This paper presents the algorithms and system architecture of an autonomous racecar. The introduced vehicle is powered by a software stack designed for robustness, reliability, and extensibility. In order to autonomously race around a previously unknown track, the proposed solution combines state of the art techniques from different fields of robotics. Specifically, perception, estimation, and control are incorporated into one high-performance autonomous racecar. This complex robotic system, developed by AMZ Driverless and ETH Zurich, finished 1st overall at each competition we attended: Formula Student Germany 2017, Formula Student Italy 2018 and Formula Student Germany 2018. We discuss the findings and learnings from these competitions and present an experimental evaluation of each module of our solution.

AMZ Driverless: The full autonomous racing system

This paper shows a software stack capable of planning a minimum curvature trajectory for an autonomous race car on the basis of an occupancy grid map and introduces a controller design that allows ...

Minimum curvature trajectory planning and control for an autonomous race car

The rising popularity of self-driving cars has led to the emergence of a new research field in recent years: Autonomous racing. Researchers are developing software and hardware for high-performance race vehicles which aim to operate autonomously on the edge of the vehicle’s limits: High speeds, high accelerations, low reaction times, highly uncertain, dynamic, and adversarial environments. This paper represents the first holistic survey that covers the research in the field of autonomous racing. We focus on the field of autonomous racecars only and display the algorithms, methods, and approaches used in the areas of perception, planning, control, and end-to-end learning. Further, with an increasing number of autonomous racing competitions, researchers now have access to high-performance platforms to test and evaluate their autonomy algorithms. This survey presents a comprehensive overview of the current autonomous racing platforms, emphasizing the software-hardware co-evolution to the current stage. Finally, based on additional discussion with leading researchers in the field, we conclude with a summary of open research challenges that will guide future researchers in this field.

Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing

This paper shows the planning of time-optimal trajectories, which allows an autonomous race car to drive at the handling limits, taking into account locally changing road friction values. For this ...

Time-optimal trajectory planning for a race car considering variable tyre-road friction coefficients

This paper presents a detailed description of the software architecture that is used in the autonomous Roborace vehicles by the TUM-Team. The development of the software architecture was driven by both hardware components and usage of open source languages for making the software architecture reusable and easy to understand. The architecture combines the autonomous software functions perception, planning and control which are modularized for the usage on different hardware and for the purpose of using the car on high speed racetracks. The goal of the paper is to show which software functions are necessary for letting the car drive autonomously and fast around a racetrack.

A Software Architecture for an Autonomous Racecar

What can we learn from autonomous level-5 motorsport?

Based on a software architecture for autonomous driving presented and tested in an autonomous level-5 race-car 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.

Alexander Heilmeier

Papers

Minimum curvature trajectory planning and control for an autonomous race car

Time-optimal trajectory planning for a race car considering variable tyre-road friction coefficients

A Software Architecture for an Autonomous Racecar

What can we learn from autonomous level-5 motorsport?

A Software Architecture for the Dynamic Path Planning of an Autonomous Racecar at the Limits of Handling