Home Journals Articles Updates About
CN
image
收藏切换
A Local Path Planning Method Combining Driver Risk and Vehicle Instability Risk
收藏切换
PDF
Haozhe Qian1, 2, Wen Sun1, 2, 3, Jingbo Zhao1, 2, Jianfeng Zheng1, Junnian Wang3
Automobile Technology | 2023, (11) : 8 - 18
Less
收藏切换
Automobile Technology | 2023, (11): 8-18
A Local Path Planning Method Combining Driver Risk and Vehicle Instability Risk
Full
Haozhe Qian1, 2, Wen Sun1, 2, 3, Jingbo Zhao1, 2, Jianfeng Zheng1, Junnian Wang3
Affiliations
  • 1 Changzhou University, Changzhou 213164
  • 2 Changzhou Institute of Technology, Changzhou 213001
  • 3 National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025
Published: 2023-11-24 doi: 10.19620/j.cnki.1000-3703.20221222
Outline
收藏切换

For the problem that intelligent vehicles lack the driver’s subjective driving operation cognition and the risk cognition of traffic participants, this paper proposed a trajectory planning algorithm based on improved Driver Risk Field (DRF). Firstly, the coordinate transformation from Cartesian coordinate system to Frenet coordinate system was carried out for the position of the vehicle, which visually represented the position information between the vehicle and the road; Secondly, a driver risk field model integrating vehicle stability risk was constructed to perceive the risk of traffic participants from the driver’s perspective; Finally, considering the comfort needs of drivers and passengers, obstacle avoidance operations were performed on the perceived risk potential energy high points based on quintic and quartic polynomial trajectories. The simulation and hardware-in-the-loop test results show that the planned trajectory meets both the obstacle avoidance function and the acceleration constraints, ensuring the safety and comfort of driving.

Frenet coordinate system  /  Driver Risk Field (DRF)  /  Risk of instability  /  Comfort
Haozhe Qian, Wen Sun, Jingbo Zhao, Jianfeng Zheng, Junnian Wang. A Local Path Planning Method Combining Driver Risk and Vehicle Instability Risk[J]. Automobile Technology, 2023 , (11) : 8 -18 . DOI: 10.19620/j.cnki.1000-3703.20221222
Appendix
Less
Year 2023 volume Issue 11
PDF
252
103
Cite this Article
BibTeX
Article Info
doi: 10.19620/j.cnki.1000-3703.20221222
  • Online Date：2025-12-07
  • Published：2023-11-24
Article Data
Affiliations
History
  • Revised：2023-05-29
Funding
Affiliations
    1 Changzhou University, Changzhou 213164
    2 Changzhou Institute of Technology, Changzhou 213001
    3 National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025
References
Share
https://castjournals.cast.org.cn/joweb/qcjs/EN/10.19620/j.cnki.1000-3703.20221222
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House