Streaming Real-Time Trajectory Prediction Using Endpoint-Aware Modeling
Alexander Prutsch, David Schinagl, Horst Possegger

TL;DR
This paper introduces a lightweight, endpoint-aware streaming trajectory prediction method for autonomous vehicles that leverages previous predictions to improve accuracy and reduce latency in real-time scenarios.
Contribution
It proposes a novel endpoint-aware modeling scheme that efficiently incorporates temporal context from previous forecasts without multi-stage refinement.
Findings
Achieves state-of-the-art results on Argoverse 2 benchmarks.
Reduces inference latency compared to multi-stage methods.
Requires fewer computational resources.
Abstract
Future trajectories of neighboring traffic agents have a significant influence on the path planning and decision-making of autonomous vehicles. While trajectory forecasting is a well-studied field, research mainly focuses on snapshot-based prediction, where each scenario is treated independently of its global temporal context. However, real-world autonomous driving systems need to operate in a continuous setting, requiring real-time processing of data streams with low latency and consistent predictions over successive timesteps. We leverage this continuous setting to propose a lightweight yet highly accurate streaming-based trajectory forecasting approach. We integrate valuable information from previous predictions with a novel endpoint-aware modeling scheme. Our temporal context propagation uses the trajectory endpoints of the previous forecasts as anchors to extract targeted scenario…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Traffic Prediction and Management Techniques · Traffic control and management
