In this first article, we consider AVs as a new type of vehicle operator that’s challenging long-standing assumptions, procedures and design standards.

Until now, our vehicles, roadways, regulations, and even parking lots were designed with the assumption that a human is behind the wheel. When that assumption is challenged by technology as the driver, a re-evaluation of the entire system is required.

Automated vehicles (AVs) have the potential to fundamentally transform how we travel and transport goods; however, a transformation of this magnitude requires a significant adjustment period where technical, legal and societal barriers must be identified and addressed before widespread adoption can occur. This shift to AVs is disrupting transportation planning and regulatory frameworks, prompting policymakers and industry leaders to rethink how we design, operate and manage our transportation systems.

Over the past several decades, rapid technological advancements have moved AVs from theoretical concepts to real-world applications.

AVs are already operating in cities and on highways, moving people and goods in various ways. But because our current transportation infrastructure was built for human drivers, AVs — with their distinct capabilities and limitations — are challenging existing planning norms and traffic operations. As AVs continue to evolve, they bring both promise and complexity, requiring proactive planning and collaboration to ensure that safety remains at the core of transportation.

Transportation systems encompass physical infrastructure like roads, bridges, traffic signals, signs and lane markings, as well as vehicles and their operators. These systems also include laws and regulations that govern vehicle safety and operation, which are then enforced by a separate segment of the transportation system. The components of the transportation system have all been developed based on a common set of design assumptions that include vehicles being operated by human drivers who share the road with other humans. The emergence of non-human vehicle operators is now challenging these foundational assumptions.

Who’s driving?

Despite their advanced sensors and computational power, AVs still struggle to understand the dynamic aspects of their surroundings, especially the behavior of other drivers and vulnerable road users like pedestrians. The emerging nature of AV technology means rapid changes that regulation and legislation are not built to handle, and in many cases, there is a desire by regulators to not stifle creativity. However, this leaves a gap between performance and safety verification, and regulation and enforcement. This creates fundamental issues, including:

1. Standards do not exist to guide the development and testing of AVs, or to assess their behavioral competency.
2. When an incident occurs, it can be difficult to determine the root cause, and even AV companies might struggle to determine root cause due to system complexity.
3. AV companies may operate without any specific permission and may use vehicles that would not be legal for humans to drive.

Today’s AV industry has evolved into three distinct sub-industries: robotaxi, transit and goods movement ― each driven by different market dynamics, regulatory bodies and overall function.

While robotaxi developers typically operate in urban/suburban environments moving individuals from one point to another, AV transit developers seek to integrate into existing transit systems to move groups of people between points as either an on-demand service or along a fixed route. AV goods movement developers are moving material instead of people, often with much larger and heavier vehicles to achieve very different objectives like ‘just-in-time’ deliveries. Understanding these sub-industry distinctions enables more tailored insights and strategies that align with unique operational goals, challenges and market conditions.

External data

A continued area of debate and understanding is whether AVs “need” external data to function effectively. While most AVs are designed to operate independently, the integration of external data can significantly enhance their performance. For instance, communication methods using 5.9Ghz CV2X (Cellular Vehicle-to-Everything) and cellular networks enable AVs to share information with other vehicles and infrastructure.

With data sharing, however, comes opportunity and challenge. The opportunity lies in cooperative applications where AVs can harmonize or synchronize their movements and respond to events beyond their own sensors. The challenge lies in trusting the messages (cybersecurity) and a collaborative mindset among AV manufacturers.

Inconsistent regulatory frameworks

The rapid emergence of diverse AV sectors and varying developer approaches has left many regulatory bodies uncertain about how to effectively govern this technology. While regulations are evolving to balance innovation with safety, a standardized method for certifying the behavioral competency or capability maturity of AVs remains absent. This uncertainty is compounded by differing human factors and conflicting policy stances across agencies, resulting in a patchwork of responses. Some jurisdictions support pilot programs and testing, while others have enacted legislation that restricts or bans AV operations. These inconsistencies hinder scalable deployment and complicate cross-jurisdictional integration.

A primary challenge for the transition from human-centric regulatory frameworks to accommodate software-driven AVs is the existence of design requirements that don’t consider software-operated vehicles.

Despite significant technological progress, AVs still face challenges due to their fundamental differences from human drivers. The lack of standardized AV testing or certification for behavioral competency undermines public trust and complicates incident analysis. What’s more, segmenting into three distinct markets introduces multiple layers of regulatory requirements. Another critical consideration is the use of external data sources such as 5.9GHz CV2X and cellular V2X networks, which can enhance situational awareness and coordination but also introduce new regulatory complexities.

Given their potential to disrupt transportation systems and adjacent industries, those looking to integrate AV technology on their roads and highways must first address the very real technical, legal and societal barriers to ensure their safe and effective integration.

For additional information contact paul.avery@archtam.com or edward.stubbing@archtam.com.

In the second article, Navigating the promise and pitfalls of automated vehicles, authors examine how AVs are already disrupting traditional transportation norms, and in the third article, Automated vehicles: Getting ready for what’s next?, the focus shifts to the major AV sub-industries and their unique operational, regulatory and technical requirements.

Together, these articles serve as a foundation for navigating this evolving landscape and offer practical insights for their safe, effective and equitable integration into our existing transportation infrastructure.

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Automated vehicles face significant challenges in operating in a human-centric system. For example, product safety regulations do not allow for vehicles without steering wheels, brake pedals, and other components required by humans to operate a vehicle. So, AVs that are “purpose built” can reimagine how a transportation vehicle looks and drives, but they are not technically legal to operate on public roads. Similarly, commercial vehicle human drivers must submit to inspection points and comply with things like hours-of-service limitations. There are pilot programs in the U.S. with the Federal Motor Carrier Safety Administration to determine how AV freight vehicles might operate under a different set of rules.

AVs are also developed largely within a standards vacuum, with each company developing their own hardware and software systems, reinventing everything from basic processes like efficient sensor data processing and fusion, to more complex functions like predicting the future behavior of objects detected around it. Each company also manages safety and redundancy differently, leading to a varied focus and record of industry safety.

Global and relative localization

One of the most significant examples of the difference among AV developers is a concept called localization. Localization is the process by which an AV determines “where” it is now while tracking where it’s been, allowing it to establish its movements during that timeframe. Typically, the distance and time intervals are very small, increasing the accuracy of the AV to understand its movements and make corrections or changes to trajectory and speed without significant deviations.

There are two types of localization, global and relative. Global localization describes where an AV is on the Earth using GPS coordinates that can be prone to large errors and can swing wildly over short timespans. Relative localization describes where the AV is relative to where it just was, without any reference to a global reference frame. This is accomplished by tracking the precise movement of each wheel and combining that data with the steering angle and an inertial measurement unit.

The most effective approach to localization is to use a mix of global and relative information, and relying on one or the other more heavily depending on the situation.

Unfortunately, it’s much easier to simply rely on GPS as a foundation for the AV decision-making processes; however, this approach is very fragile, and AVs that depend on solid GPS signals will have potentially catastrophic failures when GPS is not available, or severely degraded. This can happen in downtown environments in what’s called an “urban canyon” effect. GPS is also completely lost in tunnels and even under bridges/overpasses, and vehicles that can’t compensate for the loss of stable, accurate GPS data will exhibit behaviors like extreme swerving and emergency braking.

Some say the answer to this is complicated and expensive infrastructure modifications that essentially reproduce GPS data when unavailable from satellites. However, this simply passes the fragility to the infrastructure, and potentially the liability. The solution is not infrastructure aids, but a robust approach to relative localization, which some AV companies are very good at. These companies understand that their vehicles must function competently in the human transportation system, which does not rely on GPS at all for vehicle actuation.

Other technical considerations include:

• Efficient path planning to determine possible future routes or specific paths that maximize performance and/or minimize risk.
• Behavior management to define the different states/provinces where a vehicle can operate and the specific criteria to trigger transitions between them.
• Health monitoring systems on a wide variety of AV systems, subsystems, individual devices and data streams to detect abnormalities or failures.
• Command and control systems for typical vehicle movements like throttle, brake and steering.

Context is everything

The function of an AV can’t be separated from its environment because AV software must incorporate specific behavior modes for diverse circumstances. Is there a roundabout? A flashing red signal? Pedestrians and cyclists who cross at random locations? Does the weather include heavy rain, snow or blowing sand? Is there construction or a school zone?

These are just some of the items to consider before deploying an AV because the software must include appropriate behaviors for each condition. Otherwise, that environment will be considered outside the AVs operational design domain. AV testing and deployments have so far focused on locations that have nominal year-round weather, such as San Francisco, CA and Phoenix, AZ.

The intended use of an AV will also largely determine its operational environment. For example, middle-mile AV goods movement, AV transit and robotaxis all primarily operate in urban and suburban environments, and may not need to interface with a highway. Long-haul freight vehicles, for example, spend most of their time traveling at high speeds on highways, and deal less with signalized intersections. A Class VIII vehicle with a tractor trailer might also be required to navigate a dock in an industrial zone, which would not be required for robotaxis or AV buses.

AVs are now operating in real-world environments, and policymakers, planners and industry leaders must reconsider how transportation systems are regulated and managed. While the promise of AVs is tremendous, the shift from human to machine drivers is introducing new dynamics for traffic operations, safety protocols and infrastructure design that must be resolved before widespread adoption can be achieved.

For additional information contact paul.avery@archtam.com or edward.stubbing@archtam.com.

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There are still many unknowns about how or when automated vehicles (AVs) will be widely integrated into our transportation networks. While they can reduce accidents caused by human error, improve traffic flow, and enhance overall situational awareness, AV deployments also create new challenges.

Inconsistent regulations across jurisdictions, questions surrounding use case viability for different AV applications, and a lack of attention to critical operational considerations are a few of the issues facing the transportation sector and other parts of society. AV performance also remains largely dependent on nominal environmental and road conditions, restricting their operational domains.

What you need to know

We know AVs have the potential to improve transportation, but they also have the potential to make it worse, at least in the short term. To succeed, those looking to deploy AVs must first consider:

• Use case viability: Understanding the different uses and markets for AVs is essential to developing tailored strategies and solutions that reflect the specific needs and challenges of each sector. Robotaxis face important challenges deploying in urban and suburban environments and their success has been limited. Automated buses are progressing but face barriers within the traditional vehicle procurement mechanisms. And before automated goods movement can become viable, the industry must show financial benefit over human-driven operations.
• Effect of inconsistent regulations: AV integration into transportation networks requires updated regulations that account for non-human operators and a clear understanding of AV safety in mixed-traffic environments. The lack of central regulations leads to varied local laws that include permissive or restrictive language ― and sometimes no language at all ― making it particularly hard for long-haul freight AVs crossing multiple jurisdictions. Our entire regulatory landscape for transportation must evolve to balance innovation with safety while also factoring in cybersecurity measures to protect AV systems.
• Communication and vehicle co-ordination: A key enabler for large-scale AV deployments will be their ability to communicate with other vehicles, infrastructure devices and cloud technology to facilitate situational awareness and coordination of movement. Communication protocols like Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) enable AVs to share information and synchronize their actions to avoid collisions and smooth traffic flow. However, connectivity alone is insufficient, and AVs will need the ability to coordinate their movements. Regardless of AV brand or use case, this communication and coordination will rely on trust among industry players, and mutually-adopted software protocols that are currently not even being discussed among developers, vehicle manufacturers or policy makers.
• Operational considerations: AVs can operate 24/7, which is beneficial for goods movement and increased transit service, but it could also lead to increased congestion. What’s more, a large cluster of AVs in a given location could create a system-level effect that will need to be managed to mitigate or avoid negative impacts on other systems users. And while infrastructure modifications are not strictly necessary for AV operation, enhancements such as high-contrast lane markings and traffic signals, along with location-specific situational data could yield benefits for AVs and human users alike.
• Complex environments: AVs currently operate best in environments with nominal weather and road conditions and with few, if any, interactions with dynamic roadway users such as other cars and vulnerable road users. This environment, however, is not the busy and unpredictable environment where most vehicles and people operate. To date, AV testing has been limited to environments and settings where variables can be controlled or constrained, further challenging successful deployments in real life scenarios.

Steps to successful AV integration

As AVs reach a point where they are operating within defined regulatory frameworks, communicating with each other, and coordinating their movements to achieve specific goals, they will positively affect the safety and efficiency of our transportation systems, even at low levels of market penetration.

For additional information contact paul.avery@archtam.com or edward.stubbing@archtam.com.

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This week, we hear from Paul Avery, a technical specialist in emerging transportation technologies from our Transportation business in Austin, Texas. Paul shares why and how he and his team have developed a process and digital tool called AV-Readi^TM for evaluating the level of readiness for automated vehicle (AV) deployment on roadways.

The tool, which was developed in collaboration with Corby Schaub, a GIS specialist in our Data Practice Group, and Akik Patel, a transportation planner, seeks to demystify the automated vehicles technology, focusing on transit and freight applications, for infrastructure owner operators (IOOs), such as Department of Transportation, tollway authorities, and transit agencies, as well as automated vehicle industry developers and manufacturers.

Tell us more about AV-Readi and how it helps bring clarity to the automated vehicle deployment technology.

In 2018, we formed the Automated Bus Consortium (ABC) with more than a dozen founding public transit agency members. The goal was to incentivize the development of full-sized, 40-foot SAE Level 4 automated transit buses—one of the highest degrees of automation possible—and their deployment on existing bus routes.

At the time though, there was no standard process for evaluating the feasibility of deploying an AV on a roadway. This was in part because the technical capabilities of AVs exist along a broad range of maturity levels and their true capabilities are often not publicly disclosed. To successfully deploy AVs, this issue of deployment first had to be solved. AV-Readi^TM was our solution.

AV-Readi^TM is a process and set of digital tools, and is comprised of two major components, each with a similar but distinct purpose. The first component, called complexity analysis, evaluates the roadway in detail, identifying the location, as well as the type of challenges to deployment along the roadway. This data can lead to specific insights into infrastructure or technology enhancements that may improve readiness.

The secondary, optional assessment is higher level. It evaluates the roadway as a whole, identifying attributes that help or hinder deployment. These attributes, however, are not tied to a specific location. Rather, they inform the automation process itself, and help optimize vehicle behavior.

With AV-Readi, we’ve assessed almost 7,000 miles of roadway across the United States and Canada. For our transit agency and IOO clients, our analysis has enabled them to make an informed route selection for the effective deployment of a full-sized automated transit bus along existing routes. For our industry clients, it has identified routes that would minimize the impact to local communities by ensuring large freight vehicles do not transit their streets.

What was a key challenge you faced while working on this project? How did you solve it?

One of the key challenges facing the ABC is that standard processes do not yet exist for evaluating the safety and effectiveness of automated driving systems. While the ABC’s transit agencies needed to select an existing service route for AV deployment, it wasn’t clear which were the safest or most effective.

I saw an opportunity to utilize my deep industry experience in the development of automated vehicles and Corby’s expertise in GIS data processing and visualization to create a process and tool to assess and visualize the feasibility of routes. AV-Readi^TM was the result—and contains several innovations.

Our first innovation was to quantify complexity.

To capture challenges across each route, we began splitting routes into segments within a GIS environment according to vehicle behaviors, such as braking, merging, accelerating, and other actions. We then coded these segments according to the difficulty of these behaviors to create an overall complexity index to compare routes. Using this index, agencies could easily assess which routes were favorable for AV deployment.

But even as we split routes into segments, we still wanted to assess them holistically. Our solution was the secondary analysis discussed above, which I developed with Corby and Akik Patel, a transportation planner in Austin.

This analysis assesses the environmental attributes across the whole route and determines if those attributes enable or hinder deployment on that route. The process produces a separate set of index values, which can be combined with the complexity analysis to provide a more holistic assessment of the deployment readiness.

Working on AV deployment presents many opportunities to innovate, as the field is growing rapidly. While these are just two of our more recent innovations, The AV-Readi^TM tool is continuously evolving so we can offer more analysis for our clients more efficiently, and the results continue to be validated by feedback from our clients.

How has this experience shaped your approach to future work using AV-Readi?

The development of AV-Readi^TM is ongoing, and with each new project, the team is leveraging additional capabilities and incorporating new situations. As it has developed, it’s attracted international attention, and may soon be applied to projects in Europe, Asia, and the Middle East. The team is also focusing on preparing AV-Readi^TM as a software as a service (SaaS) model to make it more accessible across ArchTam, and developing training for our professionals so they can offer this service to their local clients.

Want to learn more about AV-Readi^TM and how it is making a positive impact for IOOs and the AV industry? Follow the links below to dive deeper.

• The Automated Bus Consortium
• Automated Bus Consortium issues RFP to acquire 70 automated buses | Intelligent Transport
• Decarbonizing Transportation – AV-Readi

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