  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.
