OP-CAS: Collision Avoidance with Overtaking Maneuvers

Abstract

This paper presents a novel collision avoidance system for autonomous vehicles based on overtaking procedures. The proposed Overtaking Procedure for Collision Avoidance Systems (OP-CAS) takes a behavioral cloning-based approach which uses images obtained out of a low cost monocular camera. The algorithm selectively records the expert's corrective driving behavior during data collection. This is performed recording oscillatory driving behavior when the vehicle is returning to the center of the lane. This data augmentation method addresses the issue of covariate shift commonly found in behavioral cloning methods. This approach is computationally inexpensive, making it a viable option for real time embedded deployment. A feasibility study was performed with two remotely controlled scaled vehicles as a proof of concept. Results showed that when two expert drivers demonstrated overtaking behaviors for data collection, even a small dataset was sufficient to model the overtaking sequence. The overtaking maneuvers were deployed in real time on 1/8th scale RC platforms, validating OP-CAS for civilian vehicle safety applications.