Collision Avoidance Theory with Application to Automotive Collision Mitigation
Abstract: Avoiding collisions is a crucial issue in most transportation systems as well as in many other applications. The task of a collision avoidance system is to track objects of potential collision risk and determine any action to avoid or mitigate a collision. This thesis presents theory for tracking and decision making in collision avoidance systems. The main focus is how to make decisions based on uncertain estimates and in the presence of multiple obstacles. A general framework for dealing with nonlinear dynamic systems and arbitrary noise distributions in collision avoidance decision making is proposed. Some novel decision functions are also suggested. Furthermore, performance evaluations using simulated and experimental data are presented. Most examples in this thesis are from automotive applications.A driving application for the work presented in this thesis is an automotive emergency braking system. This system is called a collision mitigation by braking (CMbB) system. It aims at mitigating the consequences of an accident by applying the brakes once a collision becomes unavoidable. A CMbB system providing a maximum collision speed reduction of 15 km/h and an average speed reduction of 7.5 km/h is estimated to reduce all injuries, classified as anything between moderate and fatal, for rear-end collisions by 16%. Since rear-end collision correspond to approximately 30% of all accidents this corresponds to a 5% reduction for all accidents.The evaluation includes results from simulations as well as two demonstrator vehicles, with different sensor setups and different decision logic, that perform autonomous emergency braking.
CLICK HERE TO DOWNLOAD THE WHOLE DISSERTATION. (in PDF format)