Safety on the roads is a massive problem – every year, more than 1.1 million people are killed due to road traffic crashes,1 and an estimated 20 to 50 million people suffer injuries.
A major cause of these crashes is driver error. 2 Carmakers and authorities are always looking for ways to improve safety, and in recent years there have been big advances in how advanced driver assistance systems (ADAS) can help reduce deaths and injuries on the roads.
In this article, we’ll look at the role of ADAS in improving safety and the various sensor technologies that play a vital part in making this possible.
The Evolution and Importance of ADAS
Since the first anti-lock braking systems (ABS) were introduced in the 1970s, there has been a steady increase in the deployment of ADAS technologies in our vehicles, with corresponding improvements in safety. In the U.S. alone, the National Safety Council (NSC) estimates that ADAS has the potential to prevent about 62% of total traffic fatalities, saving more than 20,000 lives each year. 3
While there are multiple technologies required in ADAS systems, the most critical is the sensor. To provide the vehicle with the necessary information to assist the driver, we must use high-quality cameras and image sensors, along with precise methods for measuring the distance to nearby vehicles and objects. Beyond this, there are a multitude of other sensors in ADAS systems, such as ultrasonic sensors for park assist and blind spot detection, and rain sensors for automatic windshield wipers.
Implementing ADAS systems can be challenging. There are limitations on the processing power available to handle all the data generated by multiple sensors, and the sensors themselves have limitations in their performance. Every component must have excellent reliability, including software algorithms, and extensive testing is needed to ensure safety. The ADAS system must perform consistently in the most difficult weather and lighting conditions, and be able to cope with rain, snow, fog, temperature extremes and more.
Key Sensor Technologies in ADAS
Let’s look at some of the key technologies that are used in ADAS sensors, including image sensors, LiDAR, and ultrasonic sensors.
First, we need to consider sensor fusion, where data from multiple types of sensors is combined by software algorithms. For example, image data that can recognize pedestrians can be integrated with distance-finding LiDAR or radar. This can improve accuracy, sensitivity and reliability, to provide significantly better levels of safety – no one type of sensor can handle all requirements, so sensor fusion provides the redundancy needed.
The complexity of these multi-sensor systems can quickly escalate, with AI algorithms requiring ever more processing power. However, the sensors themselves are becoming more advanced, allowing software to be run locally at the sensor instead of by the central ADAS processor.
Automotive Image Sensors
Image sensors are the eyes of ADAS and are arguably the most important sensor type in any vehicle. They provide image data that can be integrated into a huge range of automotive applications, from lane departure warnings, to sophisticated automated driving, to systems that monitor a driver and sound an alarm if they appear to be falling asleep.
onsemi offers a wide range of image sensors, including its HyperluxTMfamily, which delivers excellent image quality at low power consumption. The sensors include innovative features such as LED flicker mitigation (LFM), which overcomes issues of misinterpretation caused by front and rear LED lighting or LED traffic signs.
Hyperlux image sensors are designed to perform in challenging lighting conditions such as direct sunlight, by offering high dynamic range (HDR) and low-light performance. Cameras with Hyperlux image sensors can handle corner cases with excellent performance that is much better than human vision.
Two examples of onsemi’s Hyperlux image sensors are the AR0823AT and AR0341AT. These are both CMOS digital image sensors which capture both low light and extremely bright illumination in every frame with a 2.1 μm super exposure pixel, which enables up to 150 dB dynamic range without needing auto exposure adjustment.
Depth Sensors (LiDAR)
One of the most important tasks for automotive sensors in ADAS is to precisely measure how far away things are, known as depth sensing. This depth information is essential for various ADAS functionalities, including collision detection, autonomous emergency braking, and active cruise control.
There are multiple technologies that can be used for depth sensing, but for automotive applications, Light Detection And Ranging (LiDAR) is often the best choice. LiDAR enables depth sensing with high depth and angular resolution, and
can operate in all light conditions, due to its active approach of using a near-infrared (NIR) laser transmitter along with a receiver. It is suitable for both short- and long- range applications.
A LiDAR system sends out infrared light, either as a continuous modulated signal, or a series of pulses. It then measures the time of flight (ToF) taken for the signal to reflect from an object and return to the sensor, thus enabling the distance to be calculated.
onsemi’s ARRAYRDM-0112A20-QFN silicon photomultiplier (SiPM) is a high gain, single photon-sensitive sensor used to detect light in visible to NIR wavelengths. It has been designed to achieve high PDE (photon detection efficiency) at the NIR wavelengths of 905/940 nm which are typically used for LiDAR depth sensing applications.
Ultrasonic Sensors
Another technology used for distance measurement is ultrasonics, where a transducer emits a sound wave, at frequencies beyond the range of human hearing, and then detects the sound that bounces back – thus enabling distance measurement from time of flight, in a similar way to LiDAR.
Ultrasonic sensors are typically deployed in close-range obstacle detection, such as parking assistance. One advantage of ultrasonics is that sound is much slower than light, so the time for a reflected sound wave to return to the sensor is typically a few microseconds, as opposed to nanoseconds for light – which means ultrasonic sensors only need low-performance processing, hence reducing system costs.
An example of an ultrasonic sensor is onsemi’s NCV75215 parking distance measurement ASSP. During vehicle parking, this operates with a piezoelectric ultrasonic transducer to provide time of flight measurement of an obstacle’s distance. It detects objects at distances from 0.25 m up to 4.5 m and provides high-sensitivity, low-noise operation.
Conclusion
onsemi has played a major role in the development of ADAS, and the sensor technologies required. For example, onsemi invented the dual gain pixel technology and HDR operation now used in many sensors, with an innovative ‘super exposure’ design that enables the sensor to provide both excellent low-light performance, and the ability to capture bright objects and scenes without saturation. Today, the majority of ADAS systems use image sensors developed by onsemi. 4
These innovations have enabled onsemi to provide high-performance sensors for automotive applications, which in turn have enabled ADAS systems to make a dramatic impact on improving vehicle safety.
The automotive industry continues to invest heavily in ADAS and pursue the goal of full autonomy for our cars – moving beyond features that support drivers to genuine self-driving capabilities (as defined by SAE as Levels 3, 4 and 5). 5 Reducing road deaths and injuries is one of the major motivations behind this trend, and onsemi’s sensor technologies will play a vital role in this transformation in automotive safety.
Interested in this technology?
Learn more about onsemi’s sensors and their capabilities in the ADAS system solution guide.
