Abstract: The number of accidents that can be attributed to driving under the influence of psychoactive substances (alcohol, drugs, and certain medicines) is constantly on a high level with drugs and medicines proportionally increasing over the years. The overall objective of the EU 6th Framework Programme project DRUID is to gain better knowledge of the various aspects of driving under the influence of drugs, alcohol and medicines. DRUID wants to offer scientific support to EU transport policy makers by suggesting guidelines and measures to combat impaired driving. To reach this ambitious aim a wide range of studies is conducted. The various studies are divided into seven work packages with complex interdependencies. There are experimental studies assessing the effects of single and combined psychoactive substances on driving performance (WP1) as well as epidemiological studies aiming to assess the situation in Europe regarding prevalence of alcohol and other psychoactive substances in drivers (WP2).The principal objective of these studies is to gain relative risk estimates for traffic accident involvement of drivers impaired by psychoactive substances and to recommend substance concentration thresholds. A theoretical framework which allows the integration of the experimental and epidemiological findings serves as a fundament for developing these recommendations. WP3 aims at improving the possibilities of detecting drug driving in Europe. Police forces evaluate practically (under realistic enforcement conditions) oral fluid screening devices. A scientific evaluation of oral fluid screening devices and other methods (i.e. roadside checklists of signs of impairment) is done as well. The outcome of the practical and scientific evaluations serves as input to cost-benefit analyses of enforcement.
Accident data shows that the vast majority of pedestrian accidents involve a passenger car. A refined method for estimating the potential effectiveness of a technology designed to support the car driver in mitigating or avoiding pedestrian accidents is presented. The basis of the benefit prediction method consists of accident scenario information for pedestrian-passenger car accidents from GIDAS, including vehicle and pedestrian velocities. These real world pedestrian accidents were first reconstructed and the system effectiveness was determined by comparing injury outcome with and without the functionality enabled for each accident. The predictions from Volvo Cars" general Benefit Estimation Model are refined by including the actual system algorithm and sensing models for a relevant car in the simulation environment. The feasibility of the method is proven by a case study on a authentic technology; the Auto Brake functionality in Collision Warning with Full Auto Brake and Pedestrian Detection (CWAB-PD). Assuming the system is adopted by all vehicles, the Case Study indicates a 24% reduction in pedestrian fatalities for crashes where the pedestrians were struck by the front of a passenger car.