TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Abstract

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.

37 pages, 18 figures, 2 tables

References in corpus (5)

• Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing
• A Sequential Two-Step Algorithm for Fast Generation of Vehicle Racing Trajectories
• Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios
• Robust Model Predictive Path Integral Control: Analysis and Performance Guarantees
• Energy Management Strategy for an Autonomous Electric Racecar using Optimal Control

Cited by in corpus (12)

• Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems
• Multi-Modal Sensor Fusion and Object Tracking for Autonomous Racing
• ForzaETH Race Stack -- Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardware
• MixNet: Structured Deep Neural Motion Prediction for Autonomous Racing
• Learning Model Predictive Control with Error Dynamics Regression for Autonomous Racing
• Analyzing the Impact of Simulation Fidelity on the Evaluation of Autonomous Driving Motion Control
• Evaluation of Local Planner-Based Stanley Control in Autonomous RC Car Racing Series
• Three-Dimensional Vehicle Dynamics State Estimation for High-Speed Race Cars under varying Signal Quality
• Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehicles
• Scalable Supervisory Architecture for Autonomous Race Cars
• Approaching Current Challenges in Developing a Software Stack for Fully Autonomous Driving
• Dynamic Constraint Tightening for Nonlinear MPC for Autonomous Racing via Contraction Analysis