A generic video and radar data fusion system for improved target selection

Abstract

This paper presents an automotive video and radar data fusion framework that can be used as a preliminary stage of an automatic cruise control or collision mitigation by braking system. The fusion framework finds the optimal assignment of radar and camera target reports and provides improved state estimates for the fused targets. A sophisticated critical path selection is presented and used in the critical target selection module that aims to select the most relevant target. This module is capable of identifying targets that cut into the ego lane or cut out from the ego lane and incorporate that into the final target selection. The selected target is then compared to a state of the art algorithm within the radar sensor. Additional test drives were made to evaluate the performance of the new algorithm. Due to its low computational effort and the sensor independent design the presented algorithm is suitable to be used in the automotive embedded environment.