  Real-time safety systems are crucial components of intelligent vehicles. This
paper introduces a prediction-based collision risk assessment approach on
highways. Given a point mass vehicle dynamics system, a stochastic forward
reachable set considering two-dimensional motion with vehicle state probability
distributions is firstly established. We then develop an acceleration
prediction model, which provides multi-modal probabilistic acceleration
distributions to propagate vehicle states. The collision probability is
calculated by summing up the probabilities of the states where two vehicles
spatially overlap. Simulation results show that the prediction model has
superior performance in terms of vehicle motion position errors, and the
proposed collision detection approach is agile and effective to identify the
collision in cut-in crash events.
