An Enabling Decision-Making Scheme by Considering Trajectory Prediction and Motion Uncertainty

Decision-making plays a crucial role in enabling automated driving and its performance is highly dependent on accurate motion prediction. Most existing motion prediction methods employ black-box neural network approaches, which may introduce high uncertainty and thus compromise driving safety. This study proposes a decision-making framework that takes motion prediction into account while considering predicted motion uncertainty. First, a multi-modal trajectory prediction method is introduced by combining the motion trend of the ego vehicle to reduce trajectory uncertainty. Then a two-dimensional dynamic risk assessment approach based on the Gaussian distribution is developed to quantify potential collision risk by analyzing occupancy relationship in space-time. Additionally, elementary motion patterns of automated driving in dynamic traffic environments are analyzed, and a decision-making method integrating finite state machines and the Markov decision process is established. Taking into account future motion uncertainty, it can dynamically adjust the ego vehicle's driving states in advance to ensuring driving safety. Finally, the effectiveness of the proposed decision-making framework is verified under different driving scenarios extracted from the NGSIM dataset, demonstrating satisfying results in avoiding potential driving risks.