How Autonomous Driving Created A Talent War

In January 2015, some of the best vehicle autonomy researchers in the world began disappearing from the National Robotics Engineering Center (NREC) at Carnegie Mellon University. By the end of that month, 50 staff members had defected from the research institute—fully a third of NREC’s headcount—including many of its top employees and the center’s director.

The erstwhile staff members reappeared just a few blocks away, inside a former chocolate factory now owned by Uber Technologies, which had purchased and renovated the space to house the flagship office of its Advanced Technologies Group. Uber had lured the researchers away from NREC with sky-high compensation packages and the promise of making a tangible impact by helping cars drive themselves in the real world—not just in the lab.

Later that year, Carnegie Mellon and Uber signed a “strategic partnership” to reset their relationship and create a more formal pathway for the university’s researchers to engage with Uber, but other players in the automotive space had already taken notice. As audacious as it was, Uber’s bold maneuver was merely the opening salvo in a battle to find and recruit the most valuable talent in the world—autonomous driving experts—by any means necessary.

In recent years, the talent war has escalated. Automotive companies must now compete not only with players in the mobility space—Toyota, Tesla, GM, Ford, Volkswagen, Hyundai, Stellantis, Mercedes Benz, Rivian, BWM, new non-original equipment manufacturer entrants like Waymo, traditional Tier 1 suppliers like Bosch, and transportation networks like Uber and Lyft—but with all industries that need high-quality engineers. Additionally, other transportation-related industries have entered the market with self-driving transport trucks, airplanes, drones, and more.

Internet of Things (IoT) and embedded system engineers in particular have been scarce. Other experts in high demand are machine learning, computer vision, and artificial intelligence specialists with the know-how to design the “guiding intelligence” of autonomous vehicles. These are the systems that translate sensor inputs and map data into self-driving capabilities that demonstrate enormous reliability across a wide variety of conditions.

With a projected compound annual market growth estimated at over 20%, and a $50 billion increase in annual sales over the next five years at stake, high-quality talents in the self-driving sector are being courted with the same kind of fervor—and compensation—as rock stars and top athletes.

How Talent Drives Innovation In Vehicular Automation

As discussed in our article on the growing importance of software to the auto industry, self-driving vehicles will fundamentally alter the automotive value chain. Until the recent past, automakers have been hardware powerhouses with a core competency in manufacturing and logistics. But sophisticated software will be equally vital to the cars of tomorrow—for self-driving cars, as necessary as the engine and transmission.

A commonly accepted industry estimate, based on a 2013 Morgan Stanley Research report, is that software and applications layers will collectively account for 60% of the value of a self-driving car. These software development and related costs are essential investments to stay competitive in the future and capitalize on additional revenue streams. As autonomous driving technology starts to be commercialized and rolled out, additional talent will be required including finance experts, technicians, trainers, technology and security operations specialists, and customer support teams.

But the need for talent doesn’t end there. Self-driving cars are at the nexus of four interrelated technological trends, each representing a horizon that must be pushed to the limit to create a superior mobility experience that will win consumers in the next decade. In the rest of this article, we review those technologies—connectivity, autonomy, shared mobility, and electrification—to help automotive stakeholders prioritize their search for talent.

1. Connectivity

As is the case with all applications of machine learning technology, self-driving capabilities are a product of their data. With higher-quality data, autonomous vehicles can make better, more reliable decisions about where and how to drive.

Most of the mission critical data required for cars to drive themselves will arrive from a robust complement of onboard sensors including cameras, radars, and lidars. But to maximize performance and safety, autonomous vehicles will need to connect to additional data sources.

At a base level, this involves connecting to GPS guidance systems and cloud-based traffic applications with specially designed maps that go well beyond basic navigation to gauge details like curb height and location of street lights to help vehicles generate optimal routes and navigate the streets. Automakers are already on top of this trend, and McKinsey & Company estimates that nearly 95% of vehicles sold globally will be connected by 2030, up from 50% in 2021.

But reducing latency and reaching the highest standards of safety and comfort implies that self-driving cars will also share data and communicate with other vehicles, and with the infrastructure and roads around them. For example, smart traffic signals could interact directly with vehicles, and sensors could be embedded in road signs and parking meters. Self-driving cars will consume data from a wide variety of sources, implying that expertise in connectivity and the Internet of Things will be essential in this field. It will be just as important to retain top security experts who can detect vulnerabilities and limit the risk that connected vehicles could be hacked or exploited.

2. Autonomy

Having ingested a wide variety of data from internal sensors and external sources, self-driving cars must transform it into route and control guidance. The algorithms that produce autonomy are the most fundamental component of driverless vehicles, and autonomy is the capability at the heart of the talent war.

The online education platform Udacity now offers a “Self-Driving Car Engineer” nanodegree and its curriculum provides a glimpse into the many software competencies required to produce vehicular autonomy of any degree. Essential areas of knowledge include deep learning, computer vision, sensor processing and fusion, localization, and control—each with in-depth submodules. Taken together, these skills enable engineers to design systems that can recognize traffic signals and signs, stay in lanes, adjust to adverse weather, react to the flow of traffic, and preempt potential collisions.

It is notable that a number of Udacity’s course modules are sponsored by companies researching self-driving car technology, including Mercedes-Benz and Uber, implying that major mobility players are so eager to lock down autonomy talent that they are willing to scoop up engineers straight out of school.

3. Shared Mobility

Research into autonomous vehicles has been accelerated tremendously by the growth of transportation networks like Uber and Lyft, as well as short-term car rental services like Zipcar and Car2go. In particular, Uber and Lyft are banking on the fact that autonomous cars will substantially reduce the variable cost of their services by eliminating drivers, making ride-hailing competitive in cost and convenience with owning a car.

The popularity of Uber and Lyft, combined with cultural factors that have reduced the value of car ownership for younger demographics, have made mobility players of all kinds sit up and take notice. Companies like Cruise, Waymo, and Motional have taken an aggressive approach, partnering with Lyft and Uber to develop the future of ride-hailing services using state-of-the-art technology.

Whether they want to supply vehicles for ride-sharing networks or create their own networks, mobility players will need to invest in talent to understand challenges in the space, from smart routing while carpooling to maintenance and passenger safety.

4. Electrification

Self-driving cars with internal combustion engines will exist well into the future, especially for long-distance applications. That said, battery technology continues to improve in range and cost, and the majority of autonomous vehicles are likely to be electric.

The reasons for this are connected to the other technological trends that have catalyzed autonomous vehicle development. For one, electric vehicles have the potential to be easier to maintain because they consist of just three main components: battery, inverter, and electric motor. At maturity, they could also be easier to refuel through wireless technology like induction charging, making them suited to the intense usage patterns of ride-hailing.

Electric cars are also easier for computers to control, and can furnish reliable power to the array of sensors that autonomous vehicle use to gather data and control their motion. By 2017, 58% of light-duty autonomous vehicles were already being built over an electric powertrain, while 21% used a hybrid powertrain. Until battery technology enables electric vehicles to match the range of gasoline cars, hybrid engines will bridge the gap, offering both range and superior compatibility with the unique needs of autonomous vehicles.

Good Self-driving Talent Is Hard to Find

The talent wars in vehicular automation have just begun. The demand for technical and business talent in the four areas outlined above will grow as organizations across the entire mobility space increase their conviction in the rapid progression of the technology and legal norms that currently gate self-driving cars from widespread adoption.

Securing talent with deep expertise in autonomy is essential to producing autonomous vehicles, but mobility players must not stop there. The winners of the talent wars will need to look across the entire stack of competencies required to produce safe and high-quality autonomous driving experiences, employing creative tactics to lock down talent and secure their road to sustainable growth in the next era of mobility.