  An important application of intelligent vehicles is advance detection of
dangerous events such as collisions. This problem is framed as a problem of
optimal alarm choice given predictive models for vehicle location and motion.
Techniques for real-time collision detection are surveyed and grouped into
three classes: random Monte Carlo sampling, faster deterministic
approximations, and machine learning models trained by simulation. Theoretical
guarantees on the performance of these collision detection techniques are
provided where possible, and empirical analysis is provided for two example
scenarios. Results validate Monte Carlo sampling as a robust solution despite
its simplicity.
