Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles

arXiv:2404.06732v1 Announce Type: cross
Abstract: With the increasing presence of autonomous vehicles (AVs) on public roads, developing robust control strategies to navigate the uncertainty of human-driven vehicles (HVs) is crucial. This paper introduces an advanced method for modeling HV behavior, combining a first-principles model with Gaussian process (GP) learning to enhance velocity prediction accuracy and provide a measurable uncertainty. We validated this innovative HV model using real-world data from field experiments and applied it to develop a GP-enhanced model predictive …

abstract advanced arxiv autonomous autonomous vehicles avs behavior control cs.lg cs.ro cs.sy eess.sy human mixed modeling paper public roads robust safety strategies traffic type uncertainty vehicles