Edge-deployed streaming real-time trajectory prediction

Future trajectories of neighboring traffic agents heavily influence the path planning and decision-making of autonomous vehicles. While traditional trajectory forecasting focuses on snapshot-based predictions that treat scenarios independently, real-world deployment demands continuous, real-time data stream processing with low latency and temporal consistency. To address these requirements, we introduce a lightweight, streaming-based trajectory forecasting framework that propagates temporal context across successive timesteps. By utilizing the predicted endpoints of previous forecasts as structural anchors, our approach efficiently guides a scene encoder to extract highly targeted context encodings without requiring multi-stage refinement or segment-wise decoding. To ensure deterministic execution bounds on real-world robotics hardware, we enforce static token boundaries via proximity-based sampling to satisfy edge-compilation constraints. Evaluated on the Argoverse 2 multi-agent and single-agent benchmarks, our method achieves state-of-the-art streaming accuracy while requiring fewer computational resources than snapshot baselines. Furthermore, hardware deployment tests on an embedded NVIDIA Jetson Orin Nano platform demonstrate that our model maintains an inference frequency exceeding 10 Hz while preserving a highly favorable latency-accuracy trade-off, proving its viability for deterministic, low-power, real-time execution in dense, continuous traffic environments.