Abstract
In autonomous cars, the automation systems assume complete operational control. In this situation, it is essential that passengers always feel comfortable with the vehicle’s decisions. In this project, we are specifically interested in risk assessment by the passenger of an autonomous car navigating among pedestrians in a shared space. A driving simulator experiment was conducted with 27 participants. The challenge was twofold: on the one hand, to find a link between the pedestrians’ avoidance behavior of the vehicle and the risk felt by the passenger; and on the other hand, to try to predict this perceived risk in real time. The study revealed a significant effect of two factors on the risk assessed by the participants: (1) the value of the TTC at the moment the vehicle begins a pedestrian avoidance maneuver; (2) the lateral distance it leaves to the pedestrian. The proposed real-time prediction model is based on the principle of impulse response operation. This new paradigm assumes that the passenger’s risk assessment is the result of a quantifiable unconscious internal phenomenon that has been estimated using the dynamics of the perceived pedestrian approach. The results showed that this approach was predictive of risk for isolated avoidance maneuvers, but was insufficient to explain the variability in the risk assessment behavior of the participants.
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Petit, J., Charron, C., Mars, F. (2020). A Pilot Study on the Dynamics of Online Risk Assessment by the Passenger of a Self-driving Car Among Pedestrians. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. Automated Driving and In-Vehicle Experience Design. HCII 2020. Lecture Notes in Computer Science(), vol 12212. Springer, Cham. https://doi.org/10.1007/978-3-030-50523-3_8
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