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Driver reaction is of vital importance in risk scenarios. Drivers can takecorrect evasive maneuver at proper cushion time to avoid the potential trafficcrashes, but this reaction process is highly experience-dependent and requiresvarious levels of driving skills. To improve driving safety and avoid thetraffic accidents, it is necessary to provide all road drivers with on-boarddriving assistance. This study explores the plausibility of case-basedreasoning (CBR) as the inference paradigm underlying the choice of personalizedcrash evasive maneuvers and the cushion time, by leveraging the wealthy ofhuman driving experience from the steady stream of traffic cases, which havebeen rarely explored in previous studies. To this end, in this paper, wepropose an open evolving framework for generating personalized on-board drivingassistance. In particular, we present the FFMTE model with high performance tomodel the traffic events and build the case database; A tailored CBR-basedmethod is then proposed to retrieve, reuse and revise the existing cases togenerate the assistance. We take the 100-Car Naturalistic Driving Study datasetas an example to build and test our framework; the experiments show reasonableresults, providing the drivers with valuable evasive information to avoid thepotential crashes in different scenarios.

An Open Case-based Reasoning Framework for Personalized On-board Driving Assistance in Risk Scenarios