论文标题
通过学习启发式和蒙特卡洛树搜索的自动化车辆的合作计划加速合作计划
Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search
论文作者
论文摘要
在城市交通情况下有效驾驶需要远见。观察其他交通参与者以及根据自己的行动推断他们可能的下一步行动被认为是合作的预测和计划。人类具有预测多个交互交流参与者的行动并相应计划的能力，而无需直接与他人沟通。先前的工作表明，无需明确的沟通即可实现有效的合作计划。但是，合作计划的搜索空间是如此之大，以至于大多数计算预算都用于探索远离解决方案的无宣传区域的搜索空间。为了加速计划过程，我们将学习的启发式方法与合作计划方法相结合，以指导搜索有前途的行动，以较低的计算成本产生更好的解决方案。
Efficient driving in urban traffic scenarios requires foresight. The observation of other traffic participants and the inference of their possible next actions depending on the own action is considered cooperative prediction and planning. Humans are well equipped with the capability to predict the actions of multiple interacting traffic participants and plan accordingly, without the need to directly communicate with others. Prior work has shown that it is possible to achieve effective cooperative planning without the need for explicit communication. However, the search space for cooperative plans is so large that most of the computational budget is spent on exploring the search space in unpromising regions that are far away from the solution. To accelerate the planning process, we combined learned heuristics with a cooperative planning method to guide the search towards regions with promising actions, yielding better solutions at lower computational costs.
