83 Unfall und Mensch
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There is considerable evidence for the negative effects of driver distraction on road safety. In many experimental studies, drivers have been primarily viewed as passive receivers of distraction. Thus, there is a lack of research on the mediating role of their self-regulatory behavior. The aim of the current study was to compare drivers' performance when engaged in a system-paced secondary task with a self-paced version of this task and how both differed from baseline driving performance without distraction. Thirty-nine participants drove in a simulator while performing a secondary visual"manual task. One group of drivers had to work on this task in predefined situations under time pressure, whereas the other group was free to decide when to work on the secondary task (self-regulation group). Drivers' performance (e.g., lateral and longitudinal control, brake reaction times) was also compared with a baseline condition without any secondary task. For the system-paced secondary task, distraction was associated with high decrements in driving performance (especially in keeping the lateral position). No effects were found for the number of collisions, probably because of the lower driving speeds while distracted (compensatory behavior). For the self-regulation group, only small impairments in driving performance were found. Drivers engaged less in the secondary task during foreseeable demanding or critical driving situations. Overall, drivers in the self-regulation group were able to anticipate the demands of different traffic situations and to adapt their engagement in the secondary task, so that only small impairments in driving performance occurred. Because in real traffic drivers are mostly free to decide when to engage in secondary tasks, it can be concluded that self-regulation should be considered in driver distraction research to ensure ecological validity.
The main focus of the benefit estimation of advanced safety systems with a warning interface by simulation is on the driver. The driver is the only link between the algorithm of the safety system and the vehicle, which makes the setup of a driver model for such simulations very important. This paper describes an approach for the use of a statistical driver model in simulation. It also gives an outlook on further work on this topic. The build-up process of the model suffices with a distribution of reaction times and a distribution of reaction intensities. Both were combined in different scenarios for every driver. Each scenario has then a specific probability to occur. To use the statistical driver model, every accident scene has to be simulated with each driver scenario (combinations of reaction times and intensities). The results of the simulations are then combined regarding the probabilities to occur, which leads to an overall estimated benefit of the specific system. The model works with one or more equipped participants and delivers a range for the benefit of advanced safety systems with warning interfaces.
It is commonly agreed that active safety will have a significant impact on reducing accident figures for pedestrians and probably also bicyclists. However, chances and limitations for active safety systems have only been derived based on accident data and the current state of the art, based on proprietary simulation models. The objective of this article is to investigate these chances and limitations by developing an open simulation model. This article introduces a simulation model, incorporating accident kinematics, driving dynamics, driver reaction times, pedestrian dynamics, performance parameters of different autonomous emergency braking (AEB) generations, as well as legal and logical limitations. The level of detail for available pedestrian accident data is limited. Relevant variables, especially timing of the pedestrian appearance and the pedestrian's moving speed, are estimated using assumptions. The model in this article uses the fact that a pedestrian and a vehicle in an accident must have been in the same spot at the same time and defines the impact position as a relevant accident parameter, which is usually available from accident data. The calculations done within the model identify the possible timing available for braking by an AEB system as well as the possible speed reduction for different accident scenarios as well as for different system configurations. The simulation model identifies the lateral impact position of the pedestrian as a significant parameter for system performance, and the system layout is designed to brake when the accident becomes unavoidable by the vehicle driver. Scenarios with a pedestrian running from behind an obstruction are the most demanding scenarios and will very likely never be avoidable for all vehicle speeds due to physical limits. Scenarios with an unobstructed person walking will very likely be treatable for a wide speed range for next generation AEB systems.
Nowadays human-created systems are increasing in complexity due to the interaction of humans and technology. Especially road traffic systems are composed of multitudinous resources (e.g. personnel, vehicles, organizations, etc.), which make it even harder to anticipate the positive and negative effects on safety. One key in achieving a significant reduction of fatalities is seen in driver assistant systems counterbalancing the lack of drivers' capabilities. But the actual outcome of implementing these sophisticated technologies especially on influencing driver's capabilities are yet unknown. Latest research exemplifies an increase of reaction times of drivers in case of dysfunctional driver assistant systems. This research paper applies STAMP/STPA (STAMP = systems-theoretic accident model and processes; STPA = systems-theoretic process analysis) to the German automobile traffic system focusing on the effects of driver assistant systems on drivers. By doing so, the potential hazards caused by technology can be identified.
This article describes the development of techniques to minimize automobile driver distraction when an in-vehicle information systems (IVIS) that requires visual attention is in use. The authors explain the visual occlusion technique that has been developed as a tool for the assessment of the in-vehicle human-machine interface (HMI) of IVIS in terms of visual demands. The authors addressed an unresolved issue in previous standardized experimental protocols - how subjects make use of the occluded intervals and how this might affect the assessments of visual demands. This study protocol assumed that subjects would continue task performance during occluded periods, leading to an underestimation of visual demands by the occlusion parameters "total shutter open time" (TSOT) and the "occlusion index". The authors predicted that a simple additional loading task to be performed in parallel could disrupt IVIS task performance during the occluded period leading to higher estimations of visual demands by TSOT and R. Their prediction was confirmed by the study findings. The results also showed that under the condition of additional auditory tracking, TSOT and R discriminated more clearly between an "easy" and a "difficult" IVIS task than under the standard condition. They conclude with a discussion of the implications of this research for designers of assessment tools for driver visual distractions.