The field of autonomous driving is witnessing significant advancements in trajectory prediction, with a focus on improving the accuracy and robustness of predictions in complex scenarios. Researchers are exploring innovative techniques such as retrospection, probabilistic modeling, and intention fusion to better capture the dynamics of traffic participants and their interactions. These approaches aim to enhance the safety and reliability of autonomous vehicles by providing more accurate and informed predictions of future trajectories. Notably, the integration of dynamic intention points and subjective intent-based frameworks is showing promising results in reducing prediction latency and improving performance in heterogeneous traffic scenarios. Noteworthy papers include:

• Learning Through Retrospection, which proposes a novel retrospection technique to improve trajectory prediction by learning from aggregated feedback and analyzing errors during inference.
• RiskNet, which introduces an interaction-aware risk forecasting framework that integrates deterministic risk modeling with probabilistic behavior prediction for comprehensive risk assessment.
• SocialMOIF, which develops a multi-order intention fusion model to capture higher-order intention interactions among neighboring groups and reinforce first-order intention interactions between neighbors and the target agent.