Case Study

Will cars really be able to drive themselves without human operators? Should they? And are they good business investments? Everyone is searching for answers.

Autonomous vehicle technology has reached a point where no automaker can ignore it. Every major auto maker is racing to develop and perfect autonomous vehicles, believing that the market for them could one day reach trillions of dollars. Companies such as Ford, General Motors, Nissan, Mercedes, Tesla, and others have invested billions in autonomous technology research and development. Ford invested $1 billion in AI firm Argo AI, and GM bought a self-driving car startup called Cruise. Ford has set a goal of producing a self-driving car with nopedalsby 2021. Ride-hailing companies like Uber and Lyft believe driverless cars that eliminate labor costs are key to their long-term profitability. Cars that drive themselves have been on the road in select locations in California, Arizona, Michigan, Paris, London, Singapore, and Beijing. Waymo, the company that emerged from Google's self-driving car project, predicts that by 2020 its fleet of self-driving Jaguars will make as many as one million trips per day.

A car that is supposed to take over driving from a human requires a very powerful computer system that must process and analyze large amounts of data generated by myriad sensors, cameras, and other devices to control and adjust steering, accelerating, and braking in response to real-time conditions. Key technologies include:

Sensors: Self-driving cars are loaded with sensors of many different types. Sensors on car wheels measure car velocity as it drives and moves through traffic. Ultrasonic sensors measure and track positions of line curbs, sidewalks, and objects very close to the car.

Cameras: Cameras are needed for spotting things like lane lines on the highway, speed signs, and traffic lights. Windshield-mounted cameras create 3-D image of the road ahead. Cameras behind the rear-view mirror focus on lane markings. Infrared cameras pick up infrared beams emitted from headlamps to extend vision for night driving.

Lidars: Lidars are light detection and ranging devices which sit on top of most self-driving cars. A lidar fires out millions of laser beams every second, measuring how long they take to bounce back. The lidar takes in a 360-degree view of a car's surroundings, identifying nearby objects with an accuracy up to 2 centimeters. Lidars are very expensive and not yet robust enough for a life of potholes, extreme temperatures, rain, or snow.

GPS: A global positioning system (GPS) pinpoints the car's macro location, and is accurate to within 1.9 meters. Combined with reading from tachometers, gyroscopes, and altimeters, it provides initial positioning.

Radar: Radar bounces radio waves off of objects to help see a car's surroundings, including blind spots, and is especially helpful for spotting big metallic objects, such as other vehicles.

Computer: All the data generated by these technologies needs to be combined, analyzed, and turned into a robot-friendly picture of the world, with instructions on how to move through it, requiring almost supercomputer-like processing power. Its software features obstacle avoidance algorithms, predictive modeling, and "smart" object discrimination (for example, knowing the difference between a bicycle and a motorcycle) to help the vehicle follow traffic rules and navigate obstacles.

Machine Learning, Deep Learning, and Computer Vision Technology: The car's computer system has to be "trained" using machine intelligence and deep learning to do things like detect lane lines and identify cyclists, by showing it millions of examples of the subject at hand. Because the world is too complex to write rule for every possible scenario, cars must be able to "learn" from experience and figure out how to navigate on their own.

Maps: Before an autonomous car takes to the streets, its developers use cameras and lidars to map its territory in extreme detail. That information helps the car verify its sensor readings, and it is key for any vehicle to know its own location.

Self-driving car companies are notorious for overhyping their progress. Should we believe them? At this point, the outlook for them is clouded.

In March 2018, a self-driving Uber Volvo XC90 operating in autonomous mode struck and killed a woman in Tempe, Arizona. Since the crash, Arizona has suspended autonomous vehicle testing in the state, and Uber is not renewing its permit to test self-driving cars in California. The company has also stopped testing autonomous cars in Pittsburgh and Toronto and it's unclear when it will be revived. Even before the accident, Uber's self-driving cars were having trouble driving through construction zones and next to tall vehicles like big truck rigs. Uber's drivers had to intervene far more frequently than drivers in other autonomous car projects.

The Uber accident raised questions about whether autonomous vehicles were even ready to be tested on public roads and how regulators should deal with this. Autonomous vehicle technology's defenders pointed out that nearly 40,000 people die on U.S. roads every year, and human error causes more than 90 percent of crashes. But no matter how quickly self-driving proliferates, it will be a very long time before the robots can put a serious dent in those numbers and convince everyday folks that they're better off letting the cars do the driving.

While proponents of self-driving cars like Tesla's Elon Musk envision a self-driving world where almost all traffic accidents would be eliminated, and the elderly and disabled could travel freely, most Americans think otherwise. A Pew Research Center survey found that most people did not want to ride in self-driving cars and were unsure if they would make roads more dangerous or safer. Eighty-seven percent wanted a person always behind the wheel, ready to take over if something went wrong.

There's still plenty that needs to be improved before self-driving vehicles could safely take to the road. Autonomous vehicles are not yet able to operate safely in all weather conditions. Heavy rain or snow can confuse current car radar and lidar systemsautonomous vehicles can't operate on their own in such weather conditions. These vehicles also have trouble when tree branches hang too low or bridges and roads have faint lane markings. On some roads, self-driving vehicles will have to make guidance decisions without the benefit of white lines or clear demarcations at the edge of the road, including Botts' Dots (small plastic markers that define lanes). Botts' Dots are not believed to be effective lane-marking for autonomous vehicles.

Computer vision systems are able to reliably recognize objects. What remains challenging is "scene understanding"for example, the ability to determine whether a bag on the road is empty or is hiding bricks or heavy objects inside. Although autonomous vehicle vision systems are now capable of picking out traffic lights reliably, they are not always able to make correct decisions if traffic lights are not working. This requires experience, intuition, and knowing how to cooperate among multiple vehicles. Autonomous vehicles must also be able to recognize a person moving alongside a road, determine whether that person is riding a bicycle, and how that person is likely to respond and behave. All of that is still difficult for an autonomous vehicle to do right now. Chaotic environments such as congested streets teeming with cars, pedestrians, and cyclists are especially difficult for self-driving cars to navigate.

Driving a car to merge into rapidly flowing lanes of traffic is an intricate task that often requires eye contact with oncoming drivers. How can autonomous vehicles communicate with humans and other machines to let them know what they want to do? Researchers are investigating whether electronic signs and car-to-car communication systems would solve this problem. There's also what's called the "trolley problem": In a situation where a crash is unavoidable, how does a robot car decide whom or what to hit? Should it hit the car coming up on its left or a tree on the side of the road?

And let's not forget security. A self-driving car is essentially a collection of networked computers and sensors linked wirelessly to the outside world, and it is no more secure than other networked systems. Keeping systems safe from intruders who want to crash or weaponize cars may prove to be the greatest challenge confronting autonomous vehicles in the future.

Some pundits predict that in the next few decades, driverless technology will add $7 trillion to the global economy and save hundreds of thousands of lives. At the same time, it could devastate the auto industry along with gas stations, taxi drivers, and truckers. People might stop buying cars because services like Uber using self-driving cars would be cheaper. This could cause mass unemployment of taxi drivers and large reductions in auto sales. It would also cut down the need for many parking garages and parking spaces, freeing up valuable real estate for other purposes. More people might decide to live further from their workplaces because autonomous vehicles linked to traffic systems would make traffic flow more smoothly and free riders to work, nap, or watch video while commuting. Some people will prosper. Most will probably benefit, but many will be left behind. Driverless technology is estimated to change one in every nine U.S. jobs, although it will also create new jobs. Another consideration is that the tremendous investment in autonomous vehicles, estimated to be around $32 billion annually, might be better spent on improving public transportation systems like trains and subways. Does America need more cars in sprawling urban areas where highways are already jammed?

The accidents self-driving cars have experienced so far point to the need to create dependable standard for measuring reliability and safety. In 2018, twenty-nine states have enacted legislation regulating autonomous vehicles, with a few states requiring a safety driver always be in the car ready to take control. U.S. federal regulators have delayed formulating an overarching set of self-driving car standards, leaving a gap for the states to fill. The federal government is only now poised to create its first law for autonomous vehicles. This law is similar to Arizona's and would allow hundreds of thousands of driverless cars to be deployed within a few years and would restrict states from putting up hurdles for the industry.

Case Study Questions

What are the challenges posed by self-driving cars?

Are self-driving cars good business investments? Explain your answer.

What ethical and social issues are raised by self-driving car technology?