Look Before You Drive: Boosting Trajectory Forecasting via Imagining Future

Predicting the future trajectories of other agents in the scene fast and effectively is crucial for autonomous driving systems. We note that high-quality predictions require us to take into account the subjective initiative of the target agents, which is reflected by the fact that they themselves make decisions based on their own predictions about the future, just like our ego vehicle's prediction-planning system. However, this characteristic has been neglected in previous studies. We introduce Look Before You Drive (LBYD), a two-stage approach that explicitly incorporates both past observations and future estimates to make predictions. To get a preliminary estimate of the future, we propose a neat and effective baseline capable of making predictions for multiple agents simultaneously. We use only the most basic structures, mainly Transformer, to ensure sufficient inference speed and room for expansion. On this basis, we cooperatively train two networks to enable the coarse estimates to boost final forecasting. Our experiments demonstrate that LBYD can significantly surpass the baseline performance. Moreover, while state-of-the-art methods rely on considering heterogeneity and artificially designed inductive biases for attention modeling, LBYD performs on par with SOTA without them on both the Argoverse 1 and the large scale Argoverse 2 datasets, and can run at 67 FPS on an RTX 3090 GPU.

Authors

• Yixuan Fan
• Xin Liu
• Yali Li 0001
• Shengjin Wang

Keywords

• Training
• Scalability
• Predictive models
• Transformers
• Trajectory
• Proposals
• History
• Trajectory Forecasting
• Baseline Performance
• Two-stage Approach
• Future Trajectories
• Targeting Agents
• Inference Speed
• Inductive Bias
• Trajectories Of Agents
• Training Stage
• Historical Information
• Stage Model
• Unique Way
• Complex Scenarios
• Baseline Methods
• Corresponding Points
• Traffic Light
• Ablation Experiments
• Self-driving
• Training Objective
• Tokenized
• Task Planning
• Bird’s Eye
• Future Situations
• Trajectory Prediction
• Displacement Error
• Missing Rate
• Forward Propagation
• Target Vehicle
• Seconds Of Data