Analysis of the main functions of mainstream sensors for autonomous vehicles

In recent years, autonomous driving has undoubtedly become a hot topic in the technology and automotive circles. Technology companies such as Google, Baidu, Apple, and Uber, as well as mainstream automakers such as Tesla, Audi, Mercedes-Benz, and BMW, have invested in the field of autonomous driving. However, the battle over the road to self-driving technology is also a hotly debated topic.

One is the belief that self-driving cars will ensure a better future by increasing road safety, reducing infrastructure costs, and enhancing mobility for children, the elderly and the disabled. On the other hand, many fear car hacking, the risk of fatal crashes, and the loss of driving-related jobs. The survey found that 54 percent of adults are concerned about the development of self-driving cars, while only 40 percent of respondents are optimistic about the potential development of automotive automation. Research also shows that people have very different perceptions and attitudes towards self-driving cars.

There is no doubt that autonomous driving is a complex and controversial technology. To understand the safety of self-driving cars, it’s important to figure out how they work and what types of self-driving vehicle sensors can help them navigate and identify objects on the road to prevent crashes. Next, I will take a look at the sensor technology solutions in autonomous vehicles with everyone.

Autonomous driving is the final direction of the development of automobile intelligence. Sensors such as lidar, camera, millimeter-wave radar, and ultrasonic sensor are the hardware basis for realizing autonomous driving. Self-driving cars would not be possible without IoT sensors: they allow the car to see and sense everything on the road and gather the information it needs to drive safely. For example: this information is processed and analyzed to build a path from point A to point B and send appropriate commands to the car’s controls, such as steering, acceleration and braking. Additionally, the information collected by IoT sensors, including actual paths, traffic jams, and obstacles on the road, can be shared among IoT vehicles. This is known as vehicle-to-vehicle communication and helps improve driving automation.

Three types of autonomous vehicle sensors are commonly used by most automakers today: cameras, radar, and lidar. Next let’s see how they work.


Cameras are like the eyes of a human driver, and self-driving cars use cameras to observe and interpret objects on the road. By equipping cars with cameras at every angle, these vehicles can maintain a 360° view of the outside environment and provide a wider picture of surrounding traffic conditions. Today, 3D cameras can be used to Display very detailed lifelike images. Image sensors automatically detect objects, classify them, and determine the distance to the object. For example, cameras can identify other cars, pedestrians, cyclists, traffic signs and signals, road markings, bridges and guardrails.

Radar (radio detection and ranging) sensors make a crucial contribution to the overall functionality of autonomous driving: they emit radio waves to detect objects and measure their distance and speed in real time. Short-range and long-range radar sensors are typically deployed throughout the car and have different capabilities. Short-range (24 GHz) radar applications enable blind-spot monitoring, lane-keeping assist, and parking assist, while long-range (77 GHz) radar sensor roles include automatic distance control and brake assist. Unlike cameras, radar systems usually have no problem identifying objects in fog or rain.

Lidar (light detection and ranging) sensors work like radar systems, the only difference is that they use laser light instead of radio waves. In addition to measuring distances to various objects on the road, lidar can create 3D images of detected objects and map their surroundings. Compared with LiDAR, millimeter-wave radar has the advantages of long detection range, unaffected by weather conditions and low cost. Since the Bomi-wave radar uses silicon-based chips, it is not particularly expensive and does not involve complex processes. At the same time, it is in an important period of the second process transformation. It is expected that there is still room for cost reduction. Compared with the temporarily unattainable cost of lidar, lower technical barriers and the advantage of being able to work around the clock, millimeter-wave radar can be said to be a lower threshold for startups to enter the autonomous driving market.

But instead of relying on a narrow field of view, the lidar can be configured to create a full 360-degree map around the vehicle. Self-driving car makers such as Google, Uber, and Toyota that have these two advantages have opted for lidar systems. About the application of LiDAR sensors in autonomous driving The solid-state area array LiDAR ranging sensor provided by – CE30-A uses the time-of-flight (TOF, Time of Flight) method for ranging, and it emits a modulated near- Infrared light, the light is reflected by the object and received by the CE30 again. CE30 converts the distance of the photographed scene by calculating the phase difference and time difference between light emission and reception.

In the future of self-driving cars Sensors play a vital role in autonomous driving: they enable the car to monitor its surroundings, detect obstacles and plan the road. Combined with car software and computers, they will allow the system to take full control of the vehicle, saving people a lot of time to perform more efficient tasks. Considering the fact that the average driver spends about 50 minutes a day in the car, imagine how invaluable self-driving cars are to the fast-paced world we live in. Although autonomous driving technology is rapidly advancing, there are no Level 4 standards required for commercial vehicles to pass autonomous driving. There is still a huge area of ​​technological improvement that manufacturers need to take seriously in order to ensure road safety.

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