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Cited by 169Autonomous Vehicle Technology and SafetyTraffic and Road SafetyVehicular Ad Hoc Networks (VANETs)

A Survey of Driving Safety With Sensing, Vehicular Communications, and Artificial Intelligence-Based Collision Avoidance

Fu, Yuchuan, Li, Changle, Yu, Fei Richard, Luan, Tom H., Zhang, Yao·2022·IEEE Transactions on Intelligent Transportation Systems
DOI 10.1109/tits.2021.3083927
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Abstract

Accurately discovering hazards and issuing appropriate warnings to drivers in advance or performing autonomous control is the core of the Collision Avoidance (CA) system used to solve traffic safety problems. More comprehensive environmental awareness, diversified communication technologies, and autonomous control can make the CA system more accurate and effective, thereby improving driving safety. In addition, the assistance of Artificial Intelligence (AI) technology can make the CA system adapt to the environment and facilitate fast and accurate decisions. Considering the current lack of a thorough survey of driving safety with sensing, vehicular communications, and AI-based collision avoidance, in this paper, we survey existing researches for state-of-the-art data-driven CA techniques. Firstly, we discuss the major steps of CA and key research issues. For each step, we review the existing enabling techniques and research methods for CA in detail, including sensing and vehicular communication for safe driving, as well as CA algorithm design. Particularly, we present a comparison between the most common AI algorithms for different functions in the CA system. Testbeds and projects for CA are summarized next. Finally, several open challenges and future research directions are also outlined.

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Crossref · 10.1109/tits.2021.3083927

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  • V2X classification can promote traffic safety by enabling real-time communication and information sharing between vehicles, providing rich traffic status information, and enhancing driver awareness and responsiveness.

    “Promote traffic safety: V2X can achieve real-time communication and information sharing between vehicles, provide rich traffic status information, and enhance driver's awareness and responsiveness.Through V2X classification, different types of traffic participants can obtain the required information according to their own needs, thus forming a more efficient and safer transportation system [17].”

    From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [17]

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Authors
Fu, Y., Li, C., Yu, F. R., Luan, T. H., & Zhang, Y.
Affiliations
  • Xidian University (CN)
  • Carleton University (CA)
ORCID
Venue
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022-07
Volume
23
Issue
7
Pages
6142-6163
ISSN
  • 1524-9050 (ISSN-L)
  • 1558-0016
DOI
10.1109/tits.2021.3083927
OpenAlex
W3172086939
Cited by
169
References
4
Citations by year
2026: 27 · 2025: 49 · 2024: 46 · 2023: 23 · 2022: 20 · 2021: 4
FWCI
7.55
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
Funders
  • National Key Research and Development Program of China (2020YFB1807700)
  • National Natural Science Foundation of China (U1801266)
  • Youth Innovation Team of Shaanxi Universities
  • Fundamental Research Funds for the Central Universities (JB210102)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
Keywords
collision avoidance, driving safety, vehicular sensing, vehicular communication, artificial intelligence, threat detection, driver warning systems, autonomous driving, collision avoidance algorithm, testbed

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