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Post-Impact Motion Planning and Tracking control for Autonomous vehicles

Chinese Journal of Mechanical Engineering 2022年第5期35卷 315-332页

作者： Cong Wang Zhenpo Wang Lei Zhang Huilong Yu Dongpu Cao National Engineering Research Center for Electric vehicles Beijing Institute of TechnologyBeijing 100081China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China School of vehicle and Mobility Tsinghua UniversityBeijing 100080China

There is an increasing awareness of the need to reduce traffic accidents and fatality due to vehicle ***-impact hazards can be more serious as the driver may fail to maintain effective control after *** avoid subsequent crash events and to stabilize the vehicle,this paper proposes a post-impact motion planning and stability control method for autonomous *** enabling motion planning method is proposed for post-impact situations by combining the polynomial curve and artificial potential field while considering obstacle avoidance.A hierarchical controller that consists of an upper and a lower controller is then developed to track the planned *** the upper controller,a time-varying linear quadratic regulator is presented to calculate the desired generalized *** the lower controller,a nonlinear-optimization-based torque allocation algorithm is proposed to optimally coordinate the actuators to realize the desired generalized *** proposed scheme is verified under comprehensive driving scenarios through hardware-in-loop tests.

关键词： Active safety Post-impact control Motion planning vehicle dynamics control

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Deep reinforcement learning-based drift parking control of automated vehicles

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Science China(Technological Sciences) 2023年第4期66卷 1152-1165页

作者： LENG Bo YU YiZe LIU Ming CAO Lei YANG Xing XIONG Lu School of Automotive Studies Tongji University

Drift parking usually requires precise control of a vehicle by a professional driver,which can reflect the performance of the vehicle under critical *** obstacles to implementing this action include the high coupling between the longitudinal and lateral states,the high precision required for the vehicle initial state when the drift is triggered,and the difficulty in determining the reference state variables in the drift process.A two-segment drift parking control system is proposed in this *** the approaching control segment,the vehicle achieves the drift-triggered vehicle speed and pose,which relies on a pathtracking algorithm based on linear time-varying model predictive *** the drifting control segment,the deep reinforcement learning algorithm twin-delayed deep deterministic policy gradient is creatively introduced to the controller *** solves the precise vehicle motion control problem under the condition of the rear wheels having locked *** various simulations,the superiority and robustness to different initial conditions and abrupt changes in the parking space are *** effectiveness of the proposed control system is verified by a ground test.

关键词： automated vehicles deep reinforcement learning drifting control vehicle dynamics control

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