Details

Autor(en) / Beteiligte

• Wang, Xishun
• Su, Tong
• Da, Fang
• Yang, Xiaodong

Titel

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

Ist Teil von

• 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, p.21995-22003

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.