A framework for mode classification in multimodal environments using radar-based sensors

Monitoring traffic at locations where bicyclists and pedestrians are present and studying their interactions with motorized vehicles has the potential to reveal underlying crash mechanisms and, in turn, guide the implementation of suitable countermeasures. Previous research has demonstrated the capabilities of vision-based systems in traffic monitoring; however, as these systems rely on video camera data, they remain constrained in adverse lighting and weather conditions. Although radar-based sensors can be used in traffic monitoring, they have not been tested in multimodal environments. To bridge this gap a novel framework to classify trajectories recorded by radar-based sensors in multimodal traffic environments is developed. The Support Vector Machine is used as the classifier and the following aspects allow to develop a robust, flexible, and transferable classification framework that can be applied in various traffic scenes. The SVM employs (1) a speed and length or speed and acceleration feature vector, (2) trajectory normalization scheme (i.e., select multiple measurements per trajectory), (3) training sample balancing strategy, and (4) cross-validation strategy. The framework is tested and validated using data from two different multimodal intersections. The results suggest that the mode type for every trajectory can be predicted with 95% accuracy using ten speed measurements and the vehicle's mean length while accounting for the unequal number of observations per class. While these results are subject to change, i.e., both SVM's input (feature vector and/or balancing approach) and performance may vary across different traffic scenes, the proposed framework is transferable and flexible.