Summary: Driver drowsiness, compounded by the high workloads and stress of the ever-increasing complexity of car and traffic environments, is a major cause of severe accidents. The objective of the project described in this paper is to develop reproducible and flexible methods for studying the relationships between physiological driver states and human-factor issues in a driving environment. For reasons of safety and reproducibility, a laboratory-based driving simulator is being used for the project experiments. Initial experiments were conducted with a cohort of about 60 healthy male subjects aged 22 to 28 under carefully controlled conditions. Performance was measured before, during, and after a 120 km stretch of stimulus-deprived, foggy highway that was intended to induce fatigue and stress. Across all trials 69% of the subjects experienced sleep events lasting several seconds, and 7 potentially fatal crashes occurred. Lane tracking behavior degraded by a factor of 2 to 3 prior to each crash. Much of the extensive data acquired by these experiments remains to be analyzed using both standard statistical techniques and highdimensional clustering algorithms. ALISA image-processing software is being applied to video images of the driver eyes and face to detect the onset of sleep and other critical situations.

Summary: Driver drowsiness is a major cause of severe accidents, many of which involve a single vehicle lane departure. The objective of the experiment described in this paper is to determine the relationships between drowsiness, lane departure events (LDE) and effects of a warning system. While in case of driver distraction the impact of such a warning system can be tested in real traffic, for reasons of safety (and reproducibility), a laboratory-based driving simulator is being used in this project. The experiments were conducted with a cohort of 63 healthy male subjects aged 22 to 27 driving for about 2.5 hrs in a stimuli-deprived scenario with a six-fold repetition under carefully controlled conditions. Several hundreds micro-sleep episodes were identified in the 53 successful trials by electrooculogram and video signal and confirmed by behavioral analysis; more than 800 lane departure warnings (LDW) occurred in the assisted sub-cohort of 17 drivers. A combined analysis of the LDE with and without LDW shows significant reduction in number, time, departure length and out-of-lane area for the assisted subjects. The timing and design of the warning could furthermore prevent almost 85% of the lane departure events caused by sleepiness.

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Summary:The increasing number and complexity of in-vehicle information systems (IVIS) and advanced driver assistance systems (ADAS) require an accurate and timely assessment of their impact on traffic safety even during the development process. The I-TSA evaluation tool, developed within the German research consortium INVENT, offers a standardized procedure for the assessment of traffic safety based on the driving error occurrence in up to 10 categories of parameters (e.g., the category "longitudinal control" includes the errors in speed, time headway and time to collision). The objective of the experiment presented here was to determine the validity and sensitivity of the I-TSA tool for this evaluation process. A homogeneous cohort of 41 young, healthy males (25 to 40 years old) drove for approximately 1 hour in a static simulator environment. The scenario on a two-lane motorway consisted of 4 counterbalanced drives with easy and difficult road shapes and traffic conditions. The trial included several interaction tasks with IVIS and ADAS differing in their stage of integration and adaptivity. The successful induction of high workload levels could be both detected by objective (such as speed compensation) and subjective measures (questionnaire). Highly significant differences in the safety levels were found between the easy and the difficult drives (demonstrating the suitability of the procedure) as well as between the sections with default and integrated behavior of the information systems (supporting its sensitivity). Preliminary results support the possibility of discriminating between visual and cognitive workload, as well as sensitivity to learning effects.