Evaluation of different approaches for road course estimation using imaging radar

This work presents three imaging radar sensor approaches to estimate road courses, needed by intelligent vehicle systems such as active cruise control or collision avoidance. Two of the approaches use gridmap data. A gridmap integrates each measurement in a chronological order. The third approach analyzes moving objects ahead of the ego vehicle. One approach has been published previously, the other two are new. A range estimation is necessary on country roads and completes each approach. All approaches are evaluated using a huge dataset of country roads. The driven trajectory is taken as ground truth for the evaluation. The advantages and disadvantages are determined for each approach. The results show the new approach based on gridmap data performs up to 78% better than the known one. The other new approach using moving objects as input information yields estimations which are about three times more accurate than the ones from the known approach.