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

DeepEMplanner: An End-to-End EM Motion Planner with Iterative Interactions