ProphNet：基于锚点告知提议的高效以智能体为中心的运动预测
ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals
AI总结：
本文提出ProphNet，一种基于锚点告知提议的以智能体为中心的运动预测模型，通过模态无关策略统一编码输入并融合目标导向锚点生成多模态轨迹，在保持高预测精度的同时实现了场景中心级别的低推理延迟。
摘要：
运动预测是自动驾驶系统的关键模块。由于多源输入的异构性、智能体行为的多模态性以及车载部署要求的低延迟，该任务极具挑战。为应对这些困难，本文提出了一种新颖的以智能体为中心的模型，利用锚点告知提议进行高效的多模态运动预测。我们设计了一种模态无关的策略，以统一方式简洁地编码复杂输入。我们生成多样化的提议，并将其与承载目标导向场景上下文的锚点融合，以诱导覆盖广泛未来轨迹的多模态预测。我们的网络架构高度统一且简洁，从而形成适合真实世界驾驶部署的高效模型。实验表明，我们的以智能体为中心的网络在预测精度上优于现有最先进方法，同时达到了以场景为中心级别的推理延迟。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
