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arXiv · 2303.12071

ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals

Xishun Wang·Tong Su·Fang Da·Xiaodong Yang
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Abstract

Motion forecasting is a key module in an autonomous driving system. Due to the heterogeneous nature of multi-sourced input, multimodality in agent behavior, and low latency required by onboard deployment, this task is notoriously challenging. To cope with these difficulties, this paper proposes a novel agent-centric model with anchor-informed proposals for efficient multimodal motion prediction. We design a modality-agnostic strategy to concisely encode the complex input in a unified manner. We generate diverse proposals, fused with anchors bearing goal-oriented scene context, to induce multimodal prediction that covers a wide range of future trajectories. Our network architecture is highly uniform and succinct, leading to an efficient model amenable for real-world driving deployment. Experiments reveal that our agent-centric network compares favorably with the state-of-the-art methods in prediction accuracy, while achieving scene-centric level inference latency.

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Xishun Wang, Tong Su, Fang Da, Xiaodong Yang. 2023-03-21. ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals. https://arxiv.org/abs/2303.12071

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