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papersSEP 10 04:00 UTC

Researchers propose MAVEN-T for real-time multi-agent trajectory prediction in autonomous driving

A newly updated arXiv paper introduces MAVEN-T, a method combining reinforcement learning with heterogeneous knowledge distillation to forecast the future paths of multiple agents simultaneously. The work targets real-time deployment in autonomous vehicles, where anticipating surrounding traffic informs collision checking, planning, and control. The approach is designed to remain dependable in dense scenarios with diverse and multimodal agent behaviors.

MAVEN-T autonomous-driving knowledge distillation multi-agent-trajectory-prediction reinforcement-learning

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIMAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction ↗SEP 10 04:00 UTC

arXiv cs.LGMAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction ↗SEP 10 04:00 UTC