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

摘要/Abstract

摘要：

本文针对自动紧急转向场景，基于最短逃逸时间，离线生成的贝塞尔轨迹作为实时的避障路径，并结合迭代线性二次调节器（iLQR）方法，进行轨迹跟踪和控制。使用仿真环境，对上述方法进行验证，相比传统的基于五次多项式生成的轨迹，“最速轨迹”更符合自动紧急转向要求。此外，结合离线生成的方式，其算力消耗较小，且充分利用了车辆的转向性能，减少了大量的参数标定。对于自动紧急转向，其控制误差变化更为剧烈，相应的控制输入较大，需要考虑系统的非线性。iLQR通过迭代的方式，在定义的预测空间内，得到最优控制输入，满足系统快速响应的要求。

关键词: 智能汽车, 自动紧急转向, 离线路径规划, 迭代线性二次调节器, 预测空间

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

涂宁宁,曹安,袁沐,侯明洋,梁盛平. 基于离线路径规划与iLQR控制的汽车自动紧急转向研究[J]. 汽车工程, 2025, 47(10): 1933-1941.

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.

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参考文献 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.