Vehicle Path Planning Based on Pedestrian Collision Avoidance Action

Abstract

Although significant progress has been made in developing autonomous vehicle technology, further research is still needed to reduce the risk of collision by planning a suitable path. Drawing vehicle paths through pedestrian collision avoidance action is worth further study. In this paper, vehicle path planning considering pedestrian collision avoidance action is proposed to improve vehicle driving safety further. First, the movement features reflecting different types of pedestrian collision avoidance intention are summarized through the actual human-vehicle collision accident video. Then, the data representing the pedestrian action features are obtained through the deep neural network and input into the trained Hidden Markov Model to predict pedestrian intention. Finally, the vehicle collision avoidance path is planned through the cubic polynomial combined with the pedestrian intention. The algorithm is verified through the pedestrian collision avoidance action in the simulation scene. The results show that this algorithm can predict the intention of different types of pedestrians to avoid collisions at frames 9, 8, and 10, respectively, which is 0.6 seconds, 0.4 seconds, and 0.6 seconds faster than the Long Short Term Memory algorithm. Finally, a suitable path is planned, and the collision was successfully avoided.