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.

提出一种新颖的面向智能驾驶系统中的多模态运动预测的代理中心模型，并结合锚定地标、融合多样化提案的方法，实现了未来轨迹的精准预测，网络结构均相并且简明，可靠且高效。实验证明，该代理中心无论从预测精度还是从情境级别推断延迟方面均优于当前最先进的方法。