DeMo++: Motion Decoupling for Autonomous Driving

TL;DR

DeMo++ is a novel framework for autonomous driving that decouples motion prediction into intention and state components, incorporating cross-scene interactions and a hybrid Attention-Mamba model to improve trajectory modeling and planning.

Contribution

It introduces a decoupled motion estimation framework with cross-scene interactions and a hybrid Attention-Mamba architecture, advancing state-of-the-art in motion forecasting and planning.

Findings

Achieves state-of-the-art results on Argoverse 2 and nuScenes benchmarks.

Improves motion prediction accuracy and planning safety.

Demonstrates effective modeling of diverse motion intentions and trajectory evolution.

Abstract

Motion forecasting and planning are tasked with estimating the trajectories of traffic agents and the ego vehicle, respectively, to ensure the safety and efficiency of autonomous driving systems in dynamically changing environments. State-of-the-art methods typically adopt a one-query-one-trajectory paradigm, where each query corresponds to a unique trajectory for predicting multi-mode trajectories. While this paradigm can produce diverse motion intentions, it often falls short in modeling the intricate spatiotemporal evolution of trajectories, which can lead to collisions or suboptimal outcomes. To overcome this limitation, we propose DeMo++, a framework that decouples motion estimation into two distinct components: holistic motion intentions to capture the diverse potential directions of movement, and fine spatiotemporal states to track the agent's dynamic progress within the scene and enable a self-refinement capability. Further, we introduce a cross-scene trajectory interaction mechanism to explore the relationships between motions in adjacent scenes. This allows DeMo++ to comprehensively model both the diversity of motion intentions and the spatiotemporal evolution of each trajectory. To effectively implement this framework, we developed a hybrid model combining Attention and Mamba. This architecture leverages the strengths of both mechanisms for efficient scene information aggregation and precise trajectory state sequence modeling. Extensive experiments demonstrate that DeMo++ achieves state-of-the-art performance across various benchmarks, including motion forecasting (Argoverse 2 and nuScenes), motion planning (nuPlan), and end-to-end planning (NAVSIM).