Technology-driven unemployment is a real threat, but robotic trucks are very unlikely to decimate the trucking profession in one sudden phase transition. The path to fully autonomous trucking is likely to be a gradual slope, not a steep cliff—a trajectory shaped not only by technical roadblocks, but by social, legal, and cultural factors. Truck drivers’ daily work consists of many complex tasks other than driving trucks—maintenance, inspections, talking to customers, safeguarding valuable goods—many of which are far more difficult to automate than highway driving. A host of new legal regimes across states will be required to ensure that the technology can be deployed safely. And widespread apprehension around autonomous vehicles (and autonomous trucks especially) will likely delay adoption. All of these factors will slow the degree at which autonomous trucks take to American highways. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Learn more. Instead of thinking about a sudden wave of trucker unemployment, then, we should think about how AI will change what truckers’ work looks like over the long haul. There will still be human truckers for a long time to come—but this doesn’t mean that what it means to be a human trucker won’t change substantially. Rather than whole-cloth replacement of human truckers, autonomous technologies might require integration between human and machines over a long period of time, as truckers are required to coordinate their work—and themselves—with the technology. There are several possible forms this integration might take. One vision of the future imagines machines and humans as coworkers. In this model, people and machines “pass the baton” back and forth to one another, like runners in a relay: The worker completes the tasks to which she is best suited, and the machine does the same. For example, a robot might take responsibility for mundane or routine tasks, while the human handles things in exceptional circumstances, or steps in to take over when the robot’s capacities are exceeded. Human/robot teams hold some promise both because they try to seize on the relative advantages of each—and because the model presumes that humans get to keep their jobs. In fact, some believe that human jobs might become more interesting and fulfilling under such a model, if robots can take on more of the “grunt work” that humans currently are tasked with completing. The human/robot team is not an especially farfetched idea for trucking work. In fact, most of us encounter a version of this model every time we sit behind a steering wheel. Modern cars commonly offer some form of technological assistance to human drivers (sometimes called “advanced driver-assistance systems”). Adaptive cruise control is an example: When a human driver activates it, the car automatically adjusts its own speed to maintain a given driving distance from the cars in front of it. What would the handoff model mean for truckers? In theory, the truck would handle the bulk of the driving in good conditions, and the human trucker would take over in situations where the machine has trouble—say, in a construction zone or crowded intersection, or when visibility is poor. When the machine is in charge, the theory goes, the trucker might be “unshackled from the wheel” and freed up for other tasks. This vision is similar to the transformation of the bank teller’s role after the advent of the ATM: The machine does the boring routine work, freeing up the human for more interesting or skill-matched pursuits. But it leaves open big questions about whether or how truckers would be paid for time in the cab while the truck drives itself—after all, if trucking companies are still paying big labor costs, are autonomous trucks worth the investment?—and also wouldn’t necessarily address problems around overwork and fatigue. There’s another problem that’s even more fundamental. Baton-passing is incredibly—perhaps intractably—difficult to execute smoothly in situations like driving. Recall that the machine passes off responsibility to the human in the situations it finds most difficult: when conditions are unusual, when there is something in the environment it isn’t equipped to contend with, when there’s a mechanical malfunction or emergency. Those situations are very likely to be safety critical. One review of the scholarly literature found “a wealth of evidence” that automating some aspects of driving led to “an elevated rate of (near-) collisions in critical events as compared to manual driving … Essentially, if the automation fails unexpectedly with very little time for the human to respond, then almost all drivers crash.” This problem is so severe because the time scale in which the baton is passed is miniscule: Because of the nature of driving, a human is likely to have an extremely short window—perhaps only a fraction of a second—in which to understand the machine’s request to intervene, assess the environmental situation, and take control of the vehicle. This tiny time window is the reason why human drivers in semiautonomous cars are warned that they must stay alert the entire time the car is driving. Despite the image of humans relaxing, napping, texting, eating, and being otherwise freed up from the requirements of driving, this image is patently unrealistic given the need for quick, safety-critical handoffs at current levels of automation. Audio and visual alarms can help humans know when a handoff is coming, but the immediacy of the need to take control means that humans must still pay constant attention. However, a 2015 NHTSA study found that in some circumstances it could take humans a full 17 seconds to regain control after a vehicle alerted them to do so—long beyond what would be required to avoid an accident. This irony creates severe problems for human/robot handoffs in autonomous cars and trucks. So long as humans have some duty to monitor the driving environment—which they do at the current state of the art—humans will almost inevitably do a poor job at accepting the baton from the machine. Does this mean there’s no hope for safe autonomous vehicles? Not necessarily. If robots and humans make bad coworkers because of the weaknesses of the human, one solution might be to increase the level of automation even more, obviating the need for short-term handoffs to a human at all. This could create a second model of integration: network coordination. Another way to think about the division of labor between humans and machines is as a matter of more systemic work-sharing. Rather than a focus on in-the-moment driving, we might think about humans and machines as sharing truck-driving work in a broader way: by dividing up responsibilities over the driving route. We’ve been thinking about the work of truck driving as a set of small, often simultaneous driving tasks: change lanes, hit the brakes, watch for road obstacles. We could instead think about it as a series of predictable segments: travel down the interstate, exit the highway and take local roads, steer around the receiver’s docks. In this model, humans and robots still share the labor of trucking work, but take turns being wholly responsible for driving—much as you and a friend might take turns driving on a road trip—with temporally and geographically predictable points of transition between the two of them. Some truckers already do this when they “drive team,” taking turns driving (often while one driver sleeps). If we think of human/robot teams working together in tandem over these segments, a second model of integration emerges: network coordination. Several trucking technology firms have set their sights on this sort of model. But wait, you might think. The reason for autonomous cars to hand off control to humans is that they aren’t fully capable of driving themselves—they can’t negotiate unexpected obstacles well, they lack humans’ tacit knowledge, they can fail catastrophically in new and complex situations. If this happens, how can we envision giving a machine total control over an entire portion of the route, without a human driver being expected to step in? All of this requires irregular driving in response to immediate human direction, sometimes in large lots without lanes or traffic markings—and is nearly impossible for a machine to do on its own. (As a point of comparison, think of how planes taxi around at airports—despite the widespread use of autopilot in the air, there’s little chance that airport taxiing will be automated anytime soon.) So, a natural division of labor in trucking might be that advanced autonomous trucks drive themselves over the long haul, and humans take the wheel for the endpoints—what’s often called the “last mile” in transportation and logistics. In 2017, Uber announced such an approach: an autonomous truck network, connected by local hubs throughout the country. Autonomous trucks would run the long hauls between the hubs, and human truckers would pilot the trucks from hubs to delivery. It isn’t a feasible model—yet. But some autonomous vehicle technology companies think the human/machine coordination challenges at current levels of semiautonomy are so difficult and intractable that they are essentially attempting to “skip” those levels, focusing their attention on developing vehicles that can drive with no human involvement under particular conditions (such as highway driving within a prespecified area or under only certain weather conditions). In trucking, if full autonomy could enable the truck to drive without the driver’s constant attention (and for longer time periods—since robots don’t get tired), the prospect seems more economically viable than a model that requires a driver to be engaged as a backup (and, presumably, paid). However, the only way the network coordination model is a viable option is if the pay structure of trucking adjusts with it. Truckers are paid by the mile, and the great majority of miles driven (and thus money earned) takes place on the highway—not on traffic-packed local roads or while maneuvering around at a terminal. The parts of the job that network coordination models might automate are precisely the parts that make up the lion’s share of a trucker’s wages. A variation on network coordination could involve allowing truck drivers to take the wheel remotely for the “last mile” of operation. Starsky Robotics, founded with significant venture capital investment in 2016, developed a “teleoperation” system in which trucks drove themselves to a certain point, and human drivers subbed in remotely from the highway exit to the terminal—as if they were playing a video game or operating a drone. In theory, such a system could allow a single driver to pilot dozens of vehicles a day, for short periods of time, all over the country—and still return home each night. (As one remote trucking executive framed it: “Think about the mom who is home driving a truck. She can drive multiple assets and never leave her kids.”) Some refer to this as a “call-center” model in which the robot calls into a human phone bank for support or handoff at predetermined points in the route. But it isn’t obvious that a model like this is sustainable, either. For one, the handoff problems seem likely to be only exacerbated by distance. And there are other problems unique to the model: Ford shut down its system testing a similar idea after the vehicles repeatedly lost their cell signal so that human operators couldn’t see the video feed. Starsky Robotics closed its doors in 2020; in a valedictory blog post, its chief executive chalked up the company’s closure in large part to the assessment that “supervised machine learning doesn’t live up to the hype” in terms of operational capability in autonomous trucking. The future of trucking might someday look like these baton-passing or network-coordination models of shared labor. But right now, human/machine interaction in trucking looks very different. What we see happening in trucking now involves a much less discrete parceling-out of functions between humans and machines. Instead, truckers’ physical bodies and intelligent systems are being integrated into one another. There are two kinds of technologies that turn truckers into RoboTruckers. The first are wearables, which monitor elements of the trucker’s internal bodily state and use them as metrics for management. For example: The second set of technologies are cameras pointed at the driver designed to detect his level of fatigue, often by monitoring his eyelids to track his gaze and look for signs of “microsleep.” Seeing Machines is one of several companies that market driver-facing cameras that use computer vision to monitor a driver’s eyelids and head position for signs of fatigue or inattention. If the driver’s eyes close or look away from the road for too long, it sounds an alarm and sends a video to his boss—and can also cause the driver’s seat to vibrate in order to “goose” him back into attention. Another driver-facing camera vendor, Netradyne, uses deep learning and data from driver- and road-facing cameras to generate scores for drivers based on their safe and unsafe driving behaviors. From the truckers’ point of view, there’s something viscerally offensive about the micromanagement enabled by these technologies. This is the felt reality of AI in trucking labor now: using AI to address human “weakness” through constant, intimate, visceral monitoring. There’s an enormous distance between the narrative of displacement that characterizes most public discussion of AI’s effects on truckers and how these effects are actually being experienced through these technologies. The threat of displacement is a real one, particularly to truckers’ economic livelihood—but driverless trucks are not yet borne out by common experience, and drivers are also not yet handing off a baton to or splitting routes with a robot coworker. Truckers’ encounters with automation and artificial intelligence have not yet supplanted them. Instead, technologies like the ones we’ve discussed above represent a distinct and simultaneous threat: a threat of compelled hybridization, an intimate invasion into their work and bodies. AI in trucking today doesn’t kick you out of the cab; it texts your boss and your wife, flashes lights in your eyes, and gooses your backside. Though truckers are, so far, still in the cab, intelligent systems are beginning to occupy these spaces as well—in the process, turning worker and machine into an uneasy, confrontational whole. Data Driven: Truckers, Technology, and the New Workplace Surveillance by Karen Levy. Copyright © 2023 by Princeton University Press. Reprinted by permission.
