Abstract

We present an approach for validation of advanced driver assistance systems, based on randomized algorithms. The new method consists of an iterative randomized simulation using adaptive importance sampling. The randomized algorithm is more efficient than conventional simulation techniques. The importance sampling pdf is estimated by a kernel density estimate, based on the results from the previous iteration. The concept is illustrated with a simple adaptive cruise control problem.

Abstract

New driver support systems are developed and introduced to the market at increasing speed. In conditions of traffic congestion drivers may be supported by a "Congestion Assistant", a system that combines the features of a Congestion Warning System (acoustic warning and gas pedal counterforce) and a Stop & Go system (automatic gas and brake pedal during congestion). To gain understanding of the effects of driving with a Congestion Assistant on drivers, mental workload of drivers was registered under different conditions as well as acceptance of the system. Mental workload was measured by means of physiological registrations, i.e. heart rate, a secondary task and with the aid of subjective scaling techniques. Acceptance was measured with an acceptance scale. The study was carried out in an advanced driving simulator. Driving with the Congestion Assistant while in congestion potentially leads to decreased driver mental workload, whereas just before congestion starts, i.e. developing just noticeable, the system may add to the workload of the driver. Acceptance is generally high after experiencing the system, though not in all respects. (c) 2008 Elsevier Ltd. All rights reserved.

Abstract

urban intersections drivers handle multiple tasks simultaneously, making urban driving a complex task. An advanced driver assistance system may support drivers in this specific driving task, but the design details of such a system need to be determined before they can be fully deployed. A driving simulator experiment was conducted to determine the relationship between different subtasks of driving at urban intersections. Participants completed four drives, each comprising 20 comparable intersections with different traffic situations and encountered one unexpected braking event during the experiment. The effects of varying levels of event urgency on the relationship between different driving subtasks were studied. Furthermore, the influence of workload on this relationship was determined by giving half of the subjects an additional cognitive task. After the lead car braked unexpectedly, participants reduced speed and increased headway depending on the urgency of the braking event. Depending on the workload, participants returned to the normal speed and headway again after a number of intersections. Participants experiencing a high-workload drove more smoothly, except for those who had experienced the most urgent unexpected event. High workload additionally affected the length of the adjustments to the unexpected event.

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.