What is ADAS (Advanced Driver Assistance Systems)?

Significant automotive safety improvements in the past (e.g., shatter-resistant glass, three-point seatbelts, airbags) were passive safety measures designed to minimize injury during an accident. Today, ADAS systems actively improve safety with the help of embedded vision by reducing the occurrence of accidents and injury to occupants.

The implementation of cameras in the vehicle involves a new AI function that uses sensor fusion to identify and process objects. Sensor fusion, similar to how the human brain processes information, combines large amounts of data with the help of image recognition software, ultrasound sensors, lidar, and radar. This technology can physically respond faster than a human driver ever could. It can analyze streaming video in real time, recognize what the video shows, and determine how to react to it.

These are some of the most common ADAS applications:

• Adaptive Cruise Control: Adaptive cruise control is particularly helpful on the highway, where drivers can find it difficult to monitor their speed and other cars over a long period of time. Adaptive cruise control can automatically accelerate, slow down, and at times stop the vehicle depending on other objects in the immediate area.
• Glare-Free High Beam and Pixel Light: Glare-free high beam and pixel light use sensors to adjust to darkness and the vehicle’s surroundings without disturbing oncoming traffic. This new headlight application detects the lights of other vehicles and redirects the vehicle’s lights away to prevent other road users from being temporarily blinded.
• Adaptive Light Control: Adaptive light control adapts the vehicle’s headlights to external lighting conditions. It changes the strength, direction, and rotation of the headlights depending on the vehicle’s environment and darkness.
• Automatic Parking: Automatic parking helps inform drivers of unseen areas so they know when to turn the steering wheel and stop. Vehicles equipped with rearview cameras have a better view of their surroundings than traditional side mirrors. Some systems can even complete parking automatically without the driver’s help by combining the input of multiple sensors.
• Autonomous Valet Parking: Autonomous valet parking is a new technology that works via vehicle sensor meshing, 5G network communication, and cloud services that manage autonomous vehicles in parking areas. Sensors provide the vehicle with information about where it is, where it needs to go, and how to get there safely. All this information is methodically evaluated and used to perform drive acceleration, braking, and steering until the vehicle is safely parked.
• Navigation System: Car navigation systems provide on-screen instructions and voice prompts to help drivers follow a route while concentrating on the road. Some systems can display exact traffic data, and if necessary, plan a new route to avoid traffic jams. Advanced systems may even offer heads-up displays to reduce driver distraction.

• Night Vision: Night vision systems enable drivers to see things that would otherwise be difficult or impossible to see at night. There are two categories of night vision implementations: Active night vision systems project infrared light, and passive systems rely on the thermal energy that comes from cars, animals, and other objects.
• Unseen Area Monitoring: Unseen area detection systems use sensors to provide drivers with important information that is otherwise difficult or impossible to obtain. Some systems sound an alarm when they detect an object in the driver’s unseen area, such as when the driver tries to move into an occupied lane.
• Automatic Emergency Braking: Automatic emergency braking uses sensors to detect whether the driver is in the process of hitting another vehicle or other objects on the road. This application can measure the distance of nearby traffic and alert the driver to any danger. Some emergency braking systems can take preventive safety measures such as tightening seat belts, reducing speed, and engaging adaptive steering to avoid a collision.
• Crosswind Stabilization: This relatively new ADAS feature supports the vehicle in counteracting strong crosswinds. The sensors in this system can detect strong pressure acting on the vehicle while driving, signaling small steering or braking corrections to help the driver maintain the vehicle’s intended path/help keep the vehicle centered.
• Driver Drowsiness Detection: Driver drowsiness detection warns drivers of sleepiness when it detects they may need a break from driving. There are several ways the system monitors whether a driver’s attention is decreasing such as steering inputs, lane-keeping behavior, vehicle speed, and driver-facing camera data. In one case, sensors can detect the movement of the driver’s head and heart rate to determine whether they indicate drowsiness. Other systems issue visual, audible, or haptic warnings.
• Driver Monitoring System: The driver monitoring system is another way of measuring the driver’s attention. The camera sensors can analyze whether the driver’s eyes are on the road or drifting. Driver monitoring systems can alert drivers with noises, vibrations in the steering wheel, or flashing lights. In some cases, the car will take the extreme measure of stopping the vehicle completely.
• 5G and V2X: This hot new 5G ADAS feature provides communication between the vehicle and other vehicles or pedestrians with increased reliability and lower latency, generally referred to as V2X. Today, millions of vehicles connect to cellular networks for real-time navigation. This application will enhance existing methods and the cellular network to improve situational awareness, control or suggest speed adjustments to account for traffic congestion, and provide real-time updates to GPS maps. V2X is essential to support over-the-air software updates for the now-extensive range of software-driven systems in cars, from map updates to bug fixes to security updates and more.

According to the National Highway Traffic Safety Administration (NHTSA), 36,640 people were killed in motor vehicle crashes on U.S. roads in 2025, highlighting that roadway safety remains a critical challenge. Compared to 2024, there was a 6.7% decline. While fatalities have declined from recent peak levels, human factors such as driver distraction, impaired driving, poor decision-making, and fatigue continue to be major contributors to crashes. NHTSA crash-causation research has consistently shown that driver-related factors play a role in the vast majority of crashes.

The opportunity to reduce accidents and save lives makes ADAS increasingly important. Features such as automatic emergency braking (AEB), pedestrian detection, surround view monitoring, parking assist, driver drowsiness detection, and other driver monitoring systems help drivers avoid or mitigate collisions by providing safety-critical warnings and interventions. As vehicles become more intelligent, ADAS technologies are playing a key role in improving road safety and forming the foundation for higher levels of vehicle automation.

Shifting toward fully autonomous cars (vehicles capable of sensing their environment and operating without human involvement) requires an increase in the electronic architecture of these vehicles.

With the increase in electronic architecture comes an increase in the volume of data. To handle this data, the new integrated domain controllers require higher computing performance, lower power consumption, and smaller packaging.

The adoption of 64-bit processors, neural networks, and AI accelerators to handle the high volume of data requires the latest semiconductor features, semiconductor process technologies, and interconnecting technologies to support ADAS capabilities.

Further, the reduction of electronic modules leads to centralized computing architectures, requiring critical automotive building blocks, including processors with vision processing capabilities, neural networks, and sensor fusion. All must be achieved while addressing the need for quality, safety, and security.

Every aspect of the car is designed to be more connected, requiring subsystem and SoC designers to expand the scope of safety measures beyond the traditional steps taken to ensure physical safety. Applying the latest embedded computer vision and deep learning techniques to automotive SoCs brings greater accuracy, power efficiency, and performance to ADAS systems.

Achieving Level 5 autonomy will require enhancements to existing features and the integration of sensor-fusion techniques, greater reliance on AI-driven decision making and robust communications capabilities such as vehicle-to-everything (V2X). Unforeseen driving scenarios (like extreme weather conditions), compliance with regulatory standards, cybersecurity risks, and driver education are some hurdles that complicate ADAS evolution. But as these systems continue to improve in detection, perception, and execution, they will ultimately enable fully autonomous vehicles.