Autonomous Driving System Design for Formula Student Driverless Racecar
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Abstract (arXiv)
This paper summarizes the work of building the autonomous system including detection system and path tracking controller for a formula student autonomous racecar. A LIDAR-vision cooperating method of detecting traffic cone which is used as track mark is proposed. Detection algorithm of the racecar also implements a precise and high rate localization method which combines the GPS-INS data and LIDAR odometry. Besides, a track map including the location and color information of the cones is built simultaneously. Finally, the system and vehicle performance on a closed loop track is tested. This paper also briefly introduces the Formula Student Autonomous Competition (FSAC) in 2017.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Sep 2018.
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Citing papers per year: 2022: 2; 2023: 2.
4 papers.
- 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 56
- 2022Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racingcited by 3
- 2023Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Carcited by 1
- 2023Racing With ROS 2 A Navigation System for an Autonomous Formula Student Race Carcited by 0
- 2023Racing With ROS 2 A Navigation System for an Autonomous Formula Student Race Car24 Nov 2023
- 2023Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car24 Aug 2023
- 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing14 Feb 2022
- 2022Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing10 Jan 2022