A Real-time Implementation of an Intersection Collision Avoidance System

Abstract

AbstractThis paper presents a real-time implementation of a collision avoidance (CA) system that uses autonomous braking and model predictive control to assist drivers in avoiding collisions with other road users. To the authors knowledge, this is the first CA system that targets general vehicle collisions that has been implemented in a car. The system is based on a recently published decision-making algorithm which is described in [1]. To validate the CA system in various collision scenarios without endangering the driver of the vehicle, a novel test platform has been developed. The test platform consist of a soft crashable obstacle which is movable in speeds up to 70 km/h and safe to collide with in any angle in relative speeds up to 100 km/h. In the current implementation, estimates of the motion of the obstacle are obtained through a reference sensor fusion system that is based on a combination of in-vehicle sensors and a differential global positioning system. Results from both intersection and rear-end collision situations are presented. The results show that the proposed CA system can be implemented in a real-time environment and that the predictive brake control algorithm accurately accounts for delays and ramp-up times in the brake system of the vehicle.