  Driver reaction is of vital importance in risk scenarios. Drivers can take
correct evasive maneuver at proper cushion time to avoid the potential traffic
crashes, but this reaction process is highly experience-dependent and requires
various levels of driving skills. To improve driving safety and avoid the
traffic accidents, it is necessary to provide all road drivers with on-board
driving assistance. This study explores the plausibility of case-based
reasoning (CBR) as the inference paradigm underlying the choice of personalized
crash evasive maneuvers and the cushion time, by leveraging the wealthy of
human driving experience from the steady stream of traffic cases, which have
been rarely explored in previous studies. To this end, in this paper, we
propose an open evolving framework for generating personalized on-board driving
assistance. In particular, we present the FFMTE model with high performance to
model the traffic events and build the case database; A tailored CBR-based
method is then proposed to retrieve, reuse and revise the existing cases to
generate the assistance. We take the 100-Car Naturalistic Driving Study dataset
as an example to build and test our framework; the experiments show reasonable
results, providing the drivers with valuable evasive information to avoid the
potential crashes in different scenarios.
