehicle Directional Control System10.1080/00423119308969025 ↗