Path Assignment Techniques For Vehicle Tracking
This work addresses a specific challenge in vehicle tracking for driver assistance systems, but it is incremental as it builds on existing path estimation and lane assignment techniques.
The paper tackles the problem of assigning detected vehicles to the host vehicle's path for driver assistance systems by introducing two methods that filter data late to avoid delays, and demonstrates their performance using ROC analysis on experimental data.
Many driver assistance systems such as Adaptive Cruise Control require the identification of the closest vehicle that is in the host vehicle's path. This entails an assignment of detected vehicles to the host vehicle path or neighboring paths. After reviewing approaches to the estimation of the host vehicle path and lane assignment techniques we introduce two methods that are motivated by the rationale to filter measured data as late in the processing stages as possible in order to avoid delays and other artifacts of intermediate filters. These filters generate discrete posterior probability distributions from which a path or "lane" index is extracted by a median estimator. The relative performance of those methods is illustrated by a ROC using experimental data and labeled ground truth data.