Abstract

This thesis deals with a model predictive control framework for control design of Advanced Driver Assistance Systems, where car-following tasks are under control. The framework is applied to design several autonomous and cooperative controllers and to examine the controller properties at the microscopic level and the resulting traffic flow characteristics at the macroscopic level. The results give new insights into impacts of ADAS on traffic flow characteristics.

Abstract

Co-operative driving with speed adaptation functionality has great potential to improve traffic-throughput, traffic-safety, and environmental-impact on heavily used traffic-infrastructures. A driving-simulator study was performed to investigate the driver behaviour with respect to such driver-support systems (Zero-, Advisory-, Intervention-, and Controlling). This paper describes the results of one specific scenario, a cut-in scenario. The results show a small reaction time, which was smaller than the response-time required for stabilizing the manoeuvre. Subjective measures show that the experienced-effort for the Controlling system was the smallest, that the satisfaction for the Controlling system was the highest and that the usefulness of all systems was positive.