Autonomous racing has seen significant advancements, driven by competitions such as the Indy Autonomous Challenge (IAC) and the Abu Dhabi Autonomous Racing League (A2RL). However, developing an autonomous racing stack for a full-scale car is often constrained by limited access to dedicated test tracks, restricting opportunities for real-world validation. While previous work typically requires extended development cycles and significant track time, this paper introduces a minimalistic autonomous racing stack for high-speed time-trial racing that emphasizes rapid deployment and efficient system integration with minimal on-track testing. The proposed stack was validated on real speedways, achieving a top speed of 206 km/h within just 11 hours' practice run on the track with 325 km in total. Additionally, we present the system performance analysis, including tracking accuracy, vehicle dynamics, and safety considerations, offering insights for teams seeking to rapidly develop and deploy an autonomous racing stack with limited track access.

Autonomous motorsports aim to replicate the human racecar driver with software and sensors. As in traditional motorsports, Autonomous Racing Vehicles (ARVs) are pushed to their handling limits in multi-agent scenarios at extremely high ($\geq 150mph$) speeds. This Operational Design Domain (ODD) presents unique challenges across the autonomy stack. The Indy Autonomous Challenge (IAC) is an international competition aiming to advance autonomous vehicle development through ARV competitions. While far from challenging what a human racecar driver can do, the IAC is pushing the state of the art by facilitating full-sized ARV competitions. This paper details the MIT-Pitt-RW Team's approach to autonomous racing in the IAC. In this work, we present our modular and fast approach to agent detection, motion planning and controls to create an autonomy stack. We also provide analysis of the performance of the software stack in single and multi-agent scenarios for rapid deployment in a fast-paced competition environment. We also cover what did and did not work when deployed on a physical system the Dallara AV-21 platform and potential improvements to address these shortcomings. Finally, we convey lessons learned and discuss limitations and future directions for improvement.

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This paper describes the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge have contributed to this dataset. The dataset spans 11 interesting racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at different speeds. The dataset contains data from 27 racing sessions across the 11 scenarios with over 6.5 hours of sensor data recorded from the track. The data is organized and released in both ROS2 and nuScenes format. We have also developed the ROS2-to-nuScenes conversion library to achieve this. The RACECAR data is unique because of the high-speed environment of autonomous racing. We present several benchmark problems on localization, object detection and tracking (LiDAR, Radar, and Camera), and mapping using the RACECAR data to explore issues that arise at the limits of operation of the vehicle.

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 Autonomous Challenge (IAC). Our autonomy stack consists primarily of a multi-modal perception module, a high-speed overtaking planner, a resilient control stack, and a system status manager. We present the details of all components of our autonomy solution, including algorithms, implementation, and unit test results. In addition, this paper outlines the design principles and the results of a systematical analysis. Even though our design principles are derived from the unique application domain of autonomous racing, they can also be applied to a variety of safety-critical, high-cost-of-failure robotics applications. The proposed system was integrated into a full-scale autonomous race car (Dallara AV-21) and field-tested extensively. As a result, team KAIST was one of three teams who qualified and participated in the official IAC race events without any accidents. Our proposed autonomous system successfully completed all missions, including overtaking at speeds of around $220 km/h$ in the IAC@CES2022, the world's first autonomous 1:1 head-to-head race.

Kurt "The CyberGuy" Knutsson looks at how autonomous car racing is revving up. It is no secret that autonomous driving is rapidly becoming the way of the future. Amazon recently had its first test drive of the Zoox robotaxi, and it was a major success, so many are wondering if this will become the norm down the road. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER As part of the 2023 International CES Convention that took place in January, the Indy Autonomous Challenge made its return to showcase some of the fastest autonomous race cars in the world. The Indy Autonomous Challenge had its tournament aiming to push the boundaries of head-to-head autonomous racing and showcase the future of autonomous mobility at Las Vegas Motor Speedway.

A robotic car has broken the world speed record, reaching impressive speeds of 192.2mph (309.3kph). The car, developed by a team from the Politecnico di Milano, called PoliMOVE, is fully autonomous and took to the track on the Space Shuttle airstrip at NASA's Kennedy Space Centre this week. During the test drive, the racecar clocked speeds of 192.2mph (309.3kph), 'This test run was exhilarating, and we are thrilled with the world record, but we're also excited by the fact that this data will be made available to all, and the industry will benefit from our work and learnings,' said Professor Sergio Savaresi, team lead from the Politecnico di Milano. The car, developed by a team from the Politecnico di Milano, called PoliMOVE, is fully autonomous and took to the track on the Space Shuttle airstrip at NASA's Kennedy Space Centre this week The racecar previously took to the track on 7 January, during the Indy Autonomous Challenge in Las Vegas – the first head-to-head race between autonomous cars.

So, you know, if this robot has a different camera than that robot, I can still get it, figure it out and get it to work. I don't, I don't have to throw the application away. And that's one of the things that's a little crazy in automotive is, you know, you start swapping out some hardware pieces underneath. You might actually have to scrap and completely rewrite an application because you don't have these kinds of abstractions, you know, you don't have the ability to like, just pick up your software. Like, you know, oh, I was running on, you know, ECU A today, but I can't get it because of the COVID supply chain.

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 maneuvers and unstructured scenarios. At the same time, it is inherently safe due to the motor-sport related track safety precautions. It is therefore an ideal testing ground for the development of autonomous driving algorithms capable of mastering the most challenging and rare situations. In addition, we provide insight into our soft-ware development workflow and present our Hardware-in-the-Loop simulation setup. It is capable of running simulations of up to eight autonomous vehicles in real time. The second part of the paper gives a high-level overview of the soft-ware architecture and covers our development priorities in building a high-per-formance autonomous racing software: maximum sensor detection range, relia-ble handling of multi-vehicle situations, as well as reliable motion control under uncertainty.

You want to find network errors fast and need a reliable tool. You are not alone, try Fiddler Everywhere free for 30-days and let this all in one web debugging solution deliver the successful outcomes you expect. My name is Roland Meertens, editor for AI and machine learning at InfoQ and product manager at Annotell. Today I will host the podcast and I will be talking with Joe, Sebastian, and Florian about the Indy Autonomous Challenge and how they managed to win it. Could you maybe introduce yourself to the listeners and tell them about what the Indy Autonomous Challenge is? Joe Speed: Sure, happy to. I'm a technical advisor for the Indy Autonomous Challenge, which is an amazing university challenge for autonomous racing. I was part of the TUM Autonomous Motorsport team. We managed to win the challenge in the end, and my main responsibilities were the mapping and localization part. And my main responsibility in our team was the perception, mainly the object detection. Roland Meertens: All right, maybe we can get started with you Joe. Maybe you can say that about what this Indy Autonomous Challenge is. Joe Speed: It's an amazing program. So, a lot of this is anecdotal. So Sebastian Thrun, who is very much like the godfather of modern autonomous driving, he had won the DARPA Grand Challenge. He was out at Indy and had commented something like, "Some of the things happening autonomy are not that exciting to me anymore, but if this, if the Indy 500 was autonomous, that would be interesting.