Intersections are the most complex and hazardous areas of the road network, and 89% of accidents at intersection are caused by driver error. We focus on these accidents and propose a novel approach to risk assessment: in this work dangerous situations are identified by detecting conflicts between intention and expectation, i.e. between what drivers intend to do and what is expected of them. Our approach is formulated as a Bayesian inference problem where intention and expectation are estimated jointly for the vehicles converging to the same intersection. This work exploits the sharing of information between vehicles using V2V wireless communication links. The proposed solution was validated by field experiments using passenger vehicles. Results show the importance of taking into account interactions between vehicles when modeling intersection situations.
['Stephanie Lefevre', 'Christian Laugier', 'Javier Ibanez-Guzman']
Risk assessment at road intersections: Comparing intention and expectation