The field of autonomous driving and human behavior understanding is rapidly advancing, with a focus on developing more accurate and efficient prediction models. Recent research has explored the use of self-supervised learning, spatial-temporal risk-attentive frameworks, and multimodal benchmarking to improve the performance of autonomous vehicles in complex scenarios. Notably, the integration of intention guidance and reward heuristics has shown promising results in enhancing trajectory prediction confidence. Furthermore, the development of large-scale benchmarks for human behavior analysis has provided a comprehensive evaluation suite for assessing the understanding of human behavior in autonomous driving.

Particularly noteworthy papers include: STRAP, which proposes a novel spatial-temporal risk-attentive trajectory prediction framework that incorporates a risk potential field to assess perceived risks arising from behaviors of nearby vehicles. Simplifying Traffic Anomaly Detection with Video Foundation Models, which investigates an architecturally simple encoder-only approach using plain Video Vision Transformers and studies how pre-training enables strong TAD performance. Foresight in Motion, which introduces an interpretable, reward-driven intention reasoner grounded in a novel query-centric Inverse Reinforcement Learning scheme to enhance trajectory prediction confidence.