DeepRacing: A Framework for Autonomous Racing

Weiss, Trent; Behl, Madhur

doi:10.23919/date48585.2020.9116486

Cited by 22 publications

(16 citation statements)

References 8 publications

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“…The racing task provides a clear objective function (fastest lap time) for the algorithm training and the race track provides with its clear driveable area and one class of objects a perfect proving ground. Researchers in this field displayed partial end-to-end approaches (Weiss and Behl, 2020;Lee et al, 2019) that combine DNNs with MPC methods to create and follow dynamic trajectories. In addition, by using algorithms from the field of RL (e.g., Soft-Actor-Critic, Q-Learning), researchers were able to demonstrate how to train an agent to drive fast (Jaritz et al, 2018;de Bruin et al, 2018), how to train an agent to overtake other agents on the race track (Song et al, 2021) and how to bridge the sim-to-real gap with model-based RL approaches (Brunnbauer et al, 2021).…”

Section: Softwarementioning

confidence: 99%

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

Betz¹,

Betz²,

Fent³

et al. 2022

Preprint

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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 −1 and 28 m s −2 . 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.

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Section: Softwarementioning

confidence: 99%

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

Betz¹,

Betz²,

Fent³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In Cai et al (2021); Weiss and Behl (2020), RL agents were trained end-to-end on visual inputs by imitating expert demonstration; in Cai et al (2021), a data-driven model of the environment was further utilized to train the agent by unrolling future trajectories.…”

Section: Related Workmentioning

confidence: 99%

Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing

Francis¹,

Chen²,

Ganju³

et al. 2022

Preprint

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We present the results of our autonomous racing virtual challenge, based on the newly-released Learn-to-Race (L2R) simulation framework, which seeks to encourage interdisciplinary research in autonomous driving and to help advance the state of the art on a realistic benchmark. Analogous to racing being used to test cutting-edge vehicles, we envision autonomous racing to serve as a particularly challenging proving ground for autonomous agents as: (i) they need to make sub-second, safety-critical decisions in a complex, fast-changing environment; and (ii) both perception and control must be robust to distribution shifts, novel road features, and unseen obstacles. Thus, the main goal of the challenge is to evaluate the joint safety, performance, and generalisation capabilities of reinforcement learning agents on multi-modal perception, through a two-stage process. In the first stage of the challenge, we evaluate an autonomous agent's ability to drive as fast as possible, while adhering to safety constraints. In the second stage, we additionally require the agent to adapt to an unseen racetrack through safe exploration. In this paper, we describe the new L2R Task 2.0 benchmark, with refined metrics and baseline approaches. We also provide an overview of deployment, evaluation, and rankings for the inaugural instance of the L2R Autonomous Racing Virtual Challenge (supported by Carnegie Mellon University, Arrival Ltd., AICrowd, Amazon Web Services, and Honda Research), which officially used the new L2R Task 2.0 benchmark and received over 20,100 views, 437 active participants, 46 teams, and 733 model submissions-from 88+ unique institutions, in 58+ different countries. Finally, we release leaderboard results from the challenge and provide description of the two top-ranking approaches in cross-domain model transfer, across multiple sensor configurations and simulated races.

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“…On one hand, Reinforcement Learning (RL) is used to train an agent in an adversarial environment. DeepRacing [48]- [50] provides solutions on three levels: pixel to control, pixel to waypoints, and pixel to curves. [51], [52] uses A3C to train racing agents.…”

Section: B Learning-based Planningmentioning

confidence: 99%

Winning the 3rd Japan Automotive AI Challenge -- Autonomous Racing with the Autoware.Auto Open Source Software Stack

Zang,

Tumu,

Betz

et al. 2022

Preprint

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The 3rd Japan Automotive AI Challenge was an international online autonomous racing challenge where 164 teams competed in December 2021. This paper outlines the winning strategy to this competition, and the advantages and challenges of using the Autoware.Auto open source autonomous driving platform for multi-agent racing. Our winning approach includes a lane-switching opponent overtaking strategy, a global raceline optimization, and the integration of various tools from Autoware.Auto including a Model-Predictive Controller. We describe the use of perception, planning and control modules for high-speed racing applications and provide experience-based insights on working with Autoware.Auto. While our approach is a rule-based strategy that is suitable for non-interactive opponents, it provides a good reference and benchmark for learning-enabled approaches.

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DeepRacing: A Framework for Autonomous Racing

Cited by 22 publications

References 8 publications

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

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

Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing

Winning the 3rd Japan Automotive AI Challenge -- Autonomous Racing with the Autoware.Auto Open Source Software Stack

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