Multi-Sensor Fusion in Automated Driving: A Survey
Open accessAbstract
With the significant development of practicability in deep learning and the ultra-high-speed information transmission rate of 5G communication technology will overcome the barrier of data transmission on the Internet of Vehicles, automated driving is becoming a pivotal technology affecting the future industry. Sensors are the key to the perception of the outside world in the automated driving system and whose cooperation performance directly determines the safety of automated driving vehicles. In this survey, we mainly discuss the different strategies of multi-sensor fusion in automated driving in recent years. The performance of conventional sensors and the necessity of multi-sensor fusion are analyzed, including radar, LiDAR, camera, ultrasonic, GPS, IMU, and V2X. According to the differences in the latest studies, we divide the fusion strategies into four categories and point out some shortcomings. Sensor fusion is mainly applied for multi-target tracking and environment reconstruction. We discuss the method of establishing a motion model and data association in multi-target tracking. At the end of the paper, we analyzed the deficiencies in the current studies and put forward some suggestions for further improvement in the future. Through this investigation, we hope to analyze the current situation of multi-sensor fusion in the automated driving process and provide more efficient and reliable fusion strategies.
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Most existing approaches combine RGB data with infrared images or 3D LiDAR points to improve the sensitivity of perception systems.
“Most existing approaches combine RGB data with infrared images or 3D LiDAR points [164] to improve the sensitivity of perception systems, which can be suitable to all conditions and scenarios.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [164]
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Flagged first, then by citations
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Sorted by citations
DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection
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- Authors
- Wang, Z., Wu, Y., & Niu, Q.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 2847-2868
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2996945478
- Cited by
- 526
- References
- 159
- Citations by year
- 2026: 75 · 2025: 96 · 2024: 110 · 2023: 110 · 2022: 71 · 2021: 45 · 2020: 18 · 2018: 1
- FWCI
- 15.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61404130113)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- multi-sensor fusion, autonomous driving, radar, LiDAR, camera, ultrasonic sensors, GPS, inertial measurement unit, vehicle-to-everything communication, Internet of Vehicles, multi-target tracking, data association, motion model, environment reconstruction, vehicle perception
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