Benchmarking of a software stack for autonomous racing against a professional human race driver

The way to full autonomy of public road vehicles requires the step-by-step replacement of the human driver, with the ultimate goal of replacing the driver completely. Eventually, the driving software has to be able to handle all situations that occur on its own, even emergency situations. These particular situations require extreme combined braking and steering actions at the limits of handling to avoid an accident or to diminish its consequences. An average human driver is not trained to handle such extreme and rarely occurring situations and therefore often fails to do so. However, professional race drivers are trained to drive a vehicle utilizing the maximum amount of possible tire forces. These abilities are of high interest for the development of autonomous driving software. Here, we compare a professional race driver and our software stack developed for autonomous racing with data analysis techniques established in motorsports. The goal of this research is to derive indications for further improvement of the performance of our software and to identify areas where it still fails to meet the performance level of the human race driver. Our results are used to extend our software's capabilities and also to incorporate our findings into the research and development of public road autonomous vehicles.Comment: Accepted at 2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER

TUM Autonomous Motorsport: An Autonomous Racing Software for the Indy Autonomous Challenge

For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems like disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the Indy Autonomous Challenge, which holds two competitions: A single-vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from TUM placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around 270 km/h and 28 ms2. This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the two-year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. Based on this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real-world evaluation of the displayed concepts.Comment: 37 pages, 18 figures, 2 table