Optimal Lane Change Path Planning Based on the NSGA-II and TOPSIS Algorithms
Source abstract
Abstract
Among so many autonomous driving technologies, autonomous lane changing is an important application scenario, which has been gaining increasing amounts of attention from both industry and academic communities because it can effectively reduce traffic congestion and improve road safety. However, most of the existing researchers transform the multi-objective optimization problem of lane changing trajectory into a single objective problem, but how to determine the weight of the objective function is relatively fuzzy. Therefore, an optimization method based on the combination of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which provides a new idea for solving the multi-objective optimization problem of lane change trajectory algorithm, is proposed in this paper. Firstly, considering the constraints of lane changing and combining with the collision detection algorithm, the feasible lane changing trajectory cluster is obtained based on the quintic polynomial. In order to ensure the comfort, stability and high efficiency of the lane changing process, the NSGA-II Algorithm is used to optimize the longitudinal displacement and time of lane changing. The continuous ordered weighted averaging (COWA) operator is introduced to calculate the weights of three objective optimization functions. Finally, the TOPSIS Algorithm is applied to obtain the optimal lane change trajectory. The simulations are conducted, and the results demonstrate that the proposed method can generate a satisfactory trajectory for automatic lane changing actions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Autonomous Vehicle Technology and Safety · Traffic control and management · Vehicle Dynamics and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
References (39)
Show source-provided references
1. National Center for Statistics and Analysis (2022). Overview of the 2021 Crash Investigation Sampling System.
2. Dynamic Lane-Changing Trajectory Planning for Autonomous Vehicles Based on Discrete Global Trajectory10.1109/TITS.2021.3083541 ↗
3. Singh, S. (2018). Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey, Traffic Safety Facts Crash Stats. Report No. Dot Hs 812 506.
4. Safe and Optimal Lane-Change Path Planning for Automated Driving10.1177/0954407020913735 ↗
5. Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges10.1007/s10846-016-0442-0 ↗
6. Zhu, F., Xu, X., Ma, L., Guo, D., Cui, X., and Kong, Q. (November, January 19). Autonomous driving vehicle control auto-calibration system: An industry-level, data-driven and learning-based vehicle longitudinal dynamic calibrating algorithm. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.10.1109/IV47402.2020.9304778 ↗
7. Geyer, J., Kassahun, Y., Mahmudi, M., Ricou, X., Durgesh, R., Chung, A.S., Hauswald, L., Pham, V.H., Mühlegg, M., and Dorn, S. (2020). A2d2: Audi Autonomous Driving Dataset, Cornell University.
8. A Survey of Deep Learning Techniques for Autonomous Driving10.1002/rob.21918 ↗
9. Caesar, H., Bankiti, V., Lang, H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020, January 13–19). nuScenes: A multimodal dataset for autonomous driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.01164 ↗
10. Personalized Trajectory Planning and Control of Lane-Change Maneuvers for Autonomous Driving10.1109/TVT.2021.3076473 ↗
11. A Dynamic Trajectory Planning Method for Lane-Changing Maneuver of Connected and Automated Vehicles10.1177/0954407020982712 ↗
12. Investigation of a Longitudinal and Lateral Lane-Changing Motion Planning Model for Intelligent Vehicles in Dynamical Driving Environments10.1109/ACCESS.2019.2909273 ↗
13. Harding, J., Powell, G., Yoon, R., Fikentscher, J., Doyle, C., Sade, D., Lukuc, M., Simons, J., and Wang, J. (2014). Vehicle-To-Vehicle Communications: Readiness of v2v Technology for Application, Dot Hs 812 014.
14. Millimeter-Wave Wireless Communications for Iot-Cloud Supported Autonomous Vehicles: Overview, Design, and Challenges10.1109/MCOM.2017.1600422CM ↗
15. 5G Communication: An Overview of Vehicle-To-Everything, Drones, and Healthcare Use-Cases10.1109/ACCESS.2019.2905347 ↗
16. A Novel Lane Change Decision-Making Model of Autonomous Vehicle Based on Support Vector Machine10.1109/ACCESS.2019.2900416 ↗
17. An Intelligent Lane-Changing Behavior Prediction and Decision-Making Strategy for an Autonomous Vehicle10.1109/TIE.2021.3066943 ↗
18. Turkoglu, K., and Selvakumar, J. (2020). Application of Chance-Constrained and Sample-Based Path Search for High Level Behavioural Planning: A Case Study of Autonomous Highway Lane Change Scenario, American Institute of Aeronautics and Astronautics. Aiaa Scitech 2020 Forum.10.2514/6.2020-0615 ↗
19. Bae, I., Kim, J.H., Moon, J., and Kim, S. (2019, January 27–30). Lane change maneuver based on bezier curve providing comfort experience for autonomous vehicle users. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand.10.1109/ITSC.2019.8916845 ↗
20. Zeng, D., Yu, Z., Xiong, L., Zhao, J., Zhang, P., Li, Z., Fu, Z., Yao, J., and Zhou, Y. (2019, January 9–12). A Novel robust lane change trajectory planning method for autonomous vehicle. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.10.1109/IVS.2019.8814151 ↗
21. Li, Z., Liang, H., Zhao, P., Wang, S., and Zhu, H. (2020, January 13–16). Efficent lane change path planning based on quintic spline for autonomous vehicles. Proceedings of the 2020 IEEE International Conference on Mechatronics and Automation (ICMA), Beijing, China.10.1109/ICMA49215.2020.9233841 ↗
22. Path Planning and Re-Planning of Lane Change Manoeuvres in Dynamic Traffic Environments
23. Accelerated Lane-Changing Trajectory Planning of Automated Vehicles with Vehicle-To-Vehicle Collaboration10.1155/2017/8132769 ↗
24. XI, C., Shi, T., Wu, Y., and Sun, L. (2020, January 20–23). Efficient motion planning for automated lane change based on imitation learning and mixed-integer optimization. Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece.10.1109/ITSC45102.2020.9294679 ↗
25. A Dynamic Automated Lane Change Maneuver Based on Vehicle-To-Vehicle Communication10.1016/j.trc.2015.11.011 ↗
26. Manoeuvring Speed Estimation of a Lane-Change System Using Geometric Hermite Interpolation10.1016/j.asej.2021.02.027 ↗
27. Structured Trajectory Planning of Collision-Free Lane Change Using the Vehicle-Driver Integration Data10.1007/s11431-016-6041-2 ↗
28. Qiao, B., and Wu, X. (2019, January 8–12). Lane change control of autonomous vehicle with real-time rerouting function. Proceedings of the 2019 IEEE/Asme International Conference on Advanced Intelligent Mechatronics (AIM), Hong Kong, China.10.1109/AIM.2019.8868634 ↗
29. Li, Z., Jiang, J., and Chen, W.-H. (2020–21, January 24). Automatic lane change maneuver in dynamic environment using model predictive control method. Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.10.1109/IROS45743.2020.9341729 ↗
30. Sugie, N., Okuda, H., Suzuki, T., Haraguchi, K., and Kang, Z. (2018, January 4–7). Simultaneous realization of planning and control for lane-change behavior using nonlinear model predictive control. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.10.1109/ITSC.2018.8569973 ↗
31. Risk Assessment and Mitigation in Local Path Planning for Autonomous Vehicles With Lstm Based Predictive Model10.1109/TASE.2021.3075773 ↗
32. Multiobjective Optimization of Lane-Changing Strategy for Intelligent Vehicles in Complex Driving Environments10.1109/TVT.2019.2956504 ↗
33. A New Safe Lane-Change Trajectory Model and Collision Avoidance Control Method for Automatic Driving Vehicles10.1016/j.eswa.2019.112953 ↗
34. Collision Avoidance Analysis for Lane Changing and Merging10.1109/25.901899 ↗
35. Trajectory Planning and Tracking Control for Autonomous Lane Change Maneuver Based on the Cooperative Vehicle Infrastructure System10.1016/j.eswa.2015.03.022 ↗
36. A Fast and Elitist Multiobjective Genetic Algorithm: Nsga-Ii10.1109/4235.996017 ↗
37. Multiple Attribute Group Decision Making Based on Interval-Valued Hesitant Fuzzy Information Measures10.1016/j.cie.2016.08.019 ↗
38. Tang, H.-C., and Yang, S.-T. (2018). Optimizing Three-Dimensional Constrained Ordered Weighted Averaging Aggregation Problem with Bounded Variables. Mathematics, 6.10.3390/math6090172 ↗
39. A State-of The-Art Survey of Topsis Applications