Active safety systems that operate alongside a higher-level autonomy stack and perform autonomous braking require reliable obstacle detection within the vehicle’s motion corridor. Many runtime-constrained monitoring approaches simplify road geometry, producing blind spots on high-curvature roads where obstacles could not be detected. In this paper, we eliminate these blind spots by aligning the monitoring region with a locally framed, predicted motion corridor. We compute a predicted corridor boundary and filter incoming lidar point clouds in real time using an azimuth-sorted visible boundary and a moving window. In a case study, our method avoids curvature-induced blind spots while maintaining performance on straight segments.     «

Active safety systems that operate alongside a higher-level autonomy stack and perform autonomous braking require reliable obstacle detection within the vehicle’s motion corridor. Many runtime-constrained monitoring approaches simplify road geometry, producing blind spots on high-curvature roads where obstacles could not be detected. In this paper, we eliminate these blind spots by aligning the monitoring region with a locally framed, predicted motion corridor. We compute a predicted corridor bo...     »