A Multi-Task Framework for Vehicle Trajectory and Disturbance Classification in Autonomous Driving Scenarios

Abstract

Systematic scenario classification supports the characterization of test conditions in simulation-based autonomous driving validation. This paper proposes a multi-task framework that combines OpenSCENARIO text embeddings with Ego-vehicle motion representations learned by Autoencoders trained on the SinD real-world dataset. The framework jointly predicts trajectory type and binary perception and decision disturbances, defined using environmental conditions and collision occurrence, respectively. We evaluate 24 combinations of four text encoders and six trajectory Autoencoders under fixed and uncertainty-based task weighting. On generated test scenarios with modified temporal and environmental parameters, Qwen3-Embedding-0.6B combined with Mamba-AE achieves the highest Full Match Accuracy of 91.61%, requiring all three labels to be predicted correctly, and trajectory accuracy of 99.97% under fixed weighting. Perception classification limits joint performance, and high validation accuracy does not consistently extend to the modified test conditions. Uncertainty-based weighting provides no consistent improvement, while latent-vector ablations show task-dependent contributions from text and motion representations. These findings support offline classification within the evaluated scenario configurations while identifying limitations in robustness to parameter changes.