AI and Mobility — Tesla, Autonomous Driving and Smart Transport
Mobility is undergoing a full-scale revolution. Artificial intelligence is no longer just assisting drivers — it is fundamentally redefining the way we travel. From autonomous vehicles to smart public transport, through logistics optimised by machine learning, AI is the engine of an unprecedented transformation. And at the heart of this revolution, one name keeps coming up: Tesla.
Tesla and AI: A Unique Synergy
Full Self-Driving (FSD)
Tesla is arguably the company that has most democratised the idea of autonomous driving. Its Full Self-Driving (FSD) system relies on a radically different approach from its competitors:
- Pure vision: Tesla has abandoned lidars and radars in favour of a system based solely on cameras and neural networks
- Large-scale learning: Every Tesla on the road contributes to model training with billions of kilometres of real-world data
- OTA updates: The software continuously improves through over-the-air updates
- End-to-end neural network: FSD v12+ uses an end-to-end neural network, with no manually coded rules
To follow the latest Tesla advances in autonomous driving and innovation, Tesla-Mag.ch offers comprehensive and up-to-date coverage of the Tesla universe and electric mobility.
The Dojo Supercomputer
Tesla has developed Dojo, one of the most powerful supercomputers in the world, specifically designed for training computer vision models. With computing power of several exaflops, Dojo enables Tesla to process the petabytes of video data collected by its global fleet.
Optimus: AI Beyond the Car
Tesla's humanoid robot Optimus illustrates how AI expertise acquired in autonomous driving extends to other domains. The same perception, planning and decision-making technologies apply to general robotics — a sign that Tesla is transforming into an AI and robotics company.
Levels of Autonomous Driving
The SAE Classification
The industry uses the SAE International classification to define autonomy levels:
| Level | Description | 2025 Examples | |-------|-------------|---------------| | 0 | No automation | Conventional vehicles | | 1 | Driver assistance | Adaptive cruise control | | 2 | Partial automation | Tesla Autopilot, GM Super Cruise | | 2+ | Advanced automation | Tesla FSD Supervised | | 3 | Conditional automation | Mercedes Drive Pilot | | 4 | High automation | Waymo (geofenced areas) | | 5 | Full automation | Not yet available |
Where Are We in 2025?
In 2025, autonomous driving has reached a tipping point:
- Waymo (Alphabet/Google) operates commercial robotaxis in several American cities
- Tesla is deploying supervised FSD at scale and testing unsupervised mode
- Mercedes-Benz is the first manufacturer to obtain Level 3 certification in Europe
- Baidu and Pony.ai operate autonomous fleets in China
- Cruise (GM) is restructuring its approach following incidents in 2024
AI in Public Transport
Smart Trains and Metros
Public transport networks are massively adopting AI:
- Autonomous metros: More than 60 cities worldwide operate driverless metro lines
- Rail traffic management: AI optimises schedules and reduces delays by 20 to 30%
- Predictive maintenance: Sensors and machine learning detect failures before they occur
- Passenger experience: Real-time information, dynamic pricing, improved accessibility
Autonomous Buses and Shuttles
Several European cities are experimenting with autonomous shuttles for last-mile transport:
- Zurich is testing autonomous minibuses in certain neighbourhoods
- Helsinki is deploying AI shuttles in underserved areas
- Singapore operates a fleet of autonomous buses on regular routes
Switzerland, a Pioneer in Smart Transport
Switzerland, with its public transport network among the best in the world, integrates AI at every level. SBB uses machine learning to optimise the punctuality — already record-breaking — of its trains. For a complete view of the Swiss AI ecosystem, see our article on AI in Switzerland 2025.
AI-Powered Logistics and Delivery
Autonomous Delivery Fleets
Last-mile logistics is being transformed by AI:
- Delivery robots: Starship Technologies, Nuro and others are deploying autonomous delivery vehicles
- Delivery drones: Amazon Prime Air and Wing (Alphabet) operate in selected areas
- Route optimisation: AI reduces kilometres driven and CO2 emissions
- Warehouse management: Amazon Kiva robots and their equivalents are reorganising logistics
Platooning: Intelligent Convoys
Platooning — convoys of trucks connected by AI — promises major efficiency gains:
- Fuel consumption reduction of 10 to 15% thanks to slipstreaming
- Improved road safety with near-instantaneous reaction times
- Road capacity optimisation by reducing distances between vehicles
- First truck driven by a human, the following ones in autonomous mode
Mobility as a Service (MaaS)
AI as Conductor
The Mobility as a Service concept integrates all transport modes into a seamless experience, orchestrated by AI:
- The user specifies their destination
- AI calculates the optimal route combining walking, cycling, bus, train, carpooling
- The system automatically books and pays for each segment
- In case of disruption, AI recalculates in real time an alternative
MaaS Applications in 2025
- Whim (Helsinki): Monthly subscription covering all transport
- Jelbi (Berlin): App integrating bus, metro, bike, scooter and taxi
- SBB Green Class (Switzerland): Combination of train + electric car + bicycle
Challenges of AI Mobility
Safety and Reliability
Safety remains the number one challenge. An autonomous vehicle must be significantly safer than a human driver to be accepted by the public. Building reliable and trustworthy AI systems is a cross-cutting challenge, analysed in depth by the experts at Trustly AI.
Statistics show that autonomous vehicles are already safer than human drivers under controlled conditions, but edge cases (extreme weather conditions, unprecedented situations) remain a challenge.
Regulation and Liability
In the event of an accident involving an autonomous vehicle, who is liable?
- The vehicle manufacturer?
- The AI software developer?
- The vehicle owner?
- The passenger supervising the system?
Europe, the United States and China are advancing at different speeds on these legal questions. The European AI Act provides initial answers.
Infrastructure and Connectivity
AI mobility requires adapted infrastructure:
- Dense 5G coverage for vehicle-to-infrastructure communication (V2X)
- Smart roads equipped with sensors and communication terminals
- HD mapping in real time, continuously updated
- Smart charging stations for autonomous electric fleets
Environmental Impact
AI can contribute significantly to transport decarbonisation:
- Optimised driving reducing energy consumption by 15 to 25%
- Smart carpooling increasing vehicle occupancy rates
- Optimised logistics reducing empty kilometres
- Predictive maintenance extending vehicle lifespan
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Outlook: Mobility in 2030
By 2030, the mobility landscape will be radically different:
- Robotaxis will be available in major cities worldwide
- Platooning will be widespread on European and American motorways
- Urban air mobility (flying taxis) will begin commercial operations
- Digital twins of cities will enable real-time traffic management
AI is not just improving existing mobility — it is reinventing its foundations. The convergence of electric vehicles, autonomous driving, sharing and connectivity paints a future where transport will be safer, cleaner and more accessible. And Tesla, with its integrated software-hardware-AI approach, is positioned to play a central role in this transformation. For an American perspective on the ecosystem fuelling this revolution, see our article on Silicon Valley and AI.
Also read: AI and sustainable energy and our guide on Edge AI and IoT architecture.