83 Unfall und Mensch
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The strong prevalence of human error as a crash causation factor in motorcycle accidents calls for countermeasures that help tackling this issue. Advanced rider assistance systems pursue this goal, providing the riders with support and thus contributing to the prevention of crashes. However, the systems can only enhance riding safety if the riders use them. For this reason, acceptance is a decisive aspect to be considered in the development process of such systems. In order to be able to improve behavioural acceptance, the factors that influence the intention to use the system need to be identified. This paper examines the particularities of motorcycle riding and the characteristics of this user group that should be considered when predicting the acceptance of advanced rider assistance systems. Founded on theories predicting behavioural intention, the acceptance of technologies and the acceptance of driver support systems, a model on the acceptance of advanced rider assistance systems is proposed, including the perceived safety when riding without support, the interface design and the social norm as determinants of the usage intention. Since actual usage cannot be measured in the development stage of the systems, the willingness to have the system installed on the own motorcycle and the willingness to pay for the system are analyzed, constituting relevant conditions that allow for actual usage at a later stage. Its validation with the results from user tests on four advanced rider assistance systems allows confirming the social norm and the interface design as powerful predictors of the acceptance of ARAS, while the extent of perceived safety when riding without support did not have any predictive value in the present study.
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.
Immediate user self-evacuation is crucial in case of fire in road tunnels. This study investigated the effects of information with or without additional virtual reality (VR) behavioural training on self-evacuation during a simulated emergency situation in a road tunnel. Forty-three participants were randomly assigned to three groups with accumulating preventive training: The control group only filled in questionnaires, the informed group additionally read an information brochure on tunnel safety, and the VR training group received an additional behavioural training in a VR tunnel scenario. One week later, during the test session, all participants conducted a drive through a real road tunnel in which they were confronted with a collision of two vehicles and intense smoke. The informed and the behaviourally trained participants evacuated themselves more reliably from the tunnel than participants of the control group. Trained participants showed better and faster behavioural responses than informed only participants. Interestingly, the few participants in the control group who reacted adequately to the scenario were all female. A 1 year follow-up online questionnaire showed a decrease of safety knowledge, but still the trained group had somewhat more safety relevant knowledge than the two other groups. Information and especially VR behavioural training both seem promising to foster adequate self-evacuation during crisis situations in tunnels, although long term beneficial behavioural effects have to be demonstrated. Measures aiming to improve users/ behaviour should take individual difference such as gender into account.