Hierarchical Uncertainty-aware Autonomous Driving in Lane-changing Scenarios: Behavior Prediction and Motion Planning

Safe and efficient interactions with surrounding vehicles in multilane driving are essential for autonomous vehicles. However, achieving smooth and flexible responses to surrounding vehicles’ lane changes remains a challenge due to the uncertainties in the behavior prediction progress. Deep learning-based methods were manifested powerful in modeling agents’ motion uncertainties for making stochastic intention classification and trajectory prediction. Nevertheless, performance degradation are likely to occur when the black-box model makes multi-modal predictions in unseen situations. This paper proposes a novel AV planning framework that combines deep learning-based behavior prediction and optimization-based uncertainty-aware motion planning to resolve these challenges. We hierarchically address uncertainties inherent in both behavior patterns and model performance through an adaptive motion planning approach, using an improved constrained iterative linear quadratic regulator that handles non-convex constraints and non-Gaussian uncertainties while minimizing travel costs. Evaluations using INTERACTION and HighD datasets demonstrate the effectiveness of uncertainty-aware planning in enhancing AV safety performance.