Learning Cooperative Trajectory Representations for Motion Forecasting (2311.00371v2)

Abstract: Motion forecasting is an essential task for autonomous driving, and utilizing information from infrastructure and other vehicles can enhance forecasting capabilities. Existing research mainly focuses on leveraging single-frame cooperative information to enhance the limited perception capability of the ego vehicle, while underutilizing the motion and interaction context of traffic participants observed from cooperative devices. In this paper, we propose a forecasting-oriented representation paradigm to utilize motion and interaction features from cooperative information. Specifically, we present V2X-Graph, a representative framework to achieve interpretable and end-to-end trajectory feature fusion for cooperative motion forecasting. V2X-Graph is evaluated on V2X-Seq in vehicle-to-infrastructure (V2I) scenarios. To further evaluate on vehicle-to-everything (V2X) scenario, we construct the first real-world V2X motion forecasting dataset V2X-Traj, which contains multiple autonomous vehicles and infrastructure in every scenario. Experimental results on both V2X-Seq and V2X-Traj show the advantage of our method. We hope both V2X-Graph and V2X-Traj will benefit the further development of cooperative motion forecasting. Find the project at https://github.com/AIR-THU/V2X-Graph.

• The paper introduces V2X-Graph, a graph neural network framework that fuses cooperative motion and interaction data for enhanced motion forecasting.
• It leverages interpretable modules like IA, MFG, ALG, and CIG to integrate multi-view spatial-temporal features and reduce forecasting errors.
• Experiments on V2X-Seq and V2X-Traj demonstrate significant improvements in long-range trajectory prediction over traditional methods.

Learning Cooperative Trajectory Representations for Motion Forecasting

The paper "Learning Cooperative Trajectory Representations for Motion Forecasting" proposes a novel approach to enhance motion forecasting in autonomous driving by leveraging cooperative information from multiple sources such as vehicles and infrastructure. The proposed framework, V2X-Graph, introduces an innovative paradigm for learning cooperative trajectory representations.

Summary of Contributions

This research work addresses the limitations of existing motion forecasting methods, which primarily rely on single-frame cooperative information and lack comprehensive utilization of cooperative motion and interaction contexts. The authors present V2X-Graph as an interpretable, end-to-end learning framework designed to fully harness cooperative motion forecasting data. The framework employs graph-based models to represent and learn from cooperative trajectories and interactions among traffic participants.

To demonstrate the effectiveness of V2X-Graph, the authors evaluate it on a vehicle-to-infrastructure (V2I) motion forecasting dataset, V2X-Seq, and also construct a new dataset, V2X-Traj, which extends the evaluation to the vehicle-to-everything (V2X) scenario. The results highlight significant improvements in motion forecasting performance, notably in long-range trajectory predictions.

Methodological Insights

V2X-Graph leverages a graph neural network (GNN) architecture to encode both node and edge features within cooperative scenarios. Nodes in the graph represent the motion and spatial-temporal features of trajectories, while edges encode the relative spatial-temporal and compact spatial information between trajectories and map elements. This approach allows for the aggregation and propagation of features across different views and cooperative sources.

The framework introduces several components within the graph for feature fusion:

1. Interpretable Association (IA): Establishes explicit associations between trajectories of the same agent across different views, aiding in representation learning and reducing error propagation.
2. Motion Fusion subGraph (MFG): Represents cooperative motion features by integrating explicit associations with implicit spatial-temporal encodings.
3. Agent-Lane subGraph (ALG): Fuses motion features with lane segment interaction features, leveraging the spatial constraints in traffic environments.
4. Cooperative Interaction subGraph (CIG): Facilitates dense interaction modeling by integrating features among distinct agents from multiple views.

Experimental Findings

The experimental results reveal several key findings:

• In V2X scenarios, V2X-Graph significantly outperforms traditional methods like TNT and HiVT when equipped with cooperative information. It achieves marked reductions in metrics such as minADE, minFDE, and MR, which are indicative of enhanced accuracy and reliability in motion forecasting.
• The research highlights the substantial benefits of incorporating cooperative data, with notable improvements observed in predicting vehicle trajectories over longer horizons.
• Detailed ablation studies underscore the effectiveness of each component in the interpretable graph, showing that the integration of motion and interaction features from multiple views is crucial for the observed performance gains.

Implications and Future Directions

The implications of this research are profound for the development of autonomous driving systems. By framing motion forecasting as a cooperative problem and employing graph-based learning, V2X-Graph presents a pathway to more accurate and reliable predictions, crucial for safe autonomous driving.

The introduction of the V2X-Traj dataset further broadens the scope of research, emphasizing the importance of real-world data in refining motion forecasting models. As autonomous systems increasingly rely on cooperative communication standards like V2X, the techniques developed in this paper could be pivotal in advancing the capabilities of these systems.

Future research could explore the integration of sensor data and appearance features to enhance agent associations and feature learning. Additionally, expanding the framework to accommodate other forms of sensor modalities and exploring scalability in diverse traffic scenarios could be promising directions for further investigation.