Intersection collision risk evaluation and active collision avoidance strategies for autonomous vehicles

Abstract

In order to ensure that the autonomous vehicle can predict and taking actions to avoid the collision in time when facing the obstacles with intersection collision risk, an intersection collision risk prediction system is proposed in this paper, and two kinds of active obstacle avoidance strategies are designed according to the system: braking strategy and steering strategy. The position information of the obstacle is predicted by Fractional extended Kalman filter, the collision risk rate is determined by the time difference between the vehicle and the obstacle through the intersection point, and a neural network is trained to quickly give the collision risk of the vehicle and the obstacle. Braking strategy and steering strategy are formulated according to collision risk, the braking deceleration and Sigmoid path parameters are given. Finally, the simulation results of PreScan and MATLAB show that the collision risk prediction system can accurately predict the collision between vehicles and obstacles, the braking and steering strategies can effectively avoid the collision.