MLB-Traj: Map-Free Trajectory Prediction With Local Behavior Query for Autonomous Driving

Abstract

Predicting future motions of target agents is crucial to ensuring the safety of autonomous vehicles in Internet of Things environments. Although significant progress has been made in this field, most mainstream approaches rely heavily on high-definition (HD) maps, which may not always be available or accurate owing to the high costs of map construction and the potential localization errors. Without the explicit guidance of HD maps, trajectory prediction would become more challenging. To address this challenge, we present MLB-Traj, an innovative framework for map-free motion prediction based on local behavior queries. MLB-Traj leverages the observation that agents often follow local behavior patterns in specific traffic scenarios, where these local behaviors reveal the potential trajectories of the targets and contain scenario-consistent information. It starts with a hierarchical dynamic modal query paradigm that first captures the scene’s general modal characteristics and then models target-specific properties. A dual Transformer query mechanism aggregates multiscale relationships to facilitate this process. To tackle potential inconsistency in map-free forecasting, we introduce a trajectory consistency module. It ensures the continuity of inferred trajectories by utilizing patch-wise interaction representations to capture local temporal dependencies, while also learning more robust representations by simulating the model’s response to spatial inconsistency in its predictions. Extensive experiments conducted on real-world datasets validate the effectiveness of MLB-Traj. The results indicate that our framework outperforms existing methods, highlighting its superiority in generating accurate predictions in map-free settings.