Path following control for autonomous formula racecar: Autonomous formula student competition

Ni, Jun; Hu, Jibin

doi:10.1109/ivs.2017.7995972

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…Prior literature in high-performance autonomy has largely focused on controls [7,24,28,34], planning [47,48], fullvehicle integration [9,44], or multi-sensor fusion [21]. This paper extends these works to the perception domain, solving challenges pertaining to full-stack visual perception system.…”

Section: Related Workmentioning

confidence: 97%

“…Autonomous racing presents a unique opportunity to test commonly-applied, safety-critical perception and autonomy algorithms in extreme situations at the limit of vehicle handling. As such, work on autonomous race cars has seen a strong uptick since the 2004 DARPA Challenge [43], from both industry [5,9] and academia [21,24,25,34,44,48]. In recent years, autonomous vehicles have grown increasingly reliant on camera-based perception [12,28], due to the sensor modality's low cost and high information density.…”

Section: Introductionmentioning

confidence: 99%

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Accurate, Low-Latency Visual Perception for Autonomous Racing:Challenges, Mechanisms, and Practical Solutions

Strobel¹,

Zhu²,

Chang³

et al. 2020

Preprint

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Autonomous racing provides the opportunity to test safety-critical perception pipelines at their limit. This paper describes the practical challenges and solutions to applying state-of-the-art computer vision algorithms to build a lowlatency, high-accuracy perception system for DUT18 Driverless (DUT18D), a 4WD electric race car with podium finishes at all Formula Driverless competitions for which it raced. The key components of DUT18D include YOLOv3-based object detection, pose estimation, and time synchronization on its dual stereovision/monovision camera setup. We highlight modifications required to adapt perception CNNs to racing domains, improvements to loss functions used for pose estimation, and methodologies for sub-microsecond camera synchronization among other improvements. We perform a thorough experimental evaluation of the system, demonstrating its accuracy and low-latency in real-world racing scenarios.

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Section: Related Workmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Accurate, Low-Latency Visual Perception for Autonomous Racing:Challenges, Mechanisms, and Practical Solutions

Strobel¹,

Zhu²,

Chang³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The most famous event in the automobile professional discipline competition is Formula Student China. This competition is sponsored by the China Society of Automotive Engineering and relevant universities and enterprises [9] . The student team in school is required to produce a small electric car or a gasoline-powered car to participate in the competition within one year according to the requirements of the competition rules.…”

Section: Improve Teaching With Discipline Competitionmentioning

confidence: 99%

Exploration on Construction of Automobile Specialty in Application-Oriented Universities

2023

aduhe

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This paper takes the School of Automobile and Transportation of Chengdu Institute of Technology as an example. The main measures and means of building automobile specialty in application-oriented universities are discussed. Specifically, it includes the integration of industry and education, and the joint construction of automobile industry college with local automobile manufacturing enterprises. Promote teaching by competition and actively organize students to participate in the discipline competition of automobile specialty. Promote education reform and introduce new energy vehicles and intelligent connected vehicle related curriculum resources. International exchange, cooperation and exchange with foreign universities on automobile majors. Finally, the problems and deficiencies in the process of specialty construction are analyzed. These above can provide reference and reference for brother colleges in similar situations to develop automobile majors.

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Accurate, Low-Latency Visual Perception for Autonomous Racing: Challenges, Mechanisms, and Practical Solutions

Strobel

Zhu

Chang

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Path following control for autonomous formula racecar: Autonomous formula student competition

Cited by 9 publications

References 14 publications

Accurate, Low-Latency Visual Perception for Autonomous Racing:Challenges, Mechanisms, and Practical Solutions

Accurate, Low-Latency Visual Perception for Autonomous Racing:Challenges, Mechanisms, and Practical Solutions

Exploration on Construction of Automobile Specialty in Application-Oriented Universities

Accurate, Low-Latency Visual Perception for Autonomous Racing: Challenges, Mechanisms, and Practical Solutions

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