Accurate Mapping and Planning for Autonomous Racing

Andresen, Leiv; Brandemuehl, Adrian; Hönger, Alex; Kuan, Benson; Vödisch, Niclas; Blum, Hermann; Reijgwart, Victor; Bernreiter, Lukas; Sekanina, Lukáš; Chung, Jen Jen; Bürki, Mathias; Oswald, Martin R.; Siegwart, Roland; Gawel, Abel

doi:10.1109/iros45743.2020.9341702

Cited by 15 publications

(11 citation statements)

References 19 publications

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“…In comparison to other methods of finding the optimal trajectory, the ANN is capable of predicting the racing line with equivalent or better accuracy than other traditional methods -the difference between the line predicted by the ANN and the OCP method compares favourably to the differences between Siegler et al From the perspective of AV control, the ANN's prediction accuracy is similar to Andresen et al (2020)'s accuracy of map generation in an autonomous racing vehicle at racing speed (±0.39m vs ±0.29m RMSE respectively). The accuracy is qualitatively similar to that achievable by a path-following driver model following a racing line at speed in an autonomous racing car (Culley et al, 2020;Kapania, 2016), and a road-going vehicle in Wang et al (2016).…”

Section: Prediction Accuracymentioning

confidence: 98%

Real-Time Optimal Trajectory Planning for Autonomous Vehicles and Lap Time Simulation Using Machine Learning

Garlick¹,

Bradley²

2021

Preprint

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The widespread development of driverless vehicles has led to the formation of autonomous racing competitions, where the high speeds and fierce rivalry in motorsport provide a testbed to accelerate technology development. A particular challenge for an autonomous vehicle is that of identifying a target trajectory -or in the case of a racing car, the ideal racing line. Many existing approaches to identifying the racing line are either not the time-optimal solutions, or have solution times which are computationally expensive, thus rendering them unsuitable for real-time application using on-board processing hardware. This paper describes a machine learning approach to generating an accurate prediction of the racing line in real-time on desktop processing hardware. The proposed algorithm is a dense feed-forward neural network, trained using a dataset comprising racing lines for a large number of circuits calculated via a traditional optimal control lap time simulation. The network is capable of predicting the racing line with a mean absolute error of ±0.27m, meaning that the accuracy outperforms a human driver, and is comparable to other parts of the autonomous vehicle control system. The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line. Results suggest that a data-driven approach may therefore be favourable for real-time generation of nearoptimal racing lines than traditional computational methods.

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Section: Prediction Accuracymentioning

confidence: 98%

Real-Time Optimal Trajectory Planning for Autonomous Vehicles and Lap Time Simulation Using Machine Learning

Garlick¹,

Bradley²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the field of perception, researchers use the unique environment of the race track to demonstrate large-scale mapping with fewer features (Nobis et al, 2019) as well as localization at high speeds (Renzler et al, 2020;Schratter et al, 2021). Since the Formula Student Driverless (FSD) competition requires the teams to drive and localize at the same time, the teams present Graph-SLAM (Andresen et al, 2020;Large et al, 2021) and Recurrent Neural Network-based methods (Srinivasan et al, 2020) for localization and state estimation of the FSD vehicle. In addition, the FSD competition provides yellow and blue cones as the race track and the teams need to detect those cones at high vehicle speeds.…”

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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“…pilatus is equipped with a complete sensor suite including two LiDARs, three cameras, an absolute speed sensor and two IMUs. The low-level as well as the state estimation are deployed on an ETAS ES900 real-time embedded system; the remainder of the Autonomous System (AS), including mapping and localization, runs on an Intel Xeon E3 processor, see [11], [29]- [32] for more details.…”

Section: A Pilatus Driverlessmentioning

confidence: 99%

A Holistic Motion Planning and Control Solution to Challenge a Professional Racecar Driver

Srinivasan,

Giles,

Liniger

2021

Preprint

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We present a holistically designed three layer control architecture capable of outperforming a professional driver racing the same car. Our approach focuses on the codesign of the motion planning and control layers, extracting the full potential of the connected system. First, a high-level planner computes an optimal trajectory around the track, then in real-time the mid-level nonlinear model predictive controller follows this path using the high-level information as guidance. Finally a high frequency, low-level controller tracks the states predicted by the mid-level controller. Tracking the predicted behavior has two advantages: it reduces the mismatch between the model used in the upper layers and the real car, and allows for a torque vectoring command to be optimized by the higher level motion planners. The tailored design of the low-level controller proved to be crucial for bridging the gap between planning and control, unlocking unseen performance in autonomous racing. The proposed approach was verified on a full size racecar, resulting in a considerable improvement over the state-of-the-art results achieved on the same vehicle. Finally, we also show that the proposed co-design approach outperforms a professional racecar driver.

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Accurate Mapping and Planning for Autonomous Racing

Cited by 15 publications

References 19 publications

Real-Time Optimal Trajectory Planning for Autonomous Vehicles and Lap Time Simulation Using Machine Learning

Real-Time Optimal Trajectory Planning for Autonomous Vehicles and Lap Time Simulation Using Machine Learning

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

A Holistic Motion Planning and Control Solution to Challenge a Professional Racecar Driver

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