High-definition maps (HD maps) are a key component of most modern self-driving systems due to their valuable semantic and geometric information. Unfortunately, building HD maps has proven hard to scale due to their cost as well as the requirements they impose in the localization system that has to work everywhere with centimeter-level accuracy. Being able to drive without an HD map would be very beneficial to scale self-driving solutions as well as to increase the failure tolerance of existing ones (e.g., if localization fails or the map is not up-to-date). Towards this goal, we propose MP3, an end-to-end approach to mapless driving where the input is raw sensor data and a high-level command (e.g., turn left at the intersection). MP3 predicts intermediate representations in the form of an online map and the current and future state of dynamic agents, and exploits them in a novel neural motion planner to make interpretable decisions taking into account uncertainty. We show that our approach is significantly safer, more comfortable, and can follow commands better than the baselines in challenging long-term closed-loop simulations, as well as when compared to an expert driver in a large-scale real-world dataset.
翻译:高清晰地图(HD地图)是大多数现代自我驱动系统的关键组成部分,因为其具有宝贵的语义和几何信息。 不幸的是,建造HD地图由于成本和在本地化系统中必须做到的准确度达到千米水平,因此很难进行规模化。在没有HD地图的情况下进行驾驶将非常有利于规模化自我驱动解决方案,以及提高现有系统(例如,如果本地化失败或地图不更新)的不耐失力容忍度。 为实现这一目标,我们提议MP3,在输入为原始传感器数据和高级指令(例如,转左转十字路口)的地方,对无映射驱动采用最终到终端的方法。 MP3预测以在线地图的形式进行中间表达,以及动态剂目前和未来的状况,并在考虑到不确定性的情况下,利用它们来做出可解释的决定。 我们表明,我们的方法比具有挑战性的长期闭路面模拟的基线更安全、更舒适,可以跟踪命令。 与专家相比,在具有挑战性的长期闭路面模拟中,可以跟踪数据。