Towards Scalable & Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation

arXiv:2404.01746v1 Announce Type: cross
Abstract: Real-world driving involves intricate interactions among vehicles navigating through dense traffic scenarios. Recent research focuses on enhancing the interaction awareness of autonomous vehicles to leverage these interactions in decision-making. These interaction-aware planners rely on neural-network-based prediction models to capture inter-vehicle interactions, aiming to integrate these predictions with traditional control techniques such as Model Predictive Control. However, this integration of deep learning-based models with traditional control paradigms often results in computationally demanding optimization problems, relying on …

abstract arxiv autonomous autonomous vehicles cs.ai cs.lg cs.ro decision distillation driving interactions knowledge making network planning prediction prediction models research scalable through traffic type vehicles world