A rear-end collision warning system for drivers with support vector machines

The paper proposes a rear-end collision warning system for drivers, where how to evaluate the risk of collision is critical. In the proposed system, the time-to-collision assuming a constant relative acceleration is employed as a subjective index of risk based on the collected data in an unconstrained drive and the discussion from a theory of visual control. The system produces an alert when it detects anomaly of the index using the one-class support vector machine, which is trained with the aforementioned data. Some cross-validation experiments show the effectiveness of the system.