基于离线路径规划与iLQR控制的汽车自动紧急转向研究

doi:10.19562/j.chinasae.qcgc.2025.10.009

Abstract

Abstract:

In the study， an offline Bezier path is generated based on the shortest escaping time for AES （Autonomous Emergency Steering）. An iLQR（iterative Linear Quadratic Regulator） scheme is introduced for trajectory tracking and control. Simulation is applied to validate the proposed method. It is found that compared to quintic polynomial， “brachistochrone curve” is more desirable for the emergency scene like AES， which achieves the relatively fastest collision avoidance. Combined with offline planning mode， the computation cost is acceptable， and steering performance is completely exploited， with much less parameter calibration. For AES， the control error is characterized by abruptness， and a large control input is always requested. The nonlinearity of system is essential to be taken into consideration. Through an iterative procedure in predictive horizon， the optimal control input is obtained， which satisfies the request of fast response during path track of AES.

Key words: intelligent vehicle, autonomous emergency steering, offline path planning, iLQR （iterative Linear Quadratic Regulator）, predictive horizon

Ningning Tu,An Cao,Mu Yuan,Mingyang Hou,Shengping Liang. Research on Autonomous Emergency Steering of Vehicle Based on Offline Path Planning and iLQR Control[J].Automotive Engineering, 2025, 47(10): 1933-1941.

Figures/Tables 17

References 24

[1]	龙亚，王宝军，马林才，等. 车道保持辅助系统关键技术的国内外研究现状［J］. 现代制造技术与装备， 2017（5）：75-76， 111.
	LONG Y， WANG B J， MA L C， et al. Research status of key technology of car lane maintenance auxiliary system ［J］. Home and Abroad Modern Manufacturing Technology and Equipment， 2017（5）：75-76， 111.
[2]	施凯津. 智能汽车紧急变道避障轨迹规划与控制方法研究［D］.镇江：江苏大学， 2018.
	SHI K J. Research on trajectory planning and control method of emergency lane change for intelligent vehicles ［D］. Zhenjiang： Jiangsu University， 2018.
[3]	ANDERSON S， PETERS S， PILUTTI T， et al. Design and development of an optimal-control-based framework for trajectory planning， threat assessment， andsemi-autonomouscontrol of passenger vehicles in hazard avoidance scenarios ［C］. Proceedings of the14th International Symposiumon Robotics Research， 2009.
[4]	MUKAI M， KAWABE T， NISHIRA H， et al. On vehicle path generation method for collision avoidance using mixed integer programming ［C］. Proceedings of the 16th IEEE International Conference on Control Applications， 2007： 1371-1375.
[5]	王莹，卫翀，马路．基于二次规划的智能车辆动态换道轨迹规划研究［J］．中国公路学报，2021， 34（7）： 79-94.
	WANG Y， WEI C， MA L. Dynamic lane-changing trajectory planning model for intelligent vehicle based on quadratic programming ［J］. China Journal of Highway and Transport， 2021， 34（7）： 79-94.
[6]	ALI M， GRAY A， GAO Y， et al. Multi-objective collision avoidance ［C］. Proceedings of the ASME 2013 Dynamic Systems and Control Conference， 2013， 3： 1-10.
[7]	GRAY A， GAO Y， LIN T， et al. Predictive control for agile semi-autonomous ground vehicles using motion primitives ［C］. 2012 American Control Conference （ACC）， 2012： 4239-4244.
[8]	GAO Y， LIN T， BORRELLI F， et al. Predictive control of autonomous ground vehicles with obstacle avoidance on slippery roads ［C］. Proceedings of the ASME 2010 Dynamic Systems and Control Conference， 2010， 1： 265-272.
[9]	KUWATA Y， KARAMAN S. Real-time motion planning with applications to autonomous urban driving ［J］. IEEE Transactions on Control Systems Technology， 2009， 17（5）： 1105-1118.
[10]	MOUHAGIR H， TALJ R， CHERFAOUI V， et al. Integrating safety distances with trajectory planning by modifying the occupancy grid for autonomous vehicle navigation［C］. 2016 IEEE 19th International Conference on Intelligent Transportation Systems （ITSC）， 2016， 1114-1119.
[11]	张新锋，陈建伟，左思. 基于贝塞尔曲线的智能商用车换道避障轨迹规划［J］. 科学技术与工程， 2020， 20 （29）： 12150-12157.
	ZHANG X F， CHEN J W， ZUO S. Trajectory planning for intelligent commercial vehicle obstacle avoidance based on quartic Bezier curve ［J］. Science Technology and Engineering， 2020， 20（29）： 12150-12157.
[12]	EIDEHALL A， DAVID M. Real time path planning for threat assessment and collision avoidance by steering ［C］. 16th International IEEE Conference on Intelligent Transportation Systems （ITSC 2013）， 2013， 916-921.
[13]	牛国臣，李文帅，魏洪旭. 基于双五次多项式的智能汽车换道轨迹规划［J］. 汽车工程， 2021， 43 （7）： 978-986， 1004.
	NIU G C，LI W S， WEI H X. Intelligent vehicle lane changing trajectory planning based on double quintic polynomials ［J］. Automotive Engineering， 2021， 43 （7）： 978-986， 1004.
[14]	RUPP A， STOLZ M. Survey on control schemes for automated driving on highways ［M］. Springer International Publishing， 2017： 43-69.
[15]	NIE L Z， GUAN J Y， et al. Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network ［J］. IET Intelligent Transport Systems， 2018， 12（6）： 485-494.
[16]	DONG X P， PEI H Y， GAN M. Autonomous vehicle lateral control based on fractional-order PID ［C］. 2021 IEEE 5th Information Technology，Networking，Electronic and Automation Control Conference （ITNEC） 5 （2021）： 830-835.
[17]	ZHANG G D， YONG X L， XU H Y， et al. Research on pressurizer pressure control system based on BP neural network control of self-adjusted PID parameters ［J］. Applied Mechanics and Materials， 2013， 291-294： 2416-2423.
[18]	FERRARA A， GIAN P I. Design of an integral suboptimal second-order sliding mode controller for the robust motion control of robot manipulators ［J］. IEEE Transactions on Control Systems Technology， 2015， 23（6）： 2316-2325.
[19]	胡杰，张志凌，钟杰锋，等. 考虑复杂扰动的轻型商用车路径跟踪混合控制方法［J］. 汽车工程， 2024， 46 （9）： 1576-1586.
	HU J， ZHANG Z L， ZHONG J F， et al. A hybrid control strategy for Light commercial vehicle path tracking considering complex disturbances ［J］. Automotive Engineering， 2024， 46 （9）： 1576-1586.
[20]	陈亮，秦兆博，孔伟伟，等. 基于最优前轮侧偏力的智能汽车LQR横向控制［J］. 清华大学学报（自然科学版）， 2021， 61 （9）： 906-912.
	CHEN L， QIN Z B， KONG W W， et al. Lateral control using LQR for intelligent vehicles based on the optimal front-tire lateral force ［J］. Journal of Tsinghua University （Science and Technology）， 2021，61（9）：906-912.
[21]	ZHENG Z A， YE Z M， ZHENG X Y. Intelligent Vehicle lateral control strategy research based on feedforward + predictive LQR algorithm with GA optimisation and PID compensation ［J］. Scientific Reports， 2024， 14 （1）.
[22]	MAYNE D. A second-order gradient method for determining optimal trajectories of non-linear discrete-time systems ［J］. International Journal of Control， 1966， 3（1）： 85-95.
[23]	DIEHL M， BOCK H， DIEDAM H， et al. Fast direct multiple shooting algorithms for optimal robot control ［J］. Fast Motions In Biomechanics and Robotics： Optimization and Feedback Control， 2006，340： 65-93.
[24]	MANCHESTER Z， SCOTT K. Derivative-free trajectory optimization with unscented dynamic programming ［C］. 2016 IEEE 55th Conference on Decision and Control （CDC）， 2016， 3642-3647.

Research on Autonomous Emergency Steering of Vehicle Based on Offline Path Planning and iLQR Control

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 17

References 24

Related Articles 15

Metrics

Comments

Recommended 0

[1]	Qinyu Sun,Hang Zhou,Rui Fu,Chang Wang,Tao Huang,Junfeng Yang,Yunhao Wang. Prediction of Lane Change Intention Based on Driver's Cognitive-Making Space [J]. Automotive Engineering, 2025, 47(8): 1468-1478.
[2]	Jian Wu,Yukang Shi,Bing Zhu,Jian Zhao,Zhicheng Chen. Intelligent Vehicle Decision for Roundabouts Based on Subjective Prior Reinforcement Learning [J]. Automotive Engineering, 2025, 47(8): 1513-1521.
[3]	Bing Zhu,Yinzi Huang,Jian Zhao,Peixing Zhang,Zhitong Gao,Jingwei Xue. Adverse Weather Condition Digital-Physical Fusion Simulation Based Intelligent Vehicle Camera-in-the-loop Test [J]. Automotive Engineering, 2025, 47(7): 1335-1343.
[4]	Xiujian Yang,Xulei Feng,Shengbin Zhang,Yongrui Bai. Trajectory Planning for Intelligent Vehicles in Unstructured Environment Based on Spatio-Temporal Corridors [J]. Automotive Engineering, 2025, 47(7): 1357-1368.
[5]	Xin Jia,Songlin Li,Yuansheng She,Feng Hong. Research on Environmental Perception Information Unified Fusion Method of Intelligent Vehicle Based on Interactive Multiple Models [J]. Automotive Engineering, 2025, 47(6): 1144-1154.
[6]	Lu Xiong,Jiaqi Zhu,Mengyuan Chen,Ziyao Li,Qiang Shu,Guirong Zhuo. Positioning Method Based on Slip Ratio Compensation for Intelligent Vehicles [J]. Automotive Engineering, 2025, 47(5): 851-858.
[7]	Bing Zhu,Rui Tang,Jian Zhao,Peixing Zhang,Wenxu Li,Jiasheng Li,Xuefeng Xu. Virtual Simulation Testing Method for Intelligent Vehicle Based on Large Language Model [J]. Automotive Engineering, 2025, 47(4): 587-597.
[8]	Nan Xu,Zhuo Yin,Yuetao Zhang,Konghui Guo. MPC Path Tracking Based on Adaptive Predictive Horizon Considering Active Actuators Characteristics [J]. Automotive Engineering, 2025, 47(10): 1847-1860.
[9]	Xianghao Meng,Ling Niu,Junqiang Xi,Danni Chen,Chao Lü. Risk Prediction of Heterogeneous Traffic Participants Based on Spatio-Temporal Graph Neural Networks [J]. Automotive Engineering, 2024, 46(9): 1537-1545.
[10]	Xiujian Yang,Yongrui Bai. Trajectory Planning for Intelligent Vehicle in Dynamic Unstructured Environment Based on the Graph Search and Optimization Methods [J]. Automotive Engineering, 2024, 46(9): 1564-1575.
[11]	Jing Zhao,Hao Liang,Tianxiao Xu,Yayue Xiao,Bowen Jiang. Research on Domain Specific Modeling Language for Intelligent Vehicle Cyber-Physical System [J]. Automotive Engineering, 2024, 46(8): 1370-1381.
[12]	Jialiang Zhu,Qiaobin Liu,Fan Yang,Lu Yang,Weihua Li. Two-Dimensional Collision Risk Prediction for Intelligent Vehicles Considering the Influence of Heterogeneous Vehicle Types [J]. Automotive Engineering, 2024, 46(8): 1414-1421.
[13]	Xiaolin Fan,Xudong Zhang,Yuan Zou,Xin Yin,Yingqun Liu. A Mapping and Planning Method Based on Simplified Visibility Graph [J]. Automotive Engineering, 2024, 46(7): 1249-1258.
[14]	Shuen Zhao,Sheng Wang,Yao Leng. Multi-objective Explicit Model Predictive Control for Intelligent Vehicle Trajectory Tracking [J]. Automotive Engineering, 2024, 46(5): 784-794.
[15]	Bing Zhu,Yinzi Huang,Jian Zhao,Peixing Zhang,Jingwei Xue. A Criticality Assessment Model for the Intelligent Vehicle Test Scenario Based on the Onboard Camera Images [J]. Automotive Engineering, 2024, 46(4): 557-563.