IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12409-d929308.html
   My bibliography  Save this article

Security Issues and Solutions for Connected and Autonomous Vehicles in a Sustainable City: A Survey

Author

Listed:
  • Zhendong Wang

    (Department of ECE, The University of Texas at Dallas, Richardson, TX 75080, USA)

  • Haoran Wei

    (Department of ECE, The University of Texas at Dallas, Richardson, TX 75080, USA)

  • Jianda Wang

    (Department of ECE, The University of Texas at Dallas, Richardson, TX 75080, USA)

  • Xiaoming Zeng

    (Department of ECE, The University of Texas at Dallas, Richardson, TX 75080, USA)

  • Yuchao Chang

    (Department of Electronic Engineering, School of Electronic Information and Electrical Engineering, Minhang Campus, Shanghai Jiaotong University, Shanghai 200233, China)

Abstract

Connected and Autonomous Vehicles (CAVs) combine technologies of autonomous vehicles (AVs) and connected vehicles (CVs) to develop quicker, more reliable, and safer traffic. Artificial Intelligence (AI)-based CAV solutions play significant roles in sustainable cities. The convergence imposes stringent security requirements for CAV safety and reliability. In practice, vehicles are developed with increased automation and connectivity. Increased automation increases the reliance on the sensor-based technologies and decreases the reliance on the driver; increased connectivity increases the exposures of vehicles’ vulnerability and increases the risk for an adversary to implement a cyber-attack. Much work has been dedicated to identifying the security vulnerabilities and recommending mitigation techniques associated with different sensors, controllers, and connection mechanisms, respectively. However, there is an absence of comprehensive and in-depth studies to identify how the cyber-attacks exploit the vehicles’ vulnerabilities to negatively impact the performance and operations of CAVs. In this survey, we set out to thoroughly review the security issues introduced by AV and CV technologies, analyze how the cyber-attacks impact the performance of CAVs, and summarize the solutions correspondingly. The impact of cyber-attacks on the performance of CAVs is elaborated from both viewpoints of intra-vehicle systems and inter-vehicle systems. We pointed out that securing the perception and operations of CAVs would be the top requirement to enable CAVs to be applied safely and reliably in practice. Additionally, we suggested to utilize cloud and new AI methods to defend against smart cyber-attacks on CAVs.

Suggested Citation

  • Zhendong Wang & Haoran Wei & Jianda Wang & Xiaoming Zeng & Yuchao Chang, 2022. "Security Issues and Solutions for Connected and Autonomous Vehicles in a Sustainable City: A Survey," Sustainability, MDPI, vol. 14(19), pages 1-29, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12409-:d:929308
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12409/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12409/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hoppe, Tobias & Kiltz, Stefan & Dittmann, Jana, 2011. "Security threats to automotive CAN networks—Practical examples and selected short-term countermeasures," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 11-25.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xingyu Li & Ruifeng Li & Yanchen Liu, 2024. "HP-LSTM: Hawkes Process–LSTM-Based Detection of DDoS Attack for In-Vehicle Network," Future Internet, MDPI, vol. 16(6), pages 1-21, May.
    2. Haoran Wei & Zhendong Wang & Yuchao Chang & Zhenghua Huang, 2022. "Introducing the Special Issue on Artificial Intelligence Applications for Sustainable Urban Living," Sustainability, MDPI, vol. 14(20), pages 1-4, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tulika Saha & Sriparna Saha & Pushpak Bhattacharyya, 2020. "Towards sentiment aided dialogue policy learning for multi-intent conversations using hierarchical reinforcement learning," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-28, July.
    2. Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
    3. Imen Azzouz & Wiem Fekih Hassen, 2023. "Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach," Energies, MDPI, vol. 16(24), pages 1-18, December.
    4. Jacob W. Crandall & Mayada Oudah & Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael A. Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
      • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," TSE Working Papers 17-806, Toulouse School of Economics (TSE).
      • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," IAST Working Papers 17-68, Institute for Advanced Study in Toulouse (IAST).
      • Jacob Crandall & Mayada Oudah & Fatimah Ishowo-Oloko Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Post-Print hal-01897802, HAL.
    5. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    6. Yassine Chemingui & Adel Gastli & Omar Ellabban, 2020. "Reinforcement Learning-Based School Energy Management System," Energies, MDPI, vol. 13(23), pages 1-21, December.
    7. Woo Jae Byun & Bumkyu Choi & Seongmin Kim & Joohyun Jo, 2023. "Practical Application of Deep Reinforcement Learning to Optimal Trade Execution," FinTech, MDPI, vol. 2(3), pages 1-16, June.
    8. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    9. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    10. Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
    11. Michelle M. LaMar, 2018. "Markov Decision Process Measurement Model," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 67-88, March.
    12. Zichen Lu & Ying Yan, 2024. "Temperature Control of Fuel Cell Based on PEI-DDPG," Energies, MDPI, vol. 17(7), pages 1-19, April.
    13. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    14. Wang, Xuan & Shu, Gequn & Tian, Hua & Wang, Rui & Cai, Jinwen, 2020. "Operation performance comparison of CCHP systems with cascade waste heat recovery systems by simulation and operation optimisation," Energy, Elsevier, vol. 206(C).
    15. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    16. Parvez Farazi, Nahid & Zou, Bo & Tulabandhula, Theja, 2022. "Dynamic On-Demand Crowdshipping Using Constrained and Heuristics-Embedded Double Dueling Deep Q-Network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    17. Louback, Eduardo & Biswas, Atriya & Machado, Fabricio & Emadi, Ali, 2024. "A review of the design process of energy management systems for dual-motor battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    18. Brammer, Janis & Lutz, Bernhard & Neumann, Dirk, 2022. "Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 299(1), pages 75-86.
    19. Neha Soni & Enakshi Khular Sharma & Narotam Singh & Amita Kapoor, 2019. "Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models," Papers 1905.02092, arXiv.org.
    20. Tri-Hai Nguyen & Laihyuk Park, 2023. "HAP-Assisted RSMA-Enabled Vehicular Edge Computing: A DRL-Based Optimization Framework," Mathematics, MDPI, vol. 11(10), pages 1-23, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12409-:d:929308. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.