Accelerating Your Mobility Transformation.

Tomorrow Mobility Solutions is dedicated to driving the adoption of sustainable and intelligent transport systems. Our insights cut through the hype to focus on the technical, economic, and operational realities of Automated Mobility.

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On this page you can find basic information about Automated Mobility and how it can tackle your challenges.

This is just the beginning. To understand how these principles apply to your specific challenges and to start building a low-risk, high-impact deployment strategy, schedule a consultation with us.

What is Automated Mobility?

The term Automated Mobility describes the use of an advanced computer system to operate a vehicle. While some refer to these as “autonomous” or “self-driving” vehicles, the Society of Automotive Engineers recommends the term “Automated Vehicle” for greater accuracy. This is because the technology only automates the driving task itself; it does not make the vehicle truly independent (or in other words, autonomous). Instead, human supervision remains a crucial component. At its core, the technology works by replicating a human driver’s ability to perceive, interpret, and act, ensuring safe operation.

Automated driving can take many forms, including parcel delivery robots, robotaxis, and automated buses. Different regions of the world have focused on developing this technology in different ways. The United States, for example, has prioritized robotaxis, with companies like Waymo and Zoox leading the way with large-scale deployments.

In Europe, the focus has been on developing automated vehicles for public transportation and ride-sharing. These applications are seen as a better way to reduce emissions and the total number of vehicles on the road, making them a more environmentally friendly option. Asia has adopted a hybrid model, implementing automated driving systems in both taxis and small buses.

The SAE levels of driving automation

To bring a common language to the field of automated vehicles, SAE International established the J3016 standard, which is the industry’s most-cited source for driving automation. This framework provides a taxonomy with detailed definitions for six levels of driving automation, from Level 0 (No Driving Automation) to Level 5 (Full Driving Automation).

A crucial distinction within these levels is the difference between Driver Support Systems and Automated Driving Systems.

Levels 0-2 are considered Driver Support Systems. In these levels, the human driver is constantly engaged in monitoring and performing the Dynamic Driving Task, even if their hands or feet are off the controls. The systems assist the driver, but the human is ultimately in charge.
Levels 3-5 are considered Automated Driving Systems. At these levels, the technology itself performs the entire DDT when engaged, and you are not considered to be driving, even if you are in the driver’s seat.

When discussing automated vehicles for public transport and commercial operations, the focus is typically on the higher levels of automation, particularly Levels 4 and 5.

Level 4 is the first level that doesn’t require a human driver to take over in real time, making it possible to remove the driver from the vehicle entirely. However, since these vehicles aren’t and can’t be fully independent, a remote supervisor is necessary to oversee their operation. By replacing the driver with a single supervisor, it’s possible to scale operations without a proportional increase in costs, as one supervisor can monitor multiple vehicles. The exact number of vehicles per supervisor is case-specific, but the general consensus is that a two-person team can supervise 10 to 15 vehicles simultaneously in some scenarios.

It’s important to distinguish between the two highest levels of automation. A Level 5 vehicle could in theory operate under any conditions a trained human could, while a Level 4 vehicle is limited by its Operational Design Domain (which is discussed in the next article). The majority of companies developing automated driving systems are currently focused on Level 4 systems, as Level 5 capabilities are not considered achievable within the near future.

The Operational Design Domain

When discussing Automated Vehicles and their technical capabilities, the conversation almost always concentrates on the SAE Level of driving automation. The focus is exclusively on the question of “How” the vehicle can be driven or operated (e.g., Level 4 autonomy).

However, while the SAE level is a necessary foundation, it only describes one part of the system’s function. Far too often, the Operational Design Domain (often referred to as the ODD) the explicit limits of the system’s safe operation, is overlooked or given only a vague, high-level description: “Operates on open roads in urban environments.”

The ODD tells you where you can operate

To truly understand a vehicle’s capabilities in a meaningful way, we must answer a second, equally critical question: “Where” or “In which circumstances” can the Automated Driving System can operate safely?

This attribute is defined by the Operational Design Domain. It is a technical, formal specification that outlines the environmental and operational boundaries within which the Automated Driving System is designed to operate without human intervention.

The ODD is a way of describing what elements can be present or adjacent in the environment and under what environmental conditions the vehicle is designed to operate safely. These elements are broadly categorized as:

Scenery Elements: The static components of the environment (e.g., road type, lane configuration, adjacent buildings).
Dynamic Elements: The moving elements (e.g., traffic agents, including vehicles and vulnerable road users).
Environmental Conditions: The atmospheric and ground conditions (e.g., time of day, weather, conditions on the road surface).

The Details That Matters for Public Transport

For a Public Transport Operator, acquiring an Automated Vehicle is often a commitment to deliver a reliable service on a fixed schedule. To make the right technology decision, you have to understand the capabilities of your vehicle in detail. You need to know if the vehicle matches our specific use case, environment, and customer needs.

A rigorous, deployable ODD must be quantitative and highly detailed, often consisting of hundreds, rather than just tens, of specific constraints. For example:

Weather: Maximum rainfall the system can handle (e.g., X mm/hour).
Ground Conditions: Acceptable snow depth on the ground (e.g., Y cm); Maximum ice/slippery friction coefficient.
Infrastructure: Supported intersection types (e.g., type X, Y, and Z); Minimum lane width or drivable area (XX meters).

The SAE Level tells a buyer how the vehicle is operated; the ODD tells them where it can deliver the committed public service. Focusing on the ODD is the necessary for completing successful, low-risk deployments.

Benefits of Automated Public Transport

The introduction of Automated Vehicles is not just a technical upgrade; it has the potential of bringing a fundamental solution to many of the long-standing challenges facing public transport. When implemented right, AVs can deliver three core benefits: economic viability, a solution to the driver shortage, and improved urban service levels.

Economic Viability and fleet optimization

Achieving true economic viability requires directly addressing the primary variables of public transport operations: maximizing vehicle utilization and minimizing high labor costs.

Lower Operating Costs: The most significant long-term saving comes from the removal of the driver’s salary, which can constitute up to 70% of a vehicle’s operating costs. This is partially replaced by the cost of the technology and the remote supervision of the vehicles, but when scaled, automated operation significantly reduces costs.
Optimized Fleet Utilization: AVs can operate more flexibly, running for longer periods without the statutory breaks and shift limits required by human personnel. This increases fleet utilization and improves overall service availability.

Solution to Driver Shortages

The public transport industry globally faces an accelerating shortage of qualified drivers due to factors like aging workforces, high training costs, and competitive labor markets. Reports indicate a significant shortfall, with the IRU (International Road Transport Union) estimating over 100,000 unfilled bus and coach driver positions across Europe alone, a number projected to grow significantly. By moving from a “one-driver-per-vehicle” model to a “one remote-supervisor-to-many-vehicles” model, automation shifts the dependency away from human resource. This strategic change ensures service continuity and allows operators to scale their fleets based on demand, rather than being constrained by available personnel.

By lowering the cost to providing service, AVs can enable the expansion of public transport networks, particularly in underserved areas.

Improved Service Level and Accessibility

First and Last Mile Connectivity: Small automated shuttles can efficiently connect residential and commercial areas to major transport hubs (rail, metro stations). This addresses the “first and last mile” problem, making public transport a more attractive and viable option than a private car.
Flexible and Demand-Responsive Transport (DRT): Automation enables routes and schedules to be dynamically adjusted based on real-time user demand (via app bookings). This offers better service in sparsely populated areas or during off-peak hours when fixed-route services are economically unsustainable.
More Comprehensive Networks: The cost-effectiveness of AVs allows Public Transport Authorities to launch service expansion to areas where traditional bus lines are simply not profitable, increasing social equity across the city.

Ready to Move from Insight to Action?

Understanding the technology is only the first step. A successful automated mobility project requires a clear strategy, a comprehensive analysis of your specific Operational Environment, and a realistic operating model.

We are here to help you navigate the complexities and build a clear roadmap for your deployments.

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