Safer Radar Motion by Scrutinizing Critical Velocity Estimates

The application of radar sensors for motion estimation has recently been discussed in the research community. However, challenging environments such as garages and tunnels can lead to erroneous motion estimation results. Since the estimate serves as an input for trajectory control in automated driving functions, it can pose a safety hazard, e.g., if undesired acceleration is applied. This work presents a framework to address unsafe controller actions caused by motion estimation failures. A monitoring module continuously observes controller actions and feeds back critically evaluated measures to the motion estimation module. We propose three additional algorithms that adapt both the estimation module and its input data-namely, the filtered radar point cloud. First, a method that incorporates previous motion states into the point cloud filtering process. Second, a ridge regression algorithm that generates alternative estimates and compensates for erroneous conclusions arising from an unfavorably selected point set. Third, an adapted cluster selection approach that increases the number of true positive detection points. Experiments show that critical estimates can be identified in most cases, with a success rate of 97.7%. Additionally, we found that the estimation process becomes significantly more robust. In summary, this work emphasizes the importance of safe motion state measurement for the operation of automated vehicles. It introduces a method for determining the criticality of motion states and presents approaches to mitigate erroneous estimations.