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.

To safely navigate in various complex traffic scenarios, autonomous driving systems are generally equipped with a motion forecasting module to provide vital information for the downstream planning module. For the real-world onboard applications, both accuracy and latency of a motion forecasting model are essential. In this report, we present an effective and efficient solution, which ranks the 3rd place [1] in the Argoverse 2 Motion Forecasting Challenge 2022.

Predictions of time-series are widely used in different disciplines. We propose CoR, Sparse Gaussian Conditional Random Fields (SGCRF) on top of Recurrent Neural Networks (RNN), for problems of this kind. CoR gains advantages from both RNN and SGCRF. It can not only effectively represent the temporal correlations in observed data, but can also learn the structured information of the output. CoR is challenging to train because it is a hybrid of deep neural networks and densely-connected graphical models. Alternative training can be a tractable way to train CoR, and furthermore, an end-to-end training method is proposed to train CoR more efficiently. CoR is evaluated by both synthetic data and real-world data, and it shows a significant improvement in performance over state-of-the-art methods.