Optimal Alarms for Vehicular Collision Detection
This addresses collision detection for intelligent vehicles, but it appears incremental as it surveys and validates existing techniques.
The paper tackles the problem of detecting vehicular collisions by framing it as an optimal alarm choice using predictive models, and finds that Monte Carlo sampling is a robust solution based on empirical analysis of two scenarios.
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.