Development of a performance-based approach for collision avoidance and mitigation

Abstract

Many threat assessment algorithms are based on a collection of threshold equations that predict when a collision is to occur. The fact that there are numerous algorithms suggests a need to understand the underlying principles behind the equation design and threshold settings. This thesis presents a methodology to develop appropriate alerting thresholds based on performance metrics. This also allows us to compare different alerting algorithms and evaluate alerting systems. The method is a performance-based approach in state-space. It can be used as a stand alone system for real-time implementation or a threshold design tool in conjunction with any chosen alerting algorithm or sensor system. Using carefully prescribed trajectory models (which may include uncertainties), the performance tradeoff with and without an alert can be predicted for different states along the course of an encounter situation. This information can then be used to set appropriate threshold values for the desired alerting logic. The development of the threshold criteria for a rear-end collision warning system is given as an example. Though the approach given is presented as a threshold design tool, the methodology is self-contained as a threat assessment logic. The possibility exists to compute the performance measures on-the-fly from which alerting decisions can be made directly. We demonstrate the methodology on Lincoln LS concept vehicle with a GPS-based system and a full-cab driving simulator as prototypes. Application examples, a collision mitigation by braking system and a face tracking warning system, are shown to handle the universality of the performance-based approach. For illustrative purposes, a vision-based system (post-processed off-line) is compared with the GPSbased system.