Motion planning is a computational problem that finds a sequence of valid trajectories, often based on surrounding agents' forecasting, environmental understanding, and historical and future contexts. It can also be viewed as a game in which agents continuously plan their next move according to other agents' intentions and the encountering environment, further achieving their ultimate goals through incremental actions. To model the dynamic planning and interaction process, we propose a novel framework, DeepEMplanner, which takes the stepwise interaction into account for fine-grained behavior learning. The ego vehicle maximizes each step motion to reach its eventual driving outcome based on the stepwise expectation from agents and its upcoming road conditions. On the other hand, the agents also follow the same philosophy to maximize their stepwise behavior under the encountering environment and the expectations from ego and other agents. Our DeepEMplanner models the interactions among ego, agents, and the dynamic environment in an autoregressive manner by interleaving the Expectation and Maximization processes. Further, we design ego-to-agents, ego-to-map, and ego-to-BEV interaction mechanisms with hierarchical dynamic key objects attention to better model the interactions. Experiments on the nuScenes benchmark show that our approach achieves state-of-the-art results.

翻译：运动规划是一种计算问题，旨在根据周围代理的预测、环境理解以及历史与未来上下文，寻找一系列有效的轨迹。该问题也可被视作一场博弈：代理根据其他代理的意图及所处环境持续规划下一步行动，通过渐进式动作实现最终目标。为建模动态规划与交互过程，我们提出了一种新颖框架DeepEMplanner，该框架将逐步交互纳入细粒度行为学习。自车基于代理的逐步期望及前方道路条件，最大化每一步运动以达成最终驾驶结果。同时，其他代理也遵循相同理念，在所处环境及自车与其他代理的期望下最大化其逐步行为。我们的DeepEMplanner通过交错执行期望与最大化过程，以自回归方式建模自车、代理及动态环境间的交互。此外，我们设计了自车-代理、自车-地图及自车-鸟瞰视角的交互机制，并引入分层动态关键对象注意力以增强交互建模。在nuScenes基准上的实验表明，我们的方法取得了最先进的性能。