1. Introduction

With the rapid advancement of science and technology, the intersection of autonomous driving technology and the vehicle industry has emerged as an important research area. Radar sensors, as a major component of autonomous driving systems, are critical for monitoring the surrounding environment and supplying circumstances for artificial intelligence to make decisions. According to the Society of Automotive Engineers' (SAE) categorization standard, technology can be split into six progressive levels ranging from L0 to L5, with the L5 level of autonomous driving indicating the ability to drive a car totally without human intervention [1]. China has made significant investments in the field of autonomous driving, with IT giants including as Baidu, Huawei, and DDT, as well as automobile companies like Azure and BYD, actively developing the technology. Baidu's Apollo platform is one of the most influential open-source platforms for autonomous driving in China, supporting the development of L4-level autonomous driving vehicles and integrating a diverse set of sensors, including radar sensors, for environment sensing and path planning [2]. Internationally, research on autonomous driving technologies is more advanced. Google's Waymo project is a pioneer in the field of autonomous driving [3], and its vehicles are outfitted with powerful radar sensors that can detect their surroundings in real time. Tesla's Autopilot system also uses radar sensors to improve its autopilot skills, and it is also developing its L2 level autopilot system, which can perform autopilot operations on highways and is constantly upgrading its autopilot capabilities through OTA upgrades. Furthermore, many European research institutes, like Germany's Fraunhofer Institute, have made important contributions to radar sensor technology and application. German automakers such as Daimler, BMW, and Volkswagen have extensive technological expertise in autonomous driving technologies.

The goal of this research is to comprehensively evaluate the working principle of radar sensors and their application in autonomous driving technology, identify the challenges encountered, and propose possible improvement strategies while looking into the future.

The results of this research will provide a theoretical basis for the safety and reliability of automated driving systems, and provide guidance in combining radar sensors and the field of automated driving, thus promoting the development of the entire automated driving industry.

2. Radar sensor working principle

In complex traffic environments, drivers may fail to recognize potential dangers in time due to tiredness or distraction, resulting in accidents. currently makes it possible to safely use artificial intelligence(AI)to avoid risks while using autonomous driving technology at this point. In order to guarantee safety, artificial intelligence(AI)uses a variety of sensors to obtain real-time information about the environment and make rapid decisions, such as emergency braking or lane changes. The main use of autonomous driving technologies is to combine artificial intelligence with the current automotive sector. At its core, they use a range of sensors and complicated algorithms to achieve complete environmental awareness, optimal path planning, and autonomous vehicle control. Because of its importance to autonomous driving, sensors are the basis of safe driving.

In self-driving vehicles, sensors are the basis for realizing environment sensing. Radar sensors use electromagnetic waves to detect targets, with all-weather working ability, strong anti-interference ability and high-ranging accuracy. Compared with cameras and LiDAR, even in some bad weather conditions, such as rain, snow, and haze, radar sensors can still work well[4][5], so radar sensors play an indispensable role in the automatic driving. The principle of operation of radar sensors is based on the transmission and reception of electromagnetic waves. Radar calculates the distance and speed of a target by transmitting and receiving electromagnetic wave signals and measuring the round-trip time and frequency changes of the signals. This principle is widely used in areas such as self-driving, aviation, and meteorology.

3. Radar sensors in ADAS applications

In the field of autonomous driving, radar sensors are widely employed, especially in the field of advanced driver assistance systems(ADAS). According to research radar sensors are widely used in adaptive cruise control (ACC) blind spot monitoring systems and so on, which can effectively improve driving safety.[6]

3.1. Autonomous emergency braking

Autonomous Emergency Braking (AEB) is a critical safety feature in modern automobiles, pioneered by Chinese automaker Volvo with its City Safety system. This system represents one of the earliest implementations of AEB globally. AEB employs radar and camera technology to continuously monitor the traffic conditions ahead. It is specifically designed to avert or mitigate collisions at low speeds by promptly engaging the brakes autonomously. In the event that the AEB system identifies a potential collision risk, it initiates a series of warning signals. These warnings are communicated to the driver through auditory, visual, or tactile cues, prompting the driver to take necessary evasive maneuvers. Should the driver fail to respond adequately within the critical timeframe, the AEB system subsequently and automatically engages the vehicle's brakes to mitigate the impact of the impending collision.

3.2. Blind spot monitoring

Blind Spot Monitoring (BSM) technology is an important automotive safety aid. It is produced by several research institutes and companies, such as Ford, Nissan, Visteon, Valeo, etc. BSM systems monitor traffic conditions behind the side of the vehicle in real-time through the use of sensors, such as radar or cameras, and warn the driver when a potential hazard is detected. The Blind Spot Assist system of Mercedes-Benz, a well-known automaker, is a good application case for the BSM system. The BSM system effectively reduces the risk of collision when changing lanes and improves the safety of lane-changing operations [7].

3.3. Automatic parking assist

Automatic Parking Assist (APA) technology is a modern automotive intelligent driving assistance system designed to help drivers automatically complete parking operations in narrow parking spaces[8]. Several research organizations and companies such as BMW, Mercedes-Benz, Bosch, and Continental are key manufacturers. An example is Tesla's Autopilot system. The system integrates advanced automatic parking assistance functions, detects parking spaces through cameras and ultrasonic sensors, and automatically controls the vehicle's steering, acceleration, and braking to complete parking operations. For some drivers who have difficulty parking, the APA system greatly improves the convenience and success rate of parking, reduces accidents caused by improper parking, and gives drivers a sense of comfort and security.

3.4. Adaptive cruise control

Major automobile manufacturers such as BMW, Mercedes-Benz, Tesla, and component suppliers such as Bosch, Delphi, etc. Adaptive Cruise Control (ACC) is an advanced automotive driver assistance system designed to reduce the burden on the driver at high speeds by automatically adjusting the vehicle speed to maintain a safe distance from the vehicle in front [9]. The Tesla Autopilot system mentioned above also integrates adaptive cruise control, which detects the vehicle in front of it in real time through cameras and radar sensors, and automatically adjusts the vehicle speed to maintain a safe distance. the ACC system greatly improves driving comfort, reduces driver fatigue, improves fuel efficiency, and reduces the risk of traffic accidents. Assume that there are two vehicles, vehicle a behind and vehicle b in front. The system keeps track of the vehicle in front of a vehicle when it detects vehicle b,which records its speed and distance. The system keeps track of the vehicle in front of a vehicle when it detects vehicle b, which records its speed and distance. The ACC system automatically adjusts the speed of vehicle A to maintain a pre-set safe distance from vehicle B in front, based on the speed and distance of vehicle B.

In summary, the application of radar sensors in ADAS is the result of the joint efforts of automobile manufacturers, technology companies and research organizations, all of which aim at solving the various problems faced by drivers in different driving scenarios, such as fatigue driving, emergency response, blind-spot monitoring, backing up safety, parking difficulties and so on. These applications not only improve driving comfort and convenience, but also significantly enhance road safety, reduce traffic accidents, and protect the lives and properties of drivers and others. With the continuous progress of technology, the application of radar sensors in ADAS will be more extensive and in-depth, laying a solid foundation for the future intelligent transportation system.

4. Challenges and future directions

However, although the application of radar technology in autonomous driving has made significant progress, its development and application still face many challenges, but also contains a huge potential for development.

First of all, in order to realize more accurate environment sensing, the resolution of radar should be improved. Although high-frequency radars (e.g., 77 GHz and 79 GHz) already have high resolution, it is still a challenge to further improve the resolution to realize more accurate environmental sensing. Current research into novel antenna designs and signal processing algorithms to improve resolution Future development of radar sensors toward higher frequencies (e.g., 94 GHz) and higher resolution is a mainstream approach. High-frequency radar will provide finer environmental data to enhance the perception of the automatic driving system, and at the same time feedback information to the system, so that the system can have a more accurate judgment. Secondly, automatic driving systems usually need to combine multiple sensors to improve the perception ability. Through the fusion of multiple sensors such as camera, LIDAR, radar, etc., the automatic driving system can perceive the surrounding environment in real time and recognize the road, vehicles, pedestrians, traffic signs and so on. Based on the fused environment sensing data, real-time data transmission and collaborative processing between multiple sensors is realized through 5G communication technology to plan safe driving paths and realize autonomous navigation and obstacle avoidance. Similarly future radar sensors may integrate multiple modes of operation, such as long-range detection, short-range obstacle avoidance, and high-resolution imaging, to meet the needs of different driving scenarios. Multi-mode radar will provide more comprehensive environment sensing capability and be able to flexibly switch operating modes in different environments for more efficient and accurate detection and identification. It usually combines multiple radar technologies, such as pulse radar, continuous wave radar, frequency modulated continuous wave (FMCW) radar, Doppler radar, etc., to adapt to different application scenarios and mission requirements. Combining multiple technologies to realize environment sensing, radar sensors will generate a large amount of data that needs to be processed and analyzed in real time, which puts high demands on the computing power and data processing efficiency of the on-board computing platform. The combination of edge computing and cloud computing is used to optimize the data processing process, so that the system's real-time response capability and data processing and computation capability are improved to better meet the needs of different situations [10].

5. Conclusion

This article explores the principles and applications of radar technology in autonomous driving and its challenges and potential. Detailed introduction of the wide range of applications of radar sensors in Advanced Driver Assistance Systems (ADAS), in particular the key role in technologies such as Adaptive Cruise Control (ACC), Blind Spot Monitoring (BSM), Automatic Emergency Braking (AEB), and Automated Parking Assistance (APA) as well as the challenges and potentials regarding the enhancement of the radar resolution, the multi-sensor fusion, the design of multi-modal radars, and the data processing and computational platforms and the challenges and future of optimization. And demonstrated that these technologies significantly improve driving safety and comfort, reduce traffic accidents, and protect the lives and property of drivers and others. By combining high-frequency radar, 5G communication technology, and multiple radar modes, the sensing capability and environmental adaptability of autonomous driving systems are significantly improved. The key role of radar technology in realizing safer and more efficient autonomous driving is also emphasized. In the future, with the continuous progress of technology, the application of radar sensors in ADAS will be more extensive and in-depth as well as further improve the radar resolution and multi-modal integration, and explore more efficient computation and data processing methods, in order to promote the comprehensive development of autonomous driving technology. To lay a solid foundation for the realization of intelligent transportation system and promote the comprehensive development of automatic driving technology.