Rear-End Collision Avoidance-Based on Multi-Channel Detection

Yu Xiang, Sida Huang, Min Li, Jin Li, Wenyong Wang

Abstract: With multi-sensor-based collision avoidance systems (CASs) being adopted in today’s automobiles, a new method that enables collaborative decision-making with preceding vehicle detection under various external environments is needed. In this paper, spatial–temporal correlations of multi-channel signals that are collected by multiple sensors on the host vehicle are considered, and a multi-channel detection technique with a stochastic model is introduced for automobile collision avoidance. We propose an accurate and robust multi-channel, generalized likelihood ratio test (GLRT)-based detection and collaborative decision-making scheme, with a vehicle kinematic analysis for avoiding rear-end collisions. The results of simulations and physical experiments demonstrated that our detector expands the detection range with a high detection rate and that our proposed scheme obtains good performance under varying operating and environmental conditions.