How Do Self-Driving Cars Work?

Imagine taking in the scenery as your self-driving car safely transports you to your destination. That’s the self-driving dream technologists have envisioned for decades — and now it may be closer to reality, as technology continues to evolve. But the industry still faces many challenges, including safety standards, weather, and consumer perception.

From sensors to communication systems, here’s a closer look at how self-driving cars work.

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Core technologies behind self-driving cars

While human drivers rely on their eyes, ears, and brains to navigate the roads, self-driving cars use a complex system of sensors, artificial intelligence, and mapping to drive autonomously. Together, this combination of tools helps self-driving vehicles make decisions and steer clear of common traffic obstacles.

Here’s a closer look at some of the key technology used in self-driving cars.

Sensors

Sensors are an essential component of self-driving cars. They take in key information about potential obstacles, like pedestrians, other vehicles, traffic lights, and construction. 

Most self-driving cars use a combination of lidar (pulses of laser light that determine the presence, shape, and distance of objects) and radar sensors to determine the car’s distance from obstacles; 360-degree cameras to collect relevant image data; and ultrasonic sensors for short-distance data. Infrared sensors help these cars detect lane markings, pedestrians, cyclists, and other objects that might be difficult to see in darker settings.

Artificial intelligence and machine learning

After the car’s sensors take in visual information, artificial intelligence and machine learning algorithms help the vehicle make sense of this information and then function accordingly. A useful analogy might characterize the sensors as the car’s eyes and the AI system as its brain.

For instance, Waymo’s proprietary Driver technology combines real-time sensor data with deep learning from millions of previous trips. This allows the vehicle to anticipate the movements of other road users and determine the appropriate maneuver.

Mapping and localization

Self-driving cars are built with GPS systems, which use satellites to triangulate the car’s location. Additionally, inertial navigation systems (INS) utilize gyroscopes and accelerometers to improve GPS accuracy by tracking the vehicle’s velocity, position, and orientation.

GPS and INS systems can still be off within a few meters, so self-driving cars also come with a store of prebuilt maps that can help improve accuracy.

Communication systems

Self-driving cars connect to the internet, which enables them to communicate with other connected devices around them, like roadside infrastructure or other vehicles. This process is known as V2X (vehicle-to-everything) communication.^[1]

Extensive V2X communication can optimize traffic routes and coordinate lane signaling. The U.S. government is now funding advancements to V2X communication technology in hopes of improving road safety and efficiency.^[2]

How self-driving cars perceive their environment

Like human drivers, self-driving vehicles need to be aware of other vehicles, obstacles, and roadside infrastructure. To do this, self-driving cars use an array of cameras and sensors to perceive their environment.

As a car navigates the roads, multiple obstacles exist at different speeds and distances. For instance, a pedestrian moves differently from a cyclist, who moves differently from a vehicle. This is where deep learning comes in. A self-driving car relies on data points collected from millions of miles of driving to understand these movements and react accordingly.

A similar process is used to perceive road signs and general traffic rules. If a robotaxi enters a school zone, its camera system will “see” the sign denoting that. Or, if the road has a double yellow line, the car’s infrared sensors will pick up these lane markings. Together, the self-driving car’s system of sensors and cameras helps it understand its environment in real time.

The decision-making process

Driving involves a series of small decisions, from steering and braking to accelerating and lane changes. Self-driving cars use a series of complex algorithms to make the right choice in each situation.

Autonomous vehicles make decisions at several different levels. At the highest level is path planning, which occurs at the beginning of the trip. The car uses its mapping system and real-time traffic data to determine the most efficient path to its destination.

Once on the path to its destination, the car uses motion control to position itself safely. Its intelligence system assesses the current speed and longitudinal position to determine if the car should brake or accelerate, while its lateral position determines if steering is needed.

Then, a self-driving car uses behavior prediction for those split-second decisions and unpredictable scenarios. For instance, if a jaywalker enters its field of vision, a self-driving car will use its neural networks to predict the speed and distance of pedestrians to stop in time.^[3]

Levels of autonomy

As of now, degrees of automation vary by vehicle. Some newer models might have lane assist and a few other automated features but still require a driver behind the wheel. Others are closer to fully autonomous and require minimal human intervention.

Having a common technical language to describe these different levels of autonomy is useful. Below are the accepted terms the automotive industry uses to describe automated vehicles:^[4]

• 

  Level 0 — No automation

  These vehicles include no automated features but may have some warnings and momentary assistance, such as automatic emergency braking, blind-spot warning, and lane-departure warning. Drivers must constantly supervise these features and remain fully engaged behind the wheel.

• 

  Level 1 — Driver assistance

  Vehicles in this category have more advanced driver assistance features, which provide steering or braking/acceleration support. Lane centering and adaptive cruise control are common features in Level 1 vehicles.

• 

  Level 2 — Partial automation

  Level 2 vehicles include driver support features that assist in steering and braking/acceleration. While a Level 1 vehicle might have lane centering or adaptive cruise control, a Level 2 vehicle possesses both.

• 

  Level 3 — Conditional automation

  Drivers in these vehicles aren’t “driving,” even if they’re in the driver’s seat. But automated features only kick in during limited situations and may require driver assistance. For instance, the traffic-jam chauffeur feature takes over during heavy traffic but needs driver assistance once traffic clears.

• 

  Level 4 — High automation

  Level 4 vehicles don’t require the driver to “take over” at any point. But the conditions for autonomy are still limited to specific situations. Local driverless taxis are an example of Level 4 vehicles — they only drive in certain areas, but they’re fully autonomous.

• 

  Level 5 — Full automation

  Imagine a driverless taxi that isn’t limited by ZIP code. These fully autonomous cars can drive anywhere at any time — with no driver supervision needed.

Challenges and limitations

While self-driving cars have made major strides in recent years, they still come with a range of concerns. From complex technical challenges to regulatory issues, the following obstacles could slow the large-scale adoption of self-driving vehicles over the coming years.

Technical challenges

Certain urban environments pose a technical challenge for self-driving cars. For instance, New York City’s complex street pattern and frequent jaywalking have city officials considering a robust application process and extensive testing for autonomous vehicles.

And that’s before you consider New York’s winter weather. For now, robotaxis have thrived in cities like San Francisco that see minimal severe weather. But precipitation and fog have the potential to interact with sensors and make automated systems less reliable.

Ethical and legal considerations

The National Highway Traffic Safety Administration (NHTSA) has yet to put in place safety standards for self-driving cars. For now, autonomous vehicle companies must provide detailed crash reports so that governing bodies can accurately assess safety.^[5]

Liability also remains a major issue around self-driving cars, and the NHTSA claims policymakers are still exploring regulatory frameworks around liability. This will ensure victims of self-driving car accidents are adequately compensated.^[6]

Public acceptance and trust

Annual polls around self-driving cars suggest Americans are skeptical of the technology. In 2023, 68% of Americans said they had some fear around self-driving vehicles, and only 9% said they trust the technology. Hacking and privacy also remain major concerns.^[7]

Much of this distrust is due to a few public incidents involving self-driving cars. In October 2023, a driverless Cruise vehicle dragged a pedestrian 20 feet, which led to the California DMV suspending Cruise operations in the state.

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Current state and future prospects

Originally, self-driving vehicles required extensive test-driving on public roads to develop their decision-making systems — a costly and time-consuming process. But recent advancements in AI allow self-driving vehicles to be smarter from day one. This has helped companies like Waabi forecast autonomous trucking for 2025.

The past few years have also seen growing adoption of self-driving robotaxis. As of October 2024, Google’s self-driving vehicle project, Waymo, provides 100,000 paid rides per week — a tenfold increase from 2023. The company’s robotaxi service has gained popularity in San Francisco, Los Angeles, and Phoenix and will soon arrive in Atlanta and Austin.

Industry insiders expect robotaxis to be widely available by 2030 and viable autonomous trucking between 2028 and 2031. Still, big hurdles remain before full-scale adoption is possible, like technical obstacles, capital availability, and the need for regulation.

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