Head-to-Head autonomous racing at the limits of handling in the A2RL challenge
Institute of Automotive Technology, TUM（汽车技术研究所，慕尼黑技术大学）；Chair of Automatic Control, TUM（自动控制教授座，慕尼黑技术大学）；Professorship Autonomous Vehicle Systems, TUM（自主车辆系统教授职位，慕尼黑技术大学）
Simon Hoffmann, Simon Sagmeister, Tobias Betz, Joscha Bongard, Sascha Büttner, Dominic Ebner, Daniel Esser, Georg Jank, Sven Goblirsch, Alexander Langmann, Maxi… 展开作者
Simon Hoffmann, Simon Sagmeister, Tobias Betz, Joscha Bongard, Sascha Büttner, Dominic Ebner, Daniel Esser, Georg Jank, Sven Goblirsch, Alexander Langmann, Maximilian Leitenstern, Levent Ögretmen, Phillip Pitschi, Ann-Kathrin Schwehn, Cornelius Schröder, Marcel Weinmann, Frederik Werner, Boris Lohmann, Johannes Betz, Markus Lienkamp
Autonomous racing presents a complex challenge involving multi-agent interactions between vehicles operating at the limit of performance and dynamics. As such, it provides a valuable research and testing environment for advancing autonomous driving technology and improving road safety. This article presents the algorithms and deployment strategies developed by the TUM Autonomous Motorsport team for the inaugural Abu Dhabi Autonomous Racing League (A2RL). We showcase how our software emulates human driving behavior, pushing the limits of vehicle handling and multi-vehicle interactions to win the A2RL. Finally, we highlight the key enablers of our success and share our most significant learnings.
End2Race: An End-to-End Learning Framework for Multi-Vehicle Autonomous Racing
Department of Civil and Environmental Engineering, University of Michigan（土木与环境工程系，密歇根大学）；University of Michigan Transportation Research Institute（密歇根大学交通研究所）
This paper describes Ariel Team's autonomous racing controller for the Indy Autonomous Challenge (IAC) simulation race. IAC is the first multi-vehicle autonomous head-to-head competition, reaching speeds of 300 km/h along an oval track, modeled after the Indianapolis Motor Speedway (IMS). Our racing controller attempts to maximize progress along the track while avoiding collisions with opponent vehicles and obeying the race rules. To this end, the racing controller first computes a race line offline. Then, it repeatedly computes online a small set of dynamically feasible maneuver candidates, e…
While the majority of autonomous driving research has concentrated on everyday driving scenarios, further safety and performance improvements of autonomous vehicles require a focus on extreme driving conditions. In this context, autonomous racing is a new area of research that has been attracting considerable interest recently. Due to the fact that a vehicle is driven by its perception, planning, and control limits during racing, numerous research and development issues arise. This paper provides a comprehensive overview of the autonomous racing system built by team KAIST for the Indy Autonomo…
Korea Advanced Institute of Science and Technology（韩国科学技术院）
Resolving edge-cases in autonomous driving, head-to-head autonomous racing is getting a lot of attention from the industry and academia. In this study, we propose a game-theoretic model predictive control (MPC) approach for head-to-head autonomous racing and data-driven model identification method. For the practical estimation of nonlinear model parameters, we adopted the hyperband algorithm, which is used for neural model training in machine learning. The proposed controller comprises three modules: 1) game-based opponents' trajectory predictor, 2) high-level race strategy planner, and 3) MPC…
Technical University of Munich（慕尼黑工业大学）；University of Pennsylvania（宾夕法尼亚大学）
Motorsport has always been an enabler for technological advancement, and the same applies to the autonomous driving industry. The team TUM Auton-omous Motorsports will participate in the Indy Autonomous Challenge in Octo-ber 2021 to benchmark its self-driving software-stack by racing one out of ten autonomous Dallara AV-21 racecars at the Indianapolis Motor Speedway. The first part of this paper explains the reasons for entering an autonomous vehicle race from an academic perspective: It allows focusing on several edge cases en-countered by autonomous vehicles, such as challenging evasion mane…
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, professio…
University of Pittsburgh（匹兹堡大学）；University of Waterloo（滑铁卢大学）；Massachusetts Institute of Technology（麻省理工学院）；Carnegie Mellon University（卡内基梅隆大学）；Argo AI
Race cars are routinely driven to the edge of their handling limits in dynamic scenarios well above 200mph. Similar challenges are posed in autonomous racing, where a software stack, instead of a human driver, interacts within a multi-agent environment. For an Autonomous Racing Vehicle (ARV), operating at the edge of handling limits and acting safely in these dynamic environments is still an unsolved problem. In this paper, we present a baseline controls stack for an ARV capable of operating safely up to 140mph. Additionally, limitations in the current approach are discussed to highlight the n…
FSDP: Fast and Safe Data-Driven Overtaking Trajectory Planning for Head-to-Head Autonomous Racing Competitions
Department of Control Science and Engineering, Zhejiang University（控制科学与工程系，浙江大学）；Center for Project-Based Learning, D-ITET, ETH Zurich（基于项目的学习中心，苏黎世联邦理工学院）
ForzaETH Race Stack -- Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardware
ETH Zürich（苏黎世联邦理工学院）；Center for Project-Based Learning (PBL), ETH Zürich（苏黎世联邦理工学院项目式学习中心）；Institute for Dynamic Systems and Control (IDSC), ETH Zürich（苏黎世联邦理工学院动态系统与控制研究所）
A good racing strategy and in particular the racing line is decisive to winning races in Formula 1, MotoGP, and other forms of motor racing. The racing line defines the path followed around a track as well as the optimal speed profile along the path. The objective is to minimize lap time by driving the vehicle at the limits of friction and handling capability. The solution naturally depends upon the geometry of the track and vehicle dynamics. We introduce a novel method to compute the racing line using Bayesian optimization. Our approach is fully data-driven and computationally more efficient …
Hierarchical Control for Head-to-Head Autonomous Racing
Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；School for Engineering of Matter, Transport, and Energy（物质、运输与能源工程学院）；Arizona State University（亚利桑那州立大学）
With the evolution of self-driving cars, autonomous racing series like Roborace and the Indy Autonomous Challenge are rapidly attracting growing attention. Researchers participating in these competitions hope to subsequently transfer their developed functionality to passenger vehicles, in order to improve self-driving technology for reasons of safety, and due to environmental and social benefits. The race track has the advantage of being a safe environment where challenging situations for the algorithms are permanently created. To achieve minimum lap times on the race track, it is important to…
Based on the direct perception paradigm of autonomous driving, we investigate and modify the CNNs (convolutional neural networks) AlexNet and GoogLeNet that map an input image to few perception indicators (heading angle, distances to preceding cars, and distance to road centerline) for estimating driving affordances in highway traffic. We also design a controller with these indicators and the short-range sensor information of TORCS (the open racing car simulator) for driving simulated cars to avoid collisions. We collect a set of images from a TORCS camera in various driving scenarios, train t…
The development of software components for autonomous driving functions should always include an extensive and rigorous evaluation. Since real-world testing is expensive and safety-critical -- especially when facing dynamic racing scenarios at the limit of handling -- a favored approach is simulation-based testing. In this work, we propose an open-source graphical user interface, which allows the generation of a multi-vehicle scenario in a regular or even a race environment. The underlying method and implementation is elaborated in detail. Furthermore, we showcase the potential use-cases for t…
Carnegie Mellon University（卡内基梅隆大学）；Cleveland State University（克利夫兰州立大学）
Autonomous racing is a challenging problem, as the vehicle needs to operate at the friction or handling limits in order to achieve minimum lap times. Autonomous race cars require highly accurate perception, state estimation, planning and precise application of controls. What makes it even more challenging is the accurate identification of vehicle model parameters that dictate the effects of the lateral tire slip, which may change over time, for example, due to wear and tear of the tires. Current works either propose model identification offline or need good parameters to start with (within 15-…
RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning
TUM School of Engineering and Design, Technical University of Munich（慕尼黑工业大学工程与设计学院）；Graduate School of Information Science and Technology, The University of Tokyo（东京大学信息科学与技术研究生院）
A Hybrid Sampling-Based Trajectory Planner with Game-Theoretic Guidance for Autonomous Racing
Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Technical University of Munich（慕尼黑工业大学工程与设计学院自动驾驶车辆系统教授组）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）
Autonomous Systems Lab, ETH Zürich（苏黎世联邦理工学院自主系统实验室）；CVG Group, ETH Zürich（苏黎世联邦理工学院计算机视觉与几何组）
In autonomous racing, vehicles operate close to the limits of handling and a sensor failure can have critical consequences. To limit the impact of such failures, this paper presents the redundant perception and state estimation approaches developed for an autonomous race car. Redundancy in perception is achieved by estimating the color and position of the track delimiting objects using two sensor modalities independently. Specifically, learning-based approaches are used to generate color and pose estimates, from LiDAR and camera data respectively. The redundant perception inputs are fused by a…
In head-to-head racing, an accurate model of interactive behavior of the opposing target vehicle (TV) is required to perform tightly constrained, but highly rewarding maneuvers such as overtaking. However, such information is not typically made available in competitive scenarios, we therefore propose to construct a prediction and uncertainty model given data of the TV from previous races. In particular, a one-step Gaussian process (GP) model is trained on closed-loop interaction data to learn the behavior of a TV driven by an unknown policy. Predictions of the nominal trajectory and associated…
er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds
University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）；University of Parma（帕尔马大学）；Technology Innovation Institute - Autonomous Robotics Research Center（技术创新研究所自主机器人研究中心）；HIPERT srl（希佩尔特有限公司）；University of Pisa（比萨大学）；ETH Zurich（苏黎世联邦理工学院）
Autonomous Systems Lab, ETH Zürich（苏黎世联邦理工学院自主系统实验室）；Robotics and Autonomous Systems Group, CSIRO（澳大利亚联邦科学与工业研究组织机器人与自主系统组）；Sevensense Robotics A.G（Sevensense Robotics 股份公司）
Velocity estimation plays a central role in driverless vehicles, but standard and affordable methods struggle to cope with extreme scenarios like aggressive maneuvers due to the presence of high sideslip. To solve this, autonomous race cars are usually equipped with expensive external velocity sensors. In this paper, we present an end-to-end recurrent neural network that takes available raw sensors as input (IMU, wheel odometry, and motor currents) and outputs velocity estimates. The results are compared to two state-of-the-art Kalman filters, which respectively include and exclude expensive v…
Hong Kong University of Science and Technology（香港科技大学）
Human drone interaction in autonomous navigation incorporates spatial interaction tasks, including reconstructed 3D map from the drone and human desired target position. Augmented Reality (AR) devices can be powerful interactive tools for handling these spatial interactions. In this work, we build an AR interface that displays the reconstructed 3D map from the drone on physical surfaces in front of the operator. Spatial target positions can be further set on the 3D map by intuitive head gaze and hand gesture. The AR interface is deployed to interact with an autonomous drone to explore an unkno…
The rising popularity of driver-less cars has led to the research and development in the field of autonomous racing, and overtaking in autonomous racing is a challenging task. Vehicles have to detect and operate at the limits of dynamic handling and decisions in the car have to be made at high speeds and high acceleration. One of the most crucial parts in autonomous racing is path planning and decision making for an overtaking maneuver with a dynamic opponent vehicle. In this paper we present the evaluation of a track based offline policy learning approach for autonomous racing. We define spec…
Proper path planning is the first step of robust and efficient autonomous navigation for mobile robots. Meanwhile, it is still challenging for robots to work in a complex environment without complete prior information. This paper presents an extension to the A* search algorithm and its variants to make the path planning stable with less computational burden while handling long-distance tasks. The implemented algorithm is capable of online searching for a collision-free and smooth path when heading to the defined goal position. This paper deploys the algorithm on the autonomous drone platform a…
