Planning is complicated by the combination of perception and map information, particularly when driving in heavy traffic. Developing an extendable and efficient representation that visualizes sensor noise and provides constraints to real-time planning tasks is desirable. We aim to develop an extendable map representation offering prior to cost in planning tasks to simplify the planning process of dealing with complex driving scenarios and visualize sensor noise. In this paper, we illustrate a unified context representation empowered by a modern deep learning motion prediction model, representing statistical cognition of motion prediction for human beings. A sampling-based planner is adopted to train and compare the difference in risk map generation methods. The training tools and model structures are investigated illustrating their efficiency in this task.

翻译：规划任务因感知信息与地图信息的结合而变得复杂，尤其在交通密集的驾驶场景中。开发一种可扩展且高效的表征方法，既能可视化传感器噪声，又能为实时规划任务提供约束，具有重要价值。本文旨在构建一种可扩展的地图表征，为规划任务提供先验代价函数，从而简化处理复杂驾驶场景的规划流程，并实现传感器噪声的可视化。本文提出一种由现代深度学习运动预测模型驱动的统一场景表征方法，该表征体现了人类运动预测的统计认知特性。研究采用基于采样的规划器对风险地图生成方法进行训练与比较，并通过训练工具与模型结构的分析验证了该方法在此任务中的有效性。