Complexity reduction using the Random Forest classifier in a collision detection algorithm

Advanced proactive safety applications are considered a promising approach to increase the effectiveness of already highly optimized vehicular safety systems. Detecting an unavoidable crash situation before the actual collision is of utmost importance and requires an effective real-time implementation. In this paper a collision detection algorithm based on the curvilinear-motion model for trajectory estimation is presented. The algorithm takes into account the EGO-vehicle's driving state and the high-level representation of surrounding objects. Next the presented approach is evaluated from a real-time perspective by applying static code analysis to a reference implementation of the algorithm. The results suggest the application of further optimization techniques as the computational complexity does not allow an effective real-time behavior. In order to guarantee both real-time constraints and effective collision detection a novel method for the preselection of potential collision opponents based on the Random Forest classifier is employed. The combination of efficient preselection and the proposed collision detection algorithm leads to a highly effective context interpretation that does not neglect the tight economic constraints.