Autonomous Drifting like Professional Racing Drivers: A Survey
Autonomous drifting is an advanced technique that enhances vehicle maneuverability beyond conventional driving limits. This survey provides a comprehensive, systematic review of autonomous drifting research published between 2005 and early 2025, analyzing approximately 80 peer-reviewed studies. We e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | AppliedMath |
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| Online Access: | https://www.mdpi.com/2673-9909/5/2/33 |
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| author | Yang Liu Fulong Ma Xiaodong Mei Bohuan Xue Jin Wu Chengxi Zhang |
| author_facet | Yang Liu Fulong Ma Xiaodong Mei Bohuan Xue Jin Wu Chengxi Zhang |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | Autonomous drifting is an advanced technique that enhances vehicle maneuverability beyond conventional driving limits. This survey provides a comprehensive, systematic review of autonomous drifting research published between 2005 and early 2025, analyzing approximately 80 peer-reviewed studies. We employed a modified PRISMA approach to categorize and evaluate research across two main methodological frameworks: dynamical model-based approaches and deep learning techniques. Our analysis reveals that while dynamical methods offer precise control when accurately modeled, they often struggle with generalization to unknown environments. In contrast, deep learning approaches demonstrate better adaptability but face challenges in safety verification and sample efficiency. We comprehensively examine experimental platforms used in the field—from high-fidelity simulators to full-scale vehicles—along with their sensor configurations and computational requirements. This review uniquely identifies critical research gaps, including real-time performance limitations, environmental generalization challenges, safety validation concerns, and integration issues with broader autonomous systems. Our findings suggest that hybrid approaches combining model-based knowledge with data-driven learning may offer the most promising path forward for robust autonomous drifting capabilities in diverse applications ranging from motorsports to emergency collision avoidance in production vehicles. |
| format | Article |
| id | doaj-art-5431b0bd8fb04229abd9174297abbcf4 |
| institution | Kabale University |
| issn | 2673-9909 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AppliedMath |
| spelling | doaj-art-5431b0bd8fb04229abd9174297abbcf42025-08-20T03:32:28ZengMDPI AGAppliedMath2673-99092025-03-01523310.3390/appliedmath5020033Autonomous Drifting like Professional Racing Drivers: A SurveyYang Liu0Fulong Ma1Xiaodong Mei2Bohuan Xue3Jin Wu4Chengxi Zhang5Department of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong KongRobotics and Autonomous Systems, The Hong Kong University of Science and Technology (Guangzhou), No. 1 Du Xue Rd, Nansha District, Guangzhou 511466, ChinaDepartment of Computer Science & Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong KongDepartment of Computer Science & Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong KongDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong KongSchool of Internet of Things Engineering, Jiangnan University, Wuxi 214082, ChinaAutonomous drifting is an advanced technique that enhances vehicle maneuverability beyond conventional driving limits. This survey provides a comprehensive, systematic review of autonomous drifting research published between 2005 and early 2025, analyzing approximately 80 peer-reviewed studies. We employed a modified PRISMA approach to categorize and evaluate research across two main methodological frameworks: dynamical model-based approaches and deep learning techniques. Our analysis reveals that while dynamical methods offer precise control when accurately modeled, they often struggle with generalization to unknown environments. In contrast, deep learning approaches demonstrate better adaptability but face challenges in safety verification and sample efficiency. We comprehensively examine experimental platforms used in the field—from high-fidelity simulators to full-scale vehicles—along with their sensor configurations and computational requirements. This review uniquely identifies critical research gaps, including real-time performance limitations, environmental generalization challenges, safety validation concerns, and integration issues with broader autonomous systems. Our findings suggest that hybrid approaches combining model-based knowledge with data-driven learning may offer the most promising path forward for robust autonomous drifting capabilities in diverse applications ranging from motorsports to emergency collision avoidance in production vehicles.https://www.mdpi.com/2673-9909/5/2/33autonomous driftingdeep learningcontrollersdrifting |
| spellingShingle | Yang Liu Fulong Ma Xiaodong Mei Bohuan Xue Jin Wu Chengxi Zhang Autonomous Drifting like Professional Racing Drivers: A Survey AppliedMath autonomous drifting deep learning controllers drifting |
| title | Autonomous Drifting like Professional Racing Drivers: A Survey |
| title_full | Autonomous Drifting like Professional Racing Drivers: A Survey |
| title_fullStr | Autonomous Drifting like Professional Racing Drivers: A Survey |
| title_full_unstemmed | Autonomous Drifting like Professional Racing Drivers: A Survey |
| title_short | Autonomous Drifting like Professional Racing Drivers: A Survey |
| title_sort | autonomous drifting like professional racing drivers a survey |
| topic | autonomous drifting deep learning controllers drifting |
| url | https://www.mdpi.com/2673-9909/5/2/33 |
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