Self-driving vehicles could cut transport energy use by 15%
Robotaxis, automated shuttles, high-frequency autonomous buses, and self-driving cars on highways may be closer than we think. According to a study by the European Commission’s Joint Research Centre (JRC), technological advances and smarter system optimization could slash the additional energy consumption of autonomous driving by over 80% compared to current prototypes. Combined with shared mobility services and restrictive policies on private car use, this could lead to a 15% cut in overall energy consumption in one of the world’s most polluting sectors: transportation.
A complex energy equation
Many studies suggest that Connected, Cooperative, and Automated Mobility (CCAM) services in cities and on highways can reduce traffic congestion and improve accessibility. But there’s a catch: they demand significant energy resources. In its report “On the Energy Intensity of Road Transport in the Presence of Connected and Automated Mobility,” the JRC assessed a variety of automated transport options to tackle this looming energy surge.
The higher consumption of self-driving vehicles doesn’t come just from propulsion. It also stems from the tech that enables autonomy: sensors, onboard computing, wireless communication, live map updates, video streaming, and data center connectivity. These vehicles are constantly online, exchanging real-time information with smart city systems, infrastructure, and other vehicles. This data-heavy ecosystem, researchers estimate, could account for up to 18% of total transport-related energy use.
What the simulations reveal
Simulations show that today’s autonomous vehicle prototypes consume as much additional energy for automation as they do for propulsion. However, next-generation designs—with more efficient hardware, streamlined sensor setups, AI-powered decision-making, and energy-optimized onboard electronics—could dramatically cut energy demand.
Highway or city streets?
The study looked at two distinct environments: highways and urban areas. On highways, automation performs better. Under optimal conditions, autonomous vehicles drive more smoothly, avoiding abrupt braking and acceleration. This reduces congestion and improves traffic flow. Even a small number of self-driving cars can bring benefits, provided their algorithms are built to anticipate and cooperate with surrounding traffic.
Urban roads tell a different story. Simulations based on the Spanish city of Santander show that when private autonomous vehicles replace trips previously made by walking, biking, or public transport, a modal shift occurs. This increases overall mileage and energy use. In contrast, shared autonomous shuttles and buses offer more sustainable outcomes by enhancing accessibility, cutting private car use, and supporting active transportation modes.
Less stress, lower costs, greener travel
Unlike basic automation systems that lack traffic coordination, modern “traffic-aware” self-driving systems can boost energy efficiency by smoothing traffic flow, reducing stops and starts, and maintaining optimal distances between vehicles.
In practice, drivers could spend less time idling at red lights or stuck in traffic. This improves the travel experience and saves money by reducing wasted energy. But for these gains to materialize, vehicles must be designed with real-world traffic dynamics in mind. Poorly optimized automation, even in otherwise efficient vehicles, could still increase energy demand.
Shared mobility is key
The outcome hinges on how shared mobility is implemented in cities. Widespread use of private robotaxis, for example, could increase overall transportation energy demand by as much as 80% if they replace walking or cycling. In contrast, automated high-frequency shuttles and buses present a more energy-efficient alternative.
Ultimately, it’s not just about deploying advanced autonomous technologies. Urban planning and mobility policies must align to reduce energy consumption and create a more sustainable transportation ecosystem.