A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles
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Abstract (arXiv)
Connected and automated vehicles (CAVs) are poised to reshape transportation and mobility by replacing humans as the driver and service provider. While the primary stated motivation for vehicle automation is to improve safety and convenience of road mobility, this transformation also provides a valuable opportunity to improve vehicle energy efficiency and reduce emissions in the transportation sector. Progress in vehicle efficiency and functionality, however, does not necessarily translate to net positive environmental outcomes. Here we examine the interactions between CAV technology and the environment at four levels of increasing complexity: vehicle, transportation system, urban system, and society. We find that environmental impacts come from CAV-facilitated transformations at all four levels, rather than from CAV technology directly. We anticipate net positive environmental impacts at the vehicle, transportation system, and urban system levels, but expect greater vehicle utilization and shifts in travel patterns at the society level to offset some of these benefits. Focusing on the vehicle-level improvements associated with CAV technology is likely to yield excessively optimistic estimates of environmental benefits. Future research and policy efforts should strive to clarify the extent and possible synergetic effects from a systems level in order to envisage and address concerns regarding the short- and long-term sustainable adoption of CAV technology.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Jan 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 2; 2025: 1.
7 papers.
- 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
- 2021Unraveling the Hidden Environmental Impacts of AI Solutions for Environmentcited by 32
- 2024Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiencycited by 3
- 2022Sharing Behavior in Ride-hailing Trips: A Machine Learning Inference Approachcited by 1
- 2024A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learningcited by 1
- 2025Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routingcited by 1
- 2023Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestioncited by 0
- 2025Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing18 Feb 2025
- 2024A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learning14 May 2024
- 2024Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiency6 Feb 2024
- 2023Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion16 May 2023
- 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys30 Mar 2023
- 2022Sharing Behavior in Ride-hailing Trips: A Machine Learning Inference Approach30 Jan 2022
- 2021Unraveling the Hidden Environmental Impacts of AI Solutions for Environment22 Oct 2021