Enhancing ride comfort and driving safety in urban autonomous buses: A data-driven hierarchical longitudinal motion planning using approximate and stochastic MPCs

This paper presents an advanced hierarchical longitudinal motion planning strategy for autonomous buses (ABs), specifically designed to enhance ride comfort and ensure driving safety – key challenges in contemporary autonomous vehicle technologies. ABs typically face limitations due to a narrower perception range and heterogeneous motion profiles compared to human drivers. To tackle these issues, our study employs a dual-phase motion planning framework utilizing both approximate and stochastic Model Predictive Controls (MPCs). In the long time-horizon planning phase, we aim to enhance ride comfort by optimizing a reference motion profile that covers the full perception range of the ABs. This optimization is facilitated by leveraging deep neural networks to approximate the MPC policy, ensuring efficient real-time computational performance. The short time-horizon planning phase focuses on driving safety by determining optimal control inputs, considering vehicle dynamics, and integrating safety-related chance constraints with stochastic MPC. Additionally, this phase fine-tunes the weights in the MPC’s objective function based on risk indices derived from human driving data, balancing the priorities of safety in hazardous situations and maintaining ride comfort. Our hierarchical motion planning strategy ensures both consistent ride comfort and safety in critical scenarios. Through comprehensive evaluations using computer simulations and vehicle tests in actual urban bus-only lanes, the proposed method has proven to significantly enhance ride comfort while maintaining driving safety. This approach contributes to the ongoing improvement of the operational quality of ABs, supporting public transportation goals and meeting passenger expectations effectively.