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How to Build ADAS Technology for Autonomous Driving

Learn about ADAS technology that can give us the ultimate driving safety and comfort

January 23, 2019

8 mins read

Innovation takes time. We won’t see fully autonomous vehicles congesting the urban streets any time soon. But OEMs and Tier 1 providers are actively exploring certain core self-driving car technologies. ADAS technology is the decisive piece of the puzzle. It’s critical for the safety, comfort, and ease of driving. And ADAS technology’s value will only continue to rise as autonomous cars approach the city streets.

Let’s find out how ADAS technology gets us closer to a fully automated future.

How to Build ADAS Technology for Autonomous Driving


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OEMs and Tier 1 vendors should build ADAS technology to beat the competition and meet safety requirements

How to Build ADAS Technology for Autonomous Driving

The need to address safety issues on the road has crystallized into concrete government regulations. Official policies oblige OEMs and Tier 1 suppliers to use ADAS elements like rearview cameras in all new vehicles under 10,000 pounds (4,500 kilograms) and include autonomous emergency braking (AEB) systems in buses and trucks. These and similar regulations have become the biggest motivators for automakers to pursue ADAS opportunities.

Quick takeaways from global ADAS market developments between 2018–2022

How to Build ADAS Technology for Autonomous Driving

Source: Technavio’s report on the Global Automotive Advanced Driver Assistance System Market

The combination of increasing computing power, improved sensor technologies, and better software solutions can help automotive companies beat off the intense competition. No wonder ADAS technology is the fastest-growing segment in the automotive ADAS market.

Building ADAS in automotive has already saved lives, is improving safety today, and will do even more in the future

Consumers love the comfort and safety provided by parking assist, blind zone monitoring, and other advanced systems. And they love safety ratings, of course. Authorities around the world are fighting for safety improvements and fewer road fatalities. The development of advanced driver assistance systems is governed by international safety standards including IEC-61508 and its derivative, ISO 26262.

Historical data suggests that ADAS safety systems reduce traffic fatalities.

How to Build ADAS Technology for Autonomous Driving

Source: EE Times, Automotive King Redefines Future

OEMs respond to market demand and regulatory requirements, working toward full driving automation. You can be sure that we’ll soon see an increasing number of ADAS in automotive that help with monitoring, warning, braking, and steering.

ADAS technology and its uses reflect trends in the automotive industry

The ultimate goal of ADAS feature development is to make our roads safer and better suited for fully autonomous vehicles in the long run. Still, manufacturers and buyers shouldn’t underestimate the importance of ADAS for meeting current automotive challenges. The most significant impact of advanced driver assistance systems is in providing drivers with essential information and automating difficult and repetitive tasks. This increases safety for everyone on the road.

Below, you’ll find an infographic showing principal ADAS features and their uses. It also shows the critical components behind each solution.

Principal ADAS features

How to Build ADAS Technology for Autonomous Driving

Source: Electronic Specifier, What is driving the automotive LiDAR and RADAR market?

The chart below shows the value of each component within the ADAS solutions market, which is broken down into the following categories:

  • Adaptive Cruise Control (ACC)
  • Blind Spot Detection (BSD)
  • Park Assistance
  • Lane Departure Warning Systems (LDWS)
  • Tire Pressure Monitoring Systems (TPMS)
  • Autonomous Emergency Braking (AEB)
  • Adaptive Front Lights (AFL)
  • Other systems

The size of the ADAS market in the US by solution type, 2014–2025 (USD billion)

How to Build ADAS Technology for Autonomous Driving

Source: Grand View Research, ADAS Market Size, Share & Trend Analysis Report by Solution

Find out how Intellias developed ADAS solution for electric vehicles

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Five pillars of ADAS in automotive to ensure safety and connectivity

Applications related to self driving car technology include various state-of-the-art tools that depend on each other.

Pillars of ADAS in automotive that are key to autonomous driving and product differentiation

How to Build ADAS Technology for Autonomous Driving

Source: McKinsey & Company, Advanced driver-assistance systems: Challenges and opportunities ahead

Speaking of ADAS feature development and software architecture, we’d like to single out five components that are worth in-depth discussion: sensors, processors, software algorithms, mapping solutions, and actuators.

These elements can be roughly grouped into three subsystems responsible for:

  • data acquisition and processing
  • data fusion and decision-making
  • taking action

System-level design for ADAS in automotive

How to Build ADAS Technology for Autonomous Driving

Source: The Embedded Vision Alliance, Complex Trends and Challenges in Designing ADAS

Sensor technologies are at the forefront of ADAS technology

ADAS vision systems and ADAS safety systems require lots of fused sensors to monitor the vehicle’s surroundings and what’s going on inside the car. The most commonly used ADAS sensors today are lidar, radar, and ultrasonic.

Software for self driving cars featuring ultrasonic sensors usually consists of multiple sensors located in the front and rear bumpers and side-view mirrors. They transmit short sound waves and measure the time it takes for them to travel to a target object and return to the receiver.

Short-range and long-range applications of sensor technology

How to Build ADAS Technology for Autonomous Driving

Source: Electronic Design, Radar and Ultrasonic Sensors Strengthen ADAS Object Detection

An ADAS safety system can rely on ultrasonic sensor technology for low-speed and short-range applications such as blind spot detection, self-parking, and parking assistance. Radar and lidar are both used by ADAS engineers for object detection, collision prevention, and interaction with traffic management systems.

Still, there are differences between these technologies. Lidar is the best solution for real-time detection, but it’s unpleasantly expensive for mass deployment. Radar sensors, especially long-range ones, are reliable enough and cheaper but lack precision when detecting small objects.

Processors are indispensable for driving automation at any level

Cameras pointed in all directions, radar and lidar sensors, and multiple displays gather and present information to drivers. Computing all of this data requires high-performance processors. And the need for processing power will grow with future ADAS advancements.

The effectiveness of advanced driver assistance systems is measured by the car’s ability to sense, perceive, and react. But without enough processing power, a computer can’t decide how the car should behave in a particular real-time situation on the road. Traditional low-level programming technologies, which require lengthy development and are difficult to maintain, are inappropriate for most ADAS safety systems.

How to Build ADAS Technology for Autonomous Driving

Modern cars have more than 100 microprocessors and up to 100 million lines of code. So OEMs should choose their processors smartly. The task of seasoned engineers for self driving car technology is to build a sophisticated system that will maintain high-speed transfers with increasing amounts of data to analyze the car’s surroundings and act accordingly. Moreover, engineering companies need to tackle the challenge of multicore architectures and high frequencies with low power consumption.

Autonomous Driving & ADAS Software Development

Build adaptable ADAS for safer and more comfortable driving

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ADAS algorithms to propel safe and smart driving

The ADAS market is growing rapidly, and so is ADAS algorithm development. A variety of algorithms are powering life-saving systems. Among them, vision and image processing algorithms are real game changers for ADAS feature development.

Visual sensors are the primary type used for driving, which is why computer vision will play a crucial role in autonomous cars. ADAS algorithms use input from cameras and sensors to incorporate environmental elements into self driving car technology. Then the output provided by these algorithms to the actuation system either warns the driver of potential hazards or gives directions to the ADAS on how to act.

Lane departure warning and lane detection systems, adaptive cruise control, headlight control, autonomous emergency braking, collision warning, pedestrian protection – these are just some of the ADAS applications developed using vision and image processing.

Mapping in ADAS technology sets new standards for in-car navigation

Autonomous vehicle navigation in urban environments requires higher accuracy than GPS-based systems can offer. Besides, GPS may fail, causing what would be an ordinary situation in a car with a human driver to turn into a life-threatening situation in a self-driving vehicle. That’s why advanced driver assistance systems should ensure greater precision, increased stability within environments with dynamic obstacles, and the ability to learn and improve maps over time.

How to Build ADAS Technology for Autonomous Driving

Tech startups, established tech providers, and manufacturers are competing and partnering with each other to work out sophisticated mapping algorithms that store and update geographical and infrastructure information. By incorporating these mapping solutions into ADAS vision and ADAS safety systems, location can be pinpointed to centimeters. OEMs can achieve real-time cloud navigation services, environment perception, planning, and decision-making.

Learn how Intellias helped equip German premium cars with the most advanced driver assistance functions on the market.

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Actuators as major enablers of self driving car technology

The actuation system terminates the chain of events controlled by advanced driver assistance systems. It facilitates interactions between a vehicle’s components and takes prompt actions based on computed results.

Through programmed sequences, the actuation ADAS subsystem reacts to the object recognition results, which are processed into commands to control the vehicle. Automatic actuators allow a wide range of operations from visual, acoustic, or haptic warnings to electric power steering, autonomous acceleration, and braking.

Actuation system within ADAS vehicle architecture

How to Build ADAS Technology for Autonomous Driving

Source: Hitachi Automotive Systems Americas, Inc., Advanced Driver Assistance System

Testing ADAS in automotive helps to ensure vehicle safety and convenience

Advanced driver assistance systems aren’t only connected to vehicle components; they’re also closely interconnected. Vision and camera systems, air conditioning systems, and networks of sensors can no longer be tested in isolation. Only testing of ADAS technology in the framework of the entire vehicle will give precise results.

This isn’t the end of the testing challenges, though. There’s one more type of dependency to consider: smart and interconnected external networks such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) systems. The high interdependency of internal and external subsystems makes the testing scope enormous. Only experienced ADAS engineers can cope with it.

In recent years, the automotive industry has made huge steps toward autonomous driving. And advanced driver assistance systems lie at the core of these achievements. Although ADAS features haven’t become mainstream yet, the goal of fully automated driving is no longer a fantasy.

OEMs and Tier 1 suppliers may have difficulties with ADAS feature development, considering current and evolving legislation and the necessity to integrate ADAS technology into existing and new vehicle architectures. These are just some of the issues that force automotive players to seek external partnerships.

At Intellias, we’re working on several impressive autonomous driving projects. We’re developing ADAS features and ADAS algorithms and know exactly how to test and implement automotive ADAS in automotive. Contact us to speak with one of our automotive experts.

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