A Software Architecture for the Dynamic Path Planning of an Autonomous Racecar at the Limits of Handling Based on a software architecture for autonomous driving presented and tested in an autonomous level5 racecar in 2018 this paper describes in detail the evolutionary enhancement of this software architecture. The architecture combines the autonomous software layers perception, planning and control, which were modularized in the core software. The focus of this paper is the detailed description of how we enhanced the software with a module for an object list creation, a module for the behavioral planning and a module for the creation of dynamic trajectories. These enhancements allow the car to overtake other cars and static obstacles autonomously when driving on the race track. Furthermore, we present with a high novelty value the software module for a vehicle performance maximization, which consists of a control performance assessment and a friction estimation. The software architecture displayed in this paper will be tested and evaluated in the Roborace Season Alpha in 2019.

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.