Post-Impact Motion Planning and Tracking Control for Autonomous Vehicles

There is an increasing awareness of the need to reduce traffic accidents and fatality due to vehicle collision. Post-impact hazards can be more serious as the driver may fail to maintain effective control after collisions. To avoid subsequent crash events and to stabilize the vehicle, this paper pro...

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Bibliographic Details
Published in:Chinese journal of mechanical engineering 2022-12, Vol.35 (1), p.54-18, Article 54
Main Authors: Wang, Cong, Wang, Zhenpo, Zhang, Lei, Yu, Huilong, Cao, Dongpu
Format: Article
Language:English
Subjects:
Active safety
Actuators
Advanced Transportation Equipment
Algorithms
Autonomous vehicles
Control methods
Control stability
Controllers
Electrical Machines and Networks
Electronics and Microelectronics
Engineering
Engineering Thermodynamics
Hardware-in-the-loop simulation
Heat and Mass Transfer
Instrumentation
Linear quadratic regulator
Machines
Manufacturing
Mathematical analysis
Mechanical Engineering
Motion planning
Motion stability
Nonlinear control
Obstacle avoidance
Optimization
Original Article
Polynomials
Post-impact control
Potential fields
Power Electronics
Processes
Theoretical and Applied Mechanics
Tracking control
Traffic accidents
Vehicle dynamics control
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Summary:There is an increasing awareness of the need to reduce traffic accidents and fatality due to vehicle collision. Post-impact hazards can be more serious as the driver may fail to maintain effective control after collisions. To avoid subsequent crash events and to stabilize the vehicle, this paper proposes a post-impact motion planning and stability control method for autonomous vehicles. An 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 motion. In the upper controller, a time-varying linear quadratic regulator is presented to calculate the desired generalized forces. In the lower controller, a nonlinear-optimization-based torque allocation algorithm is proposed to optimally coordinate the actuators to realize the desired generalized forces. The proposed scheme is verified under comprehensive driving scenarios through hardware-in-loop tests.
ISSN:1000-9345
2192-8258
DOI:10.1186/s10033-022-00745-w