Beating uncertainty in racing bot evolution through enhanced exploration and pole position selection

Salem, Mohammed; Mora, Antonio M.; Merelo, J. J.

doi:10.1109/cig.2019.8847998

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…10. User interface of the virtual platforms: (a) Gazebo [157]; (b) CoppeliaSim [136]; (c) Webots [158]; (d) MORSE [139]; (e) DeepDrive [159]; (f) CARLA [141]; (g) AutoDRIVE [142]; (h) LGSVL [143]; (i) Grand Theft Auto V [144]; (j) Gran Turismo Sport [145]; (k) TORCS [160]; (l) CarSim [161]; (m) CarMaker [148]; (n) rFpro [149]; (o) VRXPERIENCE [162].…”

Section: A Virtual Testing Platformsmentioning

confidence: 99%

A Survey of Vehicle Dynamics Modeling Methods for Autonomous Racing: Theoretical Models, Physical/Virtual Platforms, and Perspectives

Zhang,

Sun,

Wang

et al. 2024

IEEE Trans. Intell. Veh.

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This paper presents the first survey of vehicle dynamics modeling methods for autonomous racing. Previous surveys have covered dynamics models for standard autonomous vehicles or, alternatively, concentrated on planning and control methods in autonomous racing with vehicle dynamics models briefly mentioned. However, previous surveys overlook the importance of vehicle dynamics under challenging conditions of top speeds and non-steady state driving, which are unique characteristics in autonomous racing. Recognizing the vital role of vehicle dynamics modeling in an autonomous racecar's prediction, planning, and control modules, this survey seeks to ascertain to what degree the nominal full-scale racecar dynamics can be streamlined without sacrificing accuracy for simplicity. Furthermore, this survey provides essential guidance for organizers of virtual autonomous races, helping them choose vehicle dynamics models that meet the required level of precision. This paper begins with a review of previous surveys on vehicle dynamics modeling, highlighting their limitations in the context of autonomous racing. Following this, it investigates the existing dynamics models for autonomous racing vehicles, along with a comprehensive examination of the existing physical/virtual testing platforms. The paper concludes by discussing emerging trends and offering perspectives in the field of vehicle dynamics modeling for autonomous racing, paving the way for groundbreaking research and innovations in autonomous racing.

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Section: A Virtual Testing Platformsmentioning

confidence: 99%

A Survey of Vehicle Dynamics Modeling Methods for Autonomous Racing: Theoretical Models, Physical/Virtual Platforms, and Perspectives

Zhang,

Sun,

Wang

et al. 2024

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

show abstract

“…A similar technique was employed by Vesel (2015) who added a 'healing' sub-process to improve performance. Salem et al (2019) reported promising results by coupling evolutionary learning with a fuzzy-logic controller to refine the vehicle control inputs and racing line, resulting in a real-time controller which can drive the vehicle around the circuit based upon learnings from previous iterative experimentation. Balaji et al (2020) used an end-to-end RL approach in real-world experiments to optimise the control inputs for a real (1/18th scale) vehicle from camera imagery.…”

Section: Related Workmentioning

confidence: 99%

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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Real-time optimal trajectory planning for autonomous vehicles and lap time simulation using machine learning

Garlick¹,

Bradley

2021

Vehicle System Dynamics

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Beating uncertainty in racing bot evolution through enhanced exploration and pole position selection

Cited by 5 publications

References 21 publications

A Survey of Vehicle Dynamics Modeling Methods for Autonomous Racing: Theoretical Models, Physical/Virtual Platforms, and Perspectives

A Survey of Vehicle Dynamics Modeling Methods for Autonomous Racing: Theoretical Models, Physical/Virtual Platforms, and Perspectives

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

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