Visibility is a crucial aspect of planning and control of autonomous vehicles (AV), particularly when navigating environments with occlusions. However, when an AV follows a trajectory with multiple occlusions, existing methods evaluate each occlusion individually, calculate a visibility cost for each, and rely on the planner to minimize the overall cost. This can result in conflicting priorities for the planner, as individual occlusion costs may appear to be in opposition. We solve this problem by creating an alternate perspective cost map that allows for an aggregate view of the occlusions in the environment. The value of each cell on the cost map is a measure of the amount of visual information that the vehicle can gain about the environment by visiting that location. Our proposed method identifies observation locations and occlusion targets drawn from both map data and sensor data. We show how to estimate an alternate perspective for each observation location and then combine all estimates into a single alternate perspective cost map for motion planning.

翻译：能见度是自动驾驶车辆（AV）规划与控制的关键要素，尤其在存在遮挡的环境中导航时尤为重要。然而，当自动驾驶车辆沿具有多重遮挡的轨迹行驶时，现有方法会对每个遮挡单独评估，计算各遮挡对应的能见度代价，并依赖规划器最小化总代价。这可能导致规划器面临优先级冲突，因为各遮挡代价看似相互矛盾。我们提出通过构建替代视角代价图解决此问题，该代价图可实现环境遮挡的聚合视图。代价图中每个单元格的值，衡量车辆访问该位置可获取的环境视觉信息量。所提方法能从地图数据和传感器数据中识别观测位置与遮挡目标。我们展示了如何为每个观测位置估计替代视角，并将所有估计值融合成单一替代视角代价图以用于运动规划。