“I think we’re quite close to having the car be fully autonomous.” So declared Tesla CEO Elon Musk in February. Just two months later, he surprised investors with a post on X announcing Tesla would unveil a robotaxi in August.  

If you’re experiencing déjà vu, you’re not alone. Musk has been talking up self-driving cars as far back as 2016, when Tesla launched a feature called "Full Self-Driving Capability." In 2019, Musk predicted full autonomy was just around the corner, saying "probably two years from now we'll make a car with no steering wheels or pedals."

That hasn’t happened, but Tesla is not alone in hitting a bumpy road in the pursuit of full autonomy. Other companies, including Cruise and Waymo, have suffered setbacks trying to move the world to a future where autonomous vehicles (AVs) are the norm.

Consider Cruise, a subsidiary of General Motors. Last October, the company had its operating license suspended in California after one of its driverless cars struck a pedestrian and dragged her 20 feet before coming to a stop. Or Alphabet’s Waymo, formerly the Google self-driving car project. In February, Waymo announced it was voluntarily recalling the software running its self-driving robotaxis after two of its vehicles crashed into the same towed pickup truck within minutes of each other.

Despite the setbacks, new and established companies are committed to solving autonomous driving.

This year, Waymo started testing its vehicles on freeways. In June, Cruise resumed driverless operations in Dallas, though these vehicles will be supervised with safety drivers behind the wheels.

On the software side, self-driving car startup Wayve Technologies raised more than $1 billion in a series C round that attracted companies such as SoftBank, Nvidia and Microsoft as investors. One thing is clear: the race to autonomous vehicles is not slowing down.      


To understand the challenges in creating fully autonomous vehicles, let’s start with how they work.

Autonomous driving generally follows a “sense-plan-act” architecture. Take Waymo as an example. Their vehicles rely on a range of sensors (LiDAR, radar, cameras) and sophisticated software using artificial intelligence to process all sensory input in real-time, decide on an action in a split-second, and send the signals back to the car’s steering, braking and control acceleration systems.

Design philosophies differ. Tesla, for example, has avoided LiDAR, which is much more expensive, relying instead on cameras, ultrasonic sensors and radar. Also, startups such as Wayve, whose AV2.0 autonomous driving technology aims to learn and adapt to new scenarios without needing pre-programmed rules or detailed maps, are trying to upend the traditional “sense-plan-act” architecture.

The industry has made great progress in bringing autonomy to the market using the “sense-plan-act” architecture. Many commercially available automobiles in the US come with autonomous features such as driver assist and enhanced cruise control.

Waymo and Cruise are piloting what is considered Level 4 autonomy – providing full autonomy but in limited-service areas and scenarios.

The ultimate goal is Level 5* autonomous vehicles, which can drive themselves on any road, in any conditions, without any input from the passengers. Developing these vehicles involves several challenges, including technological, regulatory and ethical issues.

First, let’s consider the technological challenges. AVs rely on sensors to evaluate the operating environment in real-time. When lots of AVs are on the same road, there is a risk that their LiDARs could interfere with each other. Sensors become less reliable in poor conditions; think of a lane divider covered by snow, which makes it challenging for AVs to make an informed driving decision in real time.

Moreover, the machine learning algorithms used to train the artificial intelligence at the heart of an AV system need vast amounts of data on all the driving scenarios that could be encountered on the road. While current AVs can handle many standard scenarios, it is the unexpected “edge” cases that are particularly challenging for AV systems to handle.

Second, the lack of federal regulation and the variation across state regulations create a challenge for companies trying to design and deploy AVs on a large scale.

In December 2020, the National Highway Traffic Safety Administration (NHTSA) requested comments on the development of a framework for Automated Driving System safety. In the document, NHTSA shared that the four primary functions that the agency should focus on are sensing, perception, planning and control.

Progress has been slow. In April, NHTSA finalized a standard that requires pedestrian automatic emergency braking (AEB) on all passenger cars and light trucks by September 2029. While a comprehensive federal framework continues to develop, states are filling in the gap through their own regulations. California, Michigan and Arizona are among the most proactive players, with varying regulations spanning across testing, reporting and deploying AVs.

Lastly, a host of ethical questions continues to stir public debate. One major issue is the classic “trolley problem.” In a critical situation, how should an AV decide whose safety to prioritize? This dilemma goes beyond programming complexities and involves profound moral decisions traditionally made by humans. Who should define these ethical frameworks – the engineers, the regulators or someone else?

Another concern is the risk of hacking to use a car for criminal activities or other nefarious purposes. As we navigate these challenges, one thing is clear: there are no straightforward answers. Instead, ongoing dialogue among the government, the private sector and the public is essential.


Despite the challenges, the eventual maturation of AVs is poised to disrupt the entire passenger vehicle value chain.

A McKinsey study forecasts that Level 4 autonomous driving systems for passenger cars could generate $170 billion to $230 billion in revenue by 2035. Original Equipment Manufacturers, or OEMS, which have been traditionally strong in hardware, will need to match this excellence in software development and deployment. A key question for many OEMs is whether to develop these capabilities in-house (like GM with Cruise) or form strategic partnerships (like Daimler Truck with Waymo and Torc Robotics).

The diffusion of AVs will have a big impact on adjacent industries such as insurance. Some argue that with AVs, liability shifts from the “driver” to the OEMs and technology providers that design and deploy autonomous driving solutions. However, manufacturers such as Tesla with its “Autopilot” feature, claim that the driver is ultimately responsible for the vehicle, not the company. Determining liability will be crucial for the advancement of AVs.

AVs will likely disrupt the entire automobile business model, challenging traditional vehicle ownership and leading to a future of ride-sharing. New pricing models based on fee-for-service and subscription models will likely proliferate. The increased efficiency and utilization from ride-sharing and car-sharing services could significantly reduce the total number of vehicles on the road. Competitive advantage may shift from those that manufacture quality vehicles at scale to those that deploy artificial intelligence and software to provide a superior riding experience.


The big question is: When will true Level 5 autonomous vehicles be widely available?

The latest forecast from S&P predicts that Level 5 autonomy won’t be on the road until 2035. However, before then, we will see targeted uses of Level 4 AVs. We are already seeing niche applications such as John Deere’s farming tractors, small delivery vehicles on college campuses, and ride-hailing services in cities such as San Francisco and Phoenix from companies like Waymo.

Many experts predict long-haul trucks could be the first major on-the-road adoption of AVs. Daimler Truck recently revealed its first autonomous truck and announced that its driverless semi-trucks will be on the road by 2027.

For passenger vehicles, we will likely see more localized deployments before widespread use across the country.

Factors such as population density, road infrastructure and local government support will determine which cities are attractive for AV expansion.

Consumer acceptance is crucial for a fully autonomous future. A recent S&P Global Mobility consumer survey found that 65% of buyers want Level 2+ hands-off automated highway driving. However, a 2021 McKinsey survey found that only 25% of buyers are “very interested” in advanced autonomous-driving functions (defined as L2+, L3 and L4) when buying their next car. While interest in AVs is not yet universal, this may change over time.

The potential benefits of AVs include reducing accidents caused by human errors, freeing up time spent driving, and reducing carbon emissions through greater efficiency. Both new and established companies are continuing to innovate around key technologies, business models and use cases for AVs.  

Competition is intense, with the US, China, Germany and Japan heavily invested in this technology race. The boundaries of AV possibilities will continue to be pushed on a global scale.

The development and widespread deployment of autonomous vehicles will be a winding road. Buckle up and get ready for an exciting journey ahead.


Level 4, or “high automation”, is defined as when a “system is fully responsible for driving tasks within limited service areas while occupants act only as passengers and do not need to be engaged”. Level 5, or “full automation”, is defined as when a “system is fully responsible for driving tasks while occupants act only as passengers and do not need to be engaged.” See more details on https://www.nhtsa.gov/vehicle-safety/automated-vehicles-safety.

About the Expert

Michael Lenox

Special Adviser for the Dean; Tayloe Murphy Professor of Business Administration

Lenox’s expertise is in the domain of technology strategy and policy. He studies the role of innovation in helping a business succeed. In particular, he explores the sourcing of external knowledge by firms and this practice’s impact on a company’s innovation strategy. Lenox has a longstanding interest in the interface between business strategy and public policy as it relates to the natural environment; his work explores firm strategies and nontraditional public policies that have the potential to drive green innovation and entrepreneurship.

In 2013, Lenox co-authored The Strategist’s Toolkit with Darden Professor Jared Harris. His latest book, 

Lenox is a prolific author; his most recent book, Strategy in the Digital Age: Mastering Digital Transformation, examines how digital technologies and services enable the creation of innovative products and services, as well as identifying new competitive positions.

B.S., M.S., University of Virginia; Ph.D., Massachusetts Institute of Technology