What is an Autonomous Car?

The SAE uses the term automated instead of autonomous. One reason is that the word autonomy has implications beyond the electromechanical. A fully autonomous car would be self-aware and capable of making its own choices. For example, you say “drive me to work” but the car decides to take you to the beach instead. A fully automated car, however, would follow orders and then drive itself.

The term self-driving is often used interchangeably with autonomous. However, it’s a slightly different thing. A self-driving car can drive itself in some or even all situations, but a human passenger must always be present and ready to take control. Self-driving cars would fall under Level 3 (conditional driving automation) or Level 4 (high driving automation). They are subject to geofencing, unlike a fully autonomous Level 5 car that could go anywhere.

Autonomous cars rely on sensors, actuators, complex algorithms, machine learning systems, and powerful processors to execute software.

Autonomous cars create and maintain a map of their surroundings based on a variety of sensors situated in different parts of the vehicle. Radar sensors monitor the position of nearby vehicles. Video cameras detect traffic lights, read road signs, track other vehicles, and look for pedestrians. Lidar (light detection and ranging) sensors bounce pulses of light off the car’s surroundings to measure distances, detect road edges, and identify lane markings. Ultrasonic sensors in the wheels detect curbs and other vehicles when parking.

Sophisticated software then processes all this sensory input, plots a path, and sends instructions to the car’s actuators, which control acceleration, braking, and steering. Hard-coded rules, obstacle avoidance algorithms, predictive modeling, and object recognition help the software follow traffic rules and navigate obstacles.

If we compare an autonomous vehicle to a human, the sensors represent the ears and eyes that spot potential hazards. The brain (representing AI) then interprets the surroundings based on what's observed. While sensors today are still not as accurate as human senses, many of them can be combined to build a complete picture of the vehicle's environment. Here are the key sensors on an autonomous vehicle:

• Camera: Acts as the eyes of the vehicle and sees objects in the visible RGB spectrum.
• Lidar: Analyzes distances and determines the distance between the vehicle and an obstacle
• Radar: Measures short-term distances and determines velocity
• Thermal Camera sensor: Is used when cameras aren't suitable (e.g., in tunnels and dark conditions) by measuring  heat signature emitted by people and objects that cannot be seen by the visual camera.
• Ultrasonic sensor: Mounted in bumpers to detect curbs and other cars when parking

The greatest challenges are handling rare edge cases, ensuring safety in all weather and traffic conditions, validating increasingly complex software, and proving reliability at scale. Simulation helps address these roadblocks by creating virtual environments where millions of driving scenarios can be executed rapidly, including situations that are dangerous, expensive, or impractical to reproduce in the real world. This enables teams to evaluate sensor performance, test perception and decision-making algorithms, verify safety requirements, and uncover failures much earlier, reducing development time while increasing confidence before physical testing and deployment.

Fully autonomous (Level 5) cars are undergoing testing in several pockets of the world, but none are yet available to the general public. We’re still years away from that. The challenges range from technological and legislative to environmental and philosophical. Here are just some of the unknowns.

Lidar and Radar

Lidar is expensive and is still trying to strike the right balance between range and resolution. If multiple autonomous cars were to drive on the same road, would their lidar signals interfere with one another? And if multiple radio frequencies are available, will the frequency range be enough to support mass production of autonomous cars?

Weather Conditions

What happens when an autonomous car drives in heavy precipitation? If there’s a layer of snow on the road, lane dividers disappear. How will the cameras and sensors track lane markings if the markings are obscured by water, oil, ice, or debris?

Traffic Conditions and Laws

Will autonomous cars have trouble in tunnels or on bridges? How will they do in bumper-to-bumper traffic? Will autonomous cars be relegated to a specific lane? Will they be granted carpool lane access? And what about the fleet of legacy cars still sharing the roadways for the next 20 or 30 years?

State vs. Federal Regulation

The regulatory process in the U.S. has recently shifted from federal guidance to state-by-state mandates for autonomous cars. Some states have even proposed a per-mile tax on autonomous vehicles to prevent the rise of “zombie cars” driving around without passengers. Lawmakers have also written bills proposing that all autonomous cars must be zero-emission vehicles and have a panic button installed. But are the laws going to be different from state to state? Will you be able to cross state lines with an autonomous car?

Accident Liability

Who is liable for accidents caused by an autonomous car? The manufacturer? The human passenger? The latest blueprints suggest that a fully autonomous Level 5 car will not have a dashboard or a steering wheel, so a human passenger would not even have the option to take control of the vehicle in an emergency.

Artificial vs. Emotional Intelligence

Human drivers rely on subtle cues and non-verbal communication—like making eye contact with pedestrians or reading the facial expressions and body language of other drivers—to make split-second judgment calls and predict behaviors. Will autonomous cars be able to replicate this connection? Will they have the same life-saving instincts as human drivers?

Today’s cars have 100 million lines of code. Tomorrow’s autonomous cars will have more than 300 million lines of code, so cybersecurity is a growing concern.

Synopsys offers a broad portfolio of auto-grade IP, certified for ISO 26262 and ASIL B & D readiness, to help customers build the best chips for applications like ADAS, infotainment, and mainstream MCUs. Synopsys embedded vision processor solutions help customers integrate capabilities like object and facial recognition, night vision, and adaptive cruise control.