Optimal Alarms for Vehicular Collision Detection

Recent advances in in-vehicle awareness have end uses such as messages or warnings to drivers, automated braking or control, or fully driverless vehicles. There are similarly many sensors and communication devices that can provide awareness, and many models of traffic motion or human action that add predictive power. As there are many possible approaches, a single unified framework for intelligent vehicle design seems unlikely in the near future. However, there are certain tasks that are important for a variety of intelligent vehicle applications and (relatively) independent of the individual sensors or models used. One such task is vehicular collision detection: given the current position and state of two or more vehicles and a predictive model for their future motion, determine whether there is a significant chance of collision between vehicles in the near future. This task may sound trivial and is indeed simpler than the problems of scene reconstruction, predictive modeling or path planning. This simplicity allows vehicular collision detection to be framed as a self-contained task, with solutions that compromise between speed and robustness. Collision detection closely matches the theoretical problem of optimal alarm design [1], [2]. Optimal alarms were initially studied in the context of detecting bankruptcies or machine part failures [3] - critical events that should be detected in advance with high probability, much like collisions.