论文标题
可变形的雷达多边形：用于避免短程碰撞的轻巧且可预测的占用率表示
Deformable Radar Polygon: A Lightweight and Predictable Occupancy Representation for Short-range Collision Avoidance
论文作者
论文摘要
在场景中推断可驱动的区域对于确保车辆避免障碍并促进安全的自动驾驶至关重要。在本文中，我们集中精力检测自我车辆周围的瞬时自由空间，以短距离汽车应用为目标。我们引入了一种新型的基于多边形的占用表示形式，其中内部表示自由空间，外部代表自我车辆的不可分割的区域。雷达多边形由从雷达提供的点云测量值中选择的顶点组成，每个顶点都包含来自汽车雷达的多普勒速度信息。该信息表明顶点沿径向方向运动。 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.第二种方法是雷达多边形更新算法，它采用了基于概率和跟踪的机制来更新雷达多边形，进一步增强了准确性和平滑度，以适应唯一的雷达多边形格式，我们还设计了一个碰撞范围的范围，以便通过大量范围进行启用。与其他占用检测基线相比，雷达多边形算法的IOU-GT和IOU平滑值明显更高，突出了它们的准确性和光滑度。
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.
