Autonomous Motion Decision-Making Considering Dual Uncertainties in Surrounding Vehicle Cut-in Intention-Action

Abstract

Reliable motion decision-making is critically associated with safety, efficiency, and comfort of autonomous vehicles. However, uncertainties in the cut-in behavior of vehicles in adjacent lanes make it difficult for autonomous vehicles to accurately assess and make decisions, thereby increasing the collision risks. To address this issue, a motion decision-making framework that accounts for dual uncertainties in surrounding vehicle cut-in intention and action is proposed, aiming to enhance driving safety and adaptability in dynamic traffic environments. Specifically, a hierarchical specific intention attention network is designed to extract vehicle motion correlations to predict cut-in probabilities and map driving intention uncertainty. Then, an uncertainty-driven Gaussian Markov decision-making model is proposed, where Gaussian process regression is used to further model vehicle cut-in action uncertainty based on uncertain intention. Additionally, a deep reinforcement learning algorithm for uncertainty exploration is proposed to learn the optimal driving strategy by embedding uncertainty information into the decision-making and exploration process through a multi-criteria information-gain reward mechanism. Simulation results demonstrate that our proposed method enables effective autonomous decision-making in uncertain cut-in scenarios, and has certain advantages in terms of overall safety, efficiency and comfort compared with existing algorithms.