Collision detection system for lane change on multi-lanes using convolution neural network

초록

This paper proposes a collision detection system to detect whether ego and target vehicles collide when both vehicles change from their lanes to the same lane. Although it is essential to predict this kind of collision for the active safety system, there is little literature on the case study. This paper presents the collision detection method using a Convolution Neural Network (CNN) consisting of four classes to predict collision risk on multi-lanes road conditions. The CNN is formed on stacked Occupancy Grid Maps (OGMs) based on point cloud data of the LiDAR and Radar sensors with in-vehicle sensor data for spatio-temporal information between vehicles. Further, we apply the open set recognition concept to the network to consider a conservative collision detection. The experimental results show the feasibility of the proposed collision detection system and the conservative decision about the confusing situation.

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