An Enabling Trajectory Planning Scheme for Lane Change Collision Avoidance on Highways

被引:39
作者
Zhang, Zhiqiang [1 ,2 ]
Zhang, Lei [1 ,2 ]
Deng, Junjun [1 ,2 ]
Wang, Mingqiang [1 ,2 ]
Wang, Zhenpo [1 ,2 ]
Cao, Dongpu [3 ]
机构
[1] Beijing Inst Technol, Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China
[2] Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China
[3] Univ Waterloo, Mech & Mechatron Engn, Waterloo, ON N2L 3G1, Canada
来源
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES | 2023年 / 8卷 / 01期
关键词
Trajectory; Trajectory planning; Collision avoidance; Real-time systems; Safety; Roads; Kinematics; trajectory planning; speed re-planning; path re-planning; VEHICLES; FRAMEWORK; NETWORK; MODEL;
D O I
10.1109/TIV.2021.3117840
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a hierarchical three-layer trajectory planning framework to realize real-time collision avoidance under complex driving conditions. This is mainly ascribed to the generation of a collision-free trajectory cluster based on the speed and the path re-planning. The upper-layer controller is to generate a reference quintic polynomial trajectory based on the Sequential Quadratic Programming by assuming mild speed and acceleration variations of the surrounding vehicles. The waypoints and time stamps can be obtained via the reference trajectory. When the assumption is invalid under complex driving conditions, the middle-layer controller would generate a Quadratic Programming-based trajectory cluster to assign different time stamps to each waypoint through time-based sampling methods. The lower-layer controller would be triggered to create a new feasible trajectory based on the path sampling if the collision avoidance requirements are not satisfied. The host vehicle will return to its original lane if no feasible time window is available to perform a lane change maneuver under the vehicle kinematics and lane change time/displacement constraints. The effectiveness of the proposed scheme is verified under various scenarios through comprehensive simulations.
引用
收藏
页码:147 / 158
页数:12
相关论文
共 33 条
[1]   A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction [J].
Bahram, Mohammad ;
Hubmann, Constantin ;
Lawitzky, Andreas ;
Aeberhard, Michael ;
Wollherr, Dirk .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 17 (06) :1538-1550
[2]   Analysing driving efficiency of mandatory lane change decision for autonomous vehicles [J].
Cao, Peng ;
Xu, Zhandong ;
Fan, Qiaochu ;
Liu, Xiaobo .
IET INTELLIGENT TRANSPORT SYSTEMS, 2019, 13 (03) :506-514
[3]   A path and velocity planning method for lane changing collision avoidance of intelligent vehicle based on cubic 3-D Bezier curve [J].
Chen Long ;
Qin Dongfang ;
Xu Xing ;
Cai Yingfeng ;
Xie Ju .
ADVANCES IN ENGINEERING SOFTWARE, 2019, 132 :65-73
[4]   Accurate and Efficient Approximation of Clothoids Using Bezier Curves for Path Planning [J].
Chen, Yong ;
Cai, Yiyu ;
Zheng, Jianmin ;
Thalmann, Daniel .
IEEE TRANSACTIONS ON ROBOTICS, 2017, 33 (05) :1242-1247
[5]   Mutual Information-Based Multi-AUV Path Planning for Scalar Field Sampling Using Multidimensional RRT* [J].
Cui, Rongxin ;
Li, Yang ;
Yan, Weisheng .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2016, 46 (07) :993-1004
[6]   Collision Avoidance: A Literature Review on Threat-Assessment Techniques [J].
Dahl, John ;
de Campos, Gabriel Rodrigues ;
Olsson, Claes ;
Fredriksson, Jonas .
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2019, 4 (01) :101-113
[7]   Optimal Path Planning in Complex Cost Spaces With Sampling-Based Algorithms [J].
Devaurs, Didier ;
Simeon, Thierry ;
Cortes, Juan .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2016, 13 (02) :415-424
[8]   A Review of Motion Planning Techniques for Automated Vehicles [J].
Gonzalez, David ;
Perez, Joshue ;
Milanes, Vicente ;
Nashashibi, Fawzi .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 17 (04) :1135-1145
[9]   Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles [J].
Hu, Xuemin ;
Chen, Long ;
Tang, Bo ;
Cao, Dongpu ;
He, Haibo .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2018, 100 :482-500
[10]   A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach [J].
Huang, Yanjun ;
Ding, Haitao ;
Zhang, Yubiao ;
Wang, Hong ;
Cao, Dongpu ;
Xu, Nan ;
Hu, Chuan .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2020, 67 (02) :1376-1386