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Lane-Change Trajectory Planning and Optimization for Intelligent Vehicles Based on NSGA-II
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Yunfei ZHA1, Kun ZHANG1, Lei SHEN1, Huiqin CHEN2
Chinese Journal of Automotive Engineering | 2024, 14(6) : 970 - 980
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Chinese Journal of Automotive Engineering | 2024, 14(6): 970-980
Inteligent & Connected Technologies Section/Editor in Chief: GAO Zhenhai
Lane-Change Trajectory Planning and Optimization for Intelligent Vehicles Based on NSGA-II
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Yunfei ZHA1, Kun ZHANG1, Lei SHEN1, Huiqin CHEN2
Affiliations
  • 1 Fujian Key Laboratory of Automotive Electronics and Electric Driving Technology Fujian University of Technology Fuzhou 350118 China
  • 2 School of Mechanical Engineering Hangzhou Dianzi University Hangzhou 310018 China
doi: 10.3969/j.issn.2095–1469.2024.06.05
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To address the challenge of determining control point locations in the cubic Bspline curve algorithm for intelligent vehicle lanechange trajectory planning, an optimization method based on NSGAII was proposed. Lanechanging trajectories for intelligent vehicles were planned using cubic Bspline curves. Under low, medium, and highspeed conditions, the NSGAII multiobjective optimization algorithm was applied to optimize the control point positions of these trajectories. The optimization focused on two key objectives, i.e. minimizing the length of lanechanging trajectories and reducing the average curvature of the trajectories. To verify the feasibility of the optimized trajectory, both simulations and realvehicle tests were conducted. The results show that the mean curvature and trajectory length are reduced after optimization under three different speed conditions. Specifically, the longitudinal displacement and mean curvature are reduced by 12.5% and 12%, 12.5% and 40%, 8.3% and 15.4% for low, medium and high speeds, respectively. In the cosimulation scenario, the optimized trajectory tracking shows a maximum lateral error of less than 0.1 m under low and medium speeds of 10 m/s and 20 m/s, respectively. At high speed of 30 m/s, the maximum lateral error remains below 0.3 m. In the real vehicle tests, the maximum lateral error before optimization is approximately 0.5 m. After optimization, this error is reduced to under 0.4 m, reflecting an improvement of over 20%.

intelligent vehicles  /  lane-change trajectory planning  /  NSGA-II  /  cubic B-spline
Yunfei ZHA, Kun ZHANG, Lei SHEN, Huiqin CHEN. Lane-Change Trajectory Planning and Optimization for Intelligent Vehicles Based on NSGA-II[J]. Chinese Journal of Automotive Engineering, 2024 , 14 (6) : 970 -980 . DOI: 10.3969/j.issn.2095–1469.2024.06.05
Year 2024 volume 14 Issue 6
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doi: 10.3969/j.issn.2095–1469.2024.06.05
  • Receive Date:2023-10-10
  • Online Date:2025-07-20
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  • Received:2023-10-10
  • Revised:2023-11-23
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    1 Fujian Key Laboratory of Automotive Electronics and Electric Driving Technology Fujian University of Technology Fuzhou 350118 China
    2 School of Mechanical Engineering Hangzhou Dianzi University Hangzhou 310018 China
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表12种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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