  This paper presents a driver-specific risk recognition framework for
autonomous vehicles that can extract inter-vehicle interactions. This
extraction is carried out for urban driving scenarios in a driver-cognitive
manner to improve the recognition accuracy of risky scenes. First, clustering
analysis is applied to the operation data of drivers for learning the
subjective assessment of risky scenes of different drivers and generating the
corresponding risk label for each scene. Second, the graph representation model
(GRM) is adopted to unify and construct the features of dynamic vehicles,
inter-vehicle interactions and static traffic markings in real driving scenes
into graphs. The driver-specific risk label provides ground truth to capture
the risk evaluation criteria of different drivers. In addition, the graph model
represents multiple features of the driving scenes. Therefore, the proposed
framework can learn the risk-evaluating pattern of driving scenes of different
drivers and establish driver-specific risk identifiers. Last, the performance
of the proposed framework is evaluated via experiments conducted using
real-world urban driving datasets collected by multiple drivers. The results
show that the risks and their levels in real driving environments can be
accurately recognized by the proposed framework.
