Traction Adaptive Motion Planning at the Limits of Handling
This work addresses safety and performance in autonomous driving by enabling better handling at traction limits, though it is incremental as it builds on existing optimal control methods.
The paper tackled motion planning for vehicles under varying traction conditions by proposing a method with time-varying tire force constraints and an integrated sampling augmentation to handle infeasibility and local minima, resulting in improved accident avoidance capacity in critical scenarios on a heavy-duty vehicle.
In this paper, we address the problem of motion planning and control at the limits of handling, under locally varying traction conditions. We propose a novel solution method where traction variations over the prediction horizon are represented by time-varying tire force constraints, derived from a predictive friction estimate. A constrained finite time optimal control problem is solved in a receding horizon fashion, imposing these time-varying constraints. Furthermore, our method features an integrated sampling augmentation procedure that addresses the problems of infeasibility and sensitivity to local minima that arise at abrupt constraint alterations, e.g., due to sudden friction changes. We validate the proposed algorithm on a Volvo FH16 heavy-duty vehicle, in a range of critical scenarios. Experimental results indicate that traction adaptive motion planning and control improves the vehicle's capacity to avoid accidents, both when adapting to low local traction, by ensuring dynamic feasibility of the planned motion, and when adapting to high local traction, by realizing high traction utilization.