This article sets out to identify the typical risky situations experienced by novice motorcyclists in the real world just after licensing. The procedure consists of a follow-up of six novices during their first two months of riding with their own motorbike instrumented with cameras. The novices completed logbooks on a daily basis in order to identify the risky situations they encountered, and were given face-to-face interviews to identify the context and their shortcomings during the reported events. Data show a large number of road configurations considered as risky by the riders (248 occurrences), especially during the first two weeks. The results revealed that a lack of hazard perception skills contributed to the majority of these incidents. These situations were grouped together to form clusters of typical incident scenarios on the basis of their similarities. The most frequent scenario corresponds to a lane change in dense traffic (15% of all incidents). The discussion shows how this has enhanced our understanding of novice riders' behaviour and how the findings can improve training and licensing. Lastly, the main methodological limitations of the study and some guidelines for improving future naturalistic riding studies are presented. Practitioner Summary: This article aims to identify the risky situations of novice motorcyclists in real roads. Two hundred forty-eight events were recorded and 13 incident scenarios identified. Results revealed that a lack of hazard perception contributed to the majority of these events. The most frequent scenario corresponds to a lane change in dense traffic.
A smart ultra-low power CMOS image sensor comprising an analog programmable processor array is reported. Compact and efficient motion detection algorithms are implemented to process sub-sampled images made of so-called macropixels. Only Regions of Interest (ROI) consisting of macropixels containing moving objects are read out. This drastically reduces power consumption: the 110×240 pixel image sensor fabricated in a 0.35μm technology features a power consumption of 120μW at 25fps.
Human-in-the-loop driving simulation aims to create the illusion of driving by stimulating the driver’s sensory systems in as realistic conditions as possible. However, driving simulators can only produce a subset of the sensory stimuli that would be available in a real driving situation, depending on the degree of refinement of their design. This subset must be carefully chosen because it is crucial for human acceptability. Our focus is the design of a physical dynamic (i.e., motion-based) motorcycle-riding simulator. For its instrumentation, we focused on the rider acceptability of all sub-systems and the simulator as a whole. The significance of our work lies in this particular approach; the acceptability of the riding illusion for the rider is critical for the validity of any results acquired using a simulator. In this article, we detail the design of the hardware/software architecture of our simulator under this constraint; sensors, actuators, and dataflows allow us to (1) capture the rider’s actions in real-time; (2) render the motorcycle’s behavior to the rider; and (3) measure and study rider/simulated motorcycle interactions. We believe our methodology could be adopted by future designers of motorcycle-riding simulators and other human-in-the-loop simulators to improve their rendering (including motion) quality and acceptability.
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