Autonomous Vehicles for Smart and Sustainable Cities: An In-Depth Exploration of Privacy and Cybersecurity Implications
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Abstract (arXiv)
Amidst rapid urban development, sustainable transportation solutions are required to meet the increasing demands for mobility whilst mitigating the potentially negative social, economic, and environmental impacts. This study analyses autonomous vehicles (AVs) as a potential transportation solution for smart and sustainable development. We identified privacy and cybersecurity risks of AVs as crucial to the development of smart and sustainable cities and examined the steps taken by governments around the world to address these risks. We highlight the literature that supports why AVs are essential for smart and sustainable development. We then identify the aspects of privacy and cybersecurity in AVs that are important for smart and sustainable development. Lastly, we review the efforts taken by federal governments in the US, the UK, China, Australia, Japan, Singapore, South Korea, Germany, France, and the EU, and by US state governments to address AV-related privacy and cybersecurity risks in-depth. Overall, the actions taken by governments to address privacy risks are mainly in the form of regulations or voluntary guidelines. To address cybersecurity risks, governments have mostly resorted to regulations that are not specific to AVs and are conducting research and fostering research collaborations with the private sector.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Apr 2018.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 1.
5 papers.
- 2021Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspectivecited by 9
- 2022The Risks of Machine Learning Systemscited by 5
- 2022Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directionscited by 4
- 2022Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in Chinacited by 1
- 2024Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent Drivingcited by 0
- 2024Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent Driving1 Sep 2024
- 2022Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions8 Sep 2022
- 2022Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China22 Apr 2022
- 2022The Risks of Machine Learning Systems21 Apr 2022
- 2021Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective8 Jan 2021