Inferring the drivable area in a scene is crucial for ensuring a vehicle avoids obstacles and facilitates safe autonomous driving. In this paper, we concentrate on detecting the instantaneous free space surrounding the ego vehicle, targeting short-range automotive applications. We introduce a novel polygon-based occupancy representation, where the interior signifies free space, and the exterior represents undrivable areas for the ego-vehicle. The radar polygon consists of vertices selected from point cloud measurements provided by radars, with each vertex incorporating Doppler velocity information from automotive radars. This information indicates the movement of the vertex along the radial direction. This characteristic allows for the prediction of the shape of future radar polygons, leading to its designation as a ``deformable radar polygon". We propose two approaches to leverage noisy radar measurements for producing accurate and smooth radar polygons. The first approach is a basic radar polygon formation algorithm, which independently selects polygon vertices for each frame, using SNR-based evidence for vertex fitness verification. The second approach is the radar polygon update algorithm, which employs a probabilistic and tracking-based mechanism to update the radar polygon over time, further enhancing accuracy and smoothness. To accommodate the unique radar polygon format, we also designed a collision detection method for short-range applications. Through extensive experiments and analysis on both a self-collected dataset and the open-source RadarScenes dataset, we demonstrate that our radar polygon algorithms achieve significantly higher IoU-gt and IoU-smooth values compared to other occupancy detection baselines, highlighting their accuracy and smoothness.

翻译：推断场景中的可行驶区域对于确保车辆避开障碍物并促进安全自动驾驶至关重要。本文聚焦于检测自车周围的瞬时自由空间，针对短程汽车应用。我们提出了一种新颖的基于多边形的占据表示方法，其中内部表示自由空间，外部表示自车不可行驶区域。雷达多边形由从雷达提供的点云测量中选择的顶点构成，每个顶点融合了汽车雷达的多普勒速度信息。该信息指示了顶点沿径向的运动。这一特性使得预测未来雷达多边形的形状成为可能，因此将其命名为"可变形雷达多边形"。我们提出了两种方法来利用含噪声的雷达测量生成准确且平滑的雷达多边形。第一种方法是基础雷达多边形生成算法，该算法独立地为每帧选择多边形顶点，并使用基于信噪比的证据进行顶点适配性验证。第二种方法是雷达多边形更新算法，采用基于概率和跟踪的机制随时间更新雷达多边形，从而进一步提升准确性和平滑度。为适应独特的雷达多边形格式，我们还设计了一种适用于短程应用的碰撞检测方法。通过在自采集数据集和开源RadarScenes数据集上进行大量实验与分析，我们证明相较于其他占据检测基线方法，我们的雷达多边形算法实现了显著更高的IoU-gt和IoU-smooth值，突显了其准确性与平滑性优势。