Deformable Radar Polygon: A Lightweight and Predictable Occupancy Representation for Short-range Collision Avoidance

Inferring the drivable area in a scene is a key capability for ensuring the vehicle avoids obstacles and enabling safe autonomous driving. However, a traditional occupancy grid map suffers from high memory consumption when forming a fine-resolution grid for a large map. In this paper, we propose a lightweight, accurate, and predictable occupancy representation for automotive radars working for short-range applications that take interest in instantaneous free space surrounding the sensor. This new occupancy format is a polygon composed of a bunch of vertices selected from noisy radar measurements, which covers free space inside and gives a Doppler moving velocity for each vertex. It not only takes a very small memory and computing resources for storage and updating at every timeslot but also has the predictable shape-change property based on vertex's Doppler velocity. We name this kind of occupancy representation 'deformable radar polygon'. Two formation algorithms for radar polygon are introduced for both single timeslot and continuous inverse sensor model update. To fit this new polygon representation, a matrix-form collision detection method has been modeled as well. The radar polygon algorithms and collision detection model have been validated via extensive experiments with real collected data and simulations, showing that the deformable radar polygon is very competitive in terms of its completeness, smoothness, accuracy, lightweight as well as the shape-predictable property. Our codes will be made available at https://github.com/Xiangyu-Gao/deformable_radar_polygon_occupancy_representation.