Research On Motion Control And Decision-making Method Of Emergency Collision Avoidance System For Autonomous Vehicle

With the development of automobile industry and artificial intelligence technology,autonomous vehicles are coming into reality.However,the autonomous driving technology is not mature at the current stage.When autonomous vehicles leave research laboratory and enter into public traffic,they must be able to deal with emergency situations.This paper proposed a collision avoidance system for autonomous vehicle in emergency situations at high speed.Meanwhile,the important basic theories and key technical involved in motion control and decision-making mechanism are researched.Considering system nonlinearity,uncertainty and unknown external disturbance,the longitudinal and lateral motion control strategies for autonomous vehicle are designed by combining backstepping control method,variable structure control scheme,adaptive control theory and neural network technology,and using hierarchical control framework.The proposed strategy can improve robustness and stability of the closed-loop system,and to some extent,it can also solve the problem that the vehicle is difficult to control at driving limitsThe fifth-order polynomial equation and boundary conditions are adopted to obtain the initial expression of collision-free trajectory.Then,from the aspect of kinematics,an ideal vehicle yaw rate formula is derived from the collision avoidance trajectory expression,and the genetic algorithm is used to obtain the equivalent maximum expression of the desired yaw rate.Meanwhile,from the aspect of dynamics,the maximum expression of vehicle yaw rate is derived from the constraint condition of road surface adhesion.Finally,through the reasonable design of terminal point coordinates for the collision-free trajectory,a risk assessment model which can simultaneously consider the risk associated with collision and destabilization is derived.The proposed scheme can provide an effective solution to quantify and evaluate the risk for autonomous vehicle in emergency situations.In order to generate initial training data,a rule-based behavior decisionmaking system is designed by analyzing an excellent driver’s emergency collision avoidance manipulation and vehicle dynamics characteristics.Then,an imitative learning algorithm is designed with the Softmax classifier,and the neural network of the imitative learning is trained offline through the mini-batch stochastic gradient descent(MSGD)algorithm.A model-based value function approximation Q(λ)-learning algorithm is designed,and the policy model learned by the imitation learning algorithm is used as the initial strategy for the reinforcement learning.The proposed method can improve the efficiency and effectiveness of behavior decision-making,and it provides a technical means to online sequential decision under the condition of small sample.The simulation analysis and experimental study are performed using the cosimulation platform and the hardware-in-the-loop(HIL)system,respectively.According to the results,the proposed scheme can effectively perform an emergency collision avoidance maneuver while stabilising vehicle.