Intersections are the most complex and hazardous areas of the road network. Statistics show that accidents at intersection are mostly caused by driver error. Based on this we 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 a driver intends to do and what he is expected to do. 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 allows for a flexible and computationally efficient estimation of risk. 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.