MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
arXiv:2604.10169v2 Announce Type: replace-cross Abstract: Trajectory prediction is a key component of autonomous driving systems because future motions directly affect collision checking, behavior planning, and control. The task remains challenging under dense interactions, heterogeneous behaviors, multimodal futures, and limited on-board computation. Existing graph, attention, and generative predictors improve interaction reasoning or uncertainty modeling, but their high-capacity designs are often costly for real-time deployment. Lightweight predictors and conventional distillation reduce inf
The paper addresses a critical bottleneck in autonomous driving, specifically the computational demands of advanced trajectory prediction, which is a timely challenge as autonomous systems move towards real-world deployment.
This research provides a solution for deploying complex AI models in real-time, resource-constrained environments, which is crucial for the safety and viability of autonomous vehicles and other AI-powered robotics.
The development of reinforced heterogeneous distillation allows for the deployment of high-performing trajectory prediction models without the prohibitive computational cost, accelerating the path to commercially viable autonomous systems.
- · Autonomous vehicle developers
- · Logistics and transportation companies
- · AI hardware manufacturers
- · Robotics companies
Improved safety and efficiency of autonomous driving systems due to more accurate real-time predictions.
Faster adoption and broader deployment of autonomous vehicles and related robotic applications across various industries.
Increased public trust and regulatory acceptance of autonomous systems, leading to a significant expansion of the market.
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