Risk-Aware Vehicle Trajectory Prediction Under Safety-Critical Scenarios
<|reference_start|>Risk-Aware Vehicle Trajectory Prediction Under Safety-Critical Scenarios: Trajectory prediction is significant for intelligent vehicles to achieve high-level autonomous driving, and a lot of relevant research achievements have been made recently. Despite the rapid development, most existing studies solely focused on normal safe scenarios while largely neglecting safety-critical scenarios, particularly those involving imminent collisions. This oversight may result in autonomous vehicles lacking the essential predictive ability in such situations, posing a significant threat to safety. To tackle these, this paper proposes a risk-aware trajectory prediction framework tailored to safety-critical scenarios. Leveraging distinctive hazardous features, we develop three core risk-aware components. First, we introduce a risk-incorporated scene encoder, which augments conventional encoders with quantitative risk information to achieve risk-aware encoding of hazardous scene contexts. Next, we incorporate endpoint-risk-combined intention queries as prediction priors in the decoder to ensure that the predicted multimodal trajectories cover both various spatial intentions and risk levels. Lastly, an auxiliary risk prediction task is implemented for the ultimate risk-aware prediction. Furthermore, to support model training and performance evaluation, we introduce a safety-critical trajectory prediction dataset and tailored evaluation metrics. We conduct comprehensive evaluations and compare our model with several SOTA models. Results demonstrate the superior performance of our model, with a significant improvement in most metrics. This prediction advancement enables autonomous vehicles to execute correct collision avoidance maneuvers under safety-critical scenarios, eventually enhancing road traffic safety.<|reference_end|>
@article{wang2024risk-aware,
    title={Risk-Aware Vehicle Trajectory Prediction Under Safety-Critical Scenarios},
    author={Qingfan Wang, Dongyang Xu, Gaoyuan Kuang, Chen Lv, Shengbo Eben Li,
  Bingbing Nie},
    journal={arXiv preprint arXiv:2407.13480},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13480},
    primaryClass={cs.RO cs.AI}
}