Motion planning plays a significant role in enabling advances of autonomous vehicles in saving lives and improving traffic efficiency. In a predictive motion-planning strategy, the ego vehicle predicts the motion of surrounding vehicles and uses these predictions to plan collision-free reference trajectories. In dynamic multi-vehicle traffic environments, a key research question is how to consider vehicle-to-vehicle interactions and motion uncertainties of the surrounding vehicles in the motion planner to achieve resilient motion planning of the autonomous ego vehicle. This Licentiate Thesis proposes a model predictive control (MPC)-based approach to achieve safe motion planning in uncertain and dynamic multi-vehicle driving environments. MPC has been widely applied for the motion planning of autonomous vehicles. However, designing resilient MPC-based motion planners that consider interactions and uncertainties of surrounding vehicles remains an open and challenging problem, which is the primary motivation for the research presented in this thesis. This thesis makes several contributions toward solving the interaction and uncertainty-aware motion-planning problems. The first contribution is an MPC, which is called interaction-aware moving target MPC. It is designed based on the combination of an interaction-aware motion-prediction model and time-varying reference targets of the optimal control problem for proactive and non-local trajectory planning in multi-vehicle dynamic scenarios. In the second contribution, the proposed MPC is extended to account for the multi-modal motion uncertainties of surrounding vehicles, including the maneuver and trajectory uncertainties, which are predicted by combining an interaction-aware motion-prediction model and a data-driven approach. Based on the modeling of uncertainties, a safety-awareness parameter is included in the design to compute the obstacle occupancy for achieving a trade-off between the performance and robustness of the MPC planner. The efficiency of the method is ...
Interaction and Uncertainty-Aware Motion Planning for Autonomous Vehicles Using Model Predictive Control
2023-01-01
doi:10.3384/9789180752008
Hochschulschrift
Elektronische Ressource
Englisch
DDC: | 629 |
Development of model predictive motion planning and control for autonomous vehicles
Springer Verlag | 2019
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