Autonomous driving technology promises to reshape freight logistics by enabling higher utilisation, smoother driving patterns, and reduced labour costs. For electric trucks in particular, self-driving systems can influence energy efficiency through more consistent speeds, reduced harsh acceleration, and optimised routing. At the same time, additional sensing and computing hardware consumes power and adds weight. At Energy Solutions, we quantify how autonomous operation affects EV truck energy use, TCO, and corridor-level decarbonisation strategies.
Autonomous freight concepts range from advanced driver assistance (ADAS) to Level 4 self-driving on defined corridors. Hardware stacks typically include cameras, radar, LIDAR (in some designs), and powerful onboard computing platforms. For EV trucks, these systems are integrated with traction inverters and battery management to coordinate smooth driving.
Energy Solutions models use representative duty cycles and driving profiles to estimate baseline and autonomous EV truck energy use. We account for smoother driving, reduced idling, and additional auxiliary loads from autonomy hardware, and we examine long-haul and regional scenarios.
Autonomous driving affects energy use through two opposing effects: smoother driving (reducing consumption) and additional hardware loads (increasing consumption). In most cases, the net effect is a modest efficiency improvement when combined with good routing.
| Scenario | Driving Profile | Traction Energy (kWh/100 km) | Autonomy Hardware Energy (kWh/100 km) | Total (kWh/100 km) |
|---|---|---|---|---|
| Human-driven, baseline highway | Mixed speeds, some harsh accelerations | 125 | 0 | 125 |
| Autonomous, optimised highway | Smoother speed control, eco-driving | 112 | 3 | 115 |
| Human-driven, regional/urban mix | Stop–go, variable driving style | 150 | 0 | 150 |
| Autonomous, regional/urban mix | More consistent speeds, reduced idling | 135 | 4 | 139 |
Source: Energy Solutions modelling; hardware loads include sensing and computing only.
A major promise of autonomous freight is enabling trucks to operate more hours per day with limited human supervision. For electric trucks, higher utilisation has several implications:
Source: Energy Solutions duty cycle analysis; values represent illustrative averages.
Autonomy affects TCO through multiple channels:
| Component | Human-Driven EV Truck | Autonomous EV Truck (Corridor-Limited) |
|---|---|---|
| Vehicle capex | 2.0–2.3× diesel | 2.3–2.7× diesel (autonomy premium) |
| Energy cost | 1.0 (index) | 0.9–0.95 (efficiency gains) |
| Labour cost | 1.0 | 0.4–0.8 (depends on supervision model) |
| Total TCO | 1.3–1.6× diesel | 1.1–1.5× diesel (wide uncertainty) |
Source: Energy Solutions modelling; assumes hydrogen or grid power at mid-2030 prices for comparison.
Automation might lower the cost of moving goods to the point that it induces additional freight demand, offsetting some of the efficiency gains. Cheaper logistics could encourage longer supply chains, more frequent deliveries, or increased empty running.
There are also broader system questions: who benefits from the labour savings, and how are workers in driving roles supported during the transition? If automation is pursued mainly to reduce labour costs without an integrated decarbonisation strategy, the result could be more freight activity with modest climate benefits.
By 2030, autonomous capabilities are likely to be gradually deployed on specific highway corridors and in depots. By 2035, combinations of autonomous operation and electrification could materially change the cost and energy profile of long-haul freight, especially in regions with well-developed charging infrastructure and favourable regulation.
| Scenario | EV Trucks with Advanced ADAS (%) | EV Trucks with Highway Autonomy (%) | Fully Autonomous EV Trucks (Selected Corridors) (%) |
|---|---|---|---|
| Conservative autonomy | 60–70 | 10–20 | 0–5 |
| Base case | 70–80 | 20–30 | 5–10 |
| Autonomy-forward | 80–90 | 25–35 | 10–20 |
Source: Energy Solutions autonomous freight scenarios; shares expressed as share of EV truck stock.
Published estimates suggest autonomous operations can improve energy efficiency by 25% to 32% versus conventional trucking in certain duty cycles, mainly through smoother speed control, reduced harsh acceleration and braking, and improved routing. Results vary by corridor design, traffic, and how autonomy is implemented.
Automation can improve efficiency for all powertrains, but the cost impact is particularly important for energy- intensive fuels such as hydrogen and for expensive battery packs. Saving 10% of energy on an EV or FCEV truck can materially improve TCO.
Not automatically. While autonomy can reduce energy use per kilometre, overall emissions also depend on fuel type, electricity carbon intensity, and total freight demand. Without a strong shift to low-carbon fuels and electricity, efficiency gains alone may not be enough to meet climate targets.