Streaming Motion Forecasting for Autonomous Driving

Trajectory forecasting is a widely-studied problem for autonomous navigation. However, existing benchmarks evaluate forecasting based on independent snapshots of trajectories, which are not representative of real-world applications that operate on a continuous stream of data. To bridge this gap, we introduce a benchmark that continuously queries future trajectories on streaming data and we refer to it as “streaming forecasting. ” Our benchmark inherently captures the disappearance and re-appearance of agents, presenting the emergent challenge of forecasting for occluded agents, which is a safetycritical problem yet overlooked by snapshot-based benchmarks. Moreover, forecasting in the context of continuous timestamps naturally asks for temporal coherence between predictions from adjacent timestamps. Based on this benchmark, we further provide solutions and analysis for streaming forecasting. We propose a plug-and-play meta-algorithm called “Predictive Streamer” that can adapt any snapshot-based forecaster into a streaming forecaster. Our algorithm estimates the states of occluded agents by propagating their positions with multi-modal trajectories, and leverages differentiable filters to ensure temporal consistency. Both occlusion reasoning and temporal coherence strategies significantly improve forecasting quality, resulting in 25% smaller endpoint errors for occluded agents and 10-20% smaller fluctuations of trajectories. Our work is intended to generate interest within the community by highlighting the importance of addressing motion forecasting in its intrinsic streaming setting. Code is available at https://github.com/ziqipang/StreamingForecasting.

Authors

• Ziqi Pang
• Deva Ramanan
• Mengtian Li
• Yu-Xiong Wang

Keywords

• Fluctuations
• Codes
• Coherence
• Benchmark testing
• Filtering algorithms
• Prediction algorithms
• Cognition
• Autonomous Vehicles
• Motion Forecasting
• Benchmark
• Data Streams
• Future Trajectories
• Temporal Coherence
• Differential Filter
• Trajectory Snapshots
• Continuous Stream Of Data
• Neural Network
• Training Set
• Weight Decay
• Multilayer Perceptron
• Kalman Filter
• Forecasting Model
• Tracking Data
• Hidden State
• Types Of Agents
• Graph Neural Networks
• Targeting Agents
• Trajectories Of Agents
• 3D Perception
• 3D Tracking
• Tracking Dataset
• Bayesian Filtering
• Trajectory Prediction
• Temporal Continuity
• Forecasting Results
• Position Of Agent
• Minimum Displacement