A ModelFree Algorithm to Safely Approach the Handling Limit of an Autonomous Racecar One of the key aspects in racing is the ability of the driver to find the handling limits of the vehicle to minimize the resulting lap time. Many approaches for raceline optimization assume the tireroad friction coefficient to be known. However, this neglects the fact that the ability of the system to realize such a race trajectory depends on complex interdependencies between the online trajectory planner, the control systems and the nonmodelled uncertainties. In general, a high quality control system can approach the physical limit more reliable, as it applies less corrective actions. We present a modelfree learning method to find the minimum achievable laptime for a given controller using online adaption of a scale factor for the maximum longitudinal and lateral accelerations in the online trajectory planner. In contrast to existing concepts, our approach can be applied as an extension to already available planning and control algorithms instead of replacing them. We demonstrate reliable and safe operation for different vehicle setups in simulation and demonstrate that the algorithm works successfully on a fullsize racecar.

Adaptive Trajectory Planning and optimization at Limits of Handling In this paper, we tackle the problem of trajectory planning and control of a vehicle under locally varying traction limitations, in the presence of suddenly appearing obstacles. We employ concepts from adaptive model predictive control for runtime adaptation of tire force constraints that are imposed by local traction conditions. To solve the resulting optimization problem for realtime control synthesis with such time varying constraints, we propose a novel numerical scheme based on Real Time Iteration Sequential Quadratic Programming (RTISQP), which we call Sampling Augmented Adaptive RTI (SAARTI). Sampling augmentation of conventional RTISQP provides additional feasible candidate trajectories for warmstarting the optimization procedure. Thus, the proposed SAARTI algorithm enables real time constraint adaptation and reduces sensitivity to local minima. Through extensive numerical simulations we demonstrate that our method increases the vehiclexe2x80x99s capacity to avoid accidents in scenarios with unanticipated obstacles and locally varying traction, compared to equivalent nonadaptive control schemes and traditional planning and tracking approaches.