The development of automotive technology has entered an intelligent era. Machine vision has been applied in many automotive driving assistance technologies. Technological advances in the field of machine vision will undoubtedly promote the development of automotive driving assistance technology. Therefore, the improvement of image acquisition quality, the optimization of image processing algorithms, how to achieve image intelligence generation, processing, recognition and decision making more quickly are important issues that need to be solved in the field of machine vision.
Editor's Note: Machine vision is an important technology in the field of automotive driving assistance systems. The article mainly reviews the application of machine vision in the fields of lane detection technology, traffic sign recognition technology, vehicle identification technology, pedestrian detection technology and driver status detection technology. This paper focuses on the current research status of machine vision technology in the above-mentioned fields, and provides reference for further research of machine vision in the field of automobile driving assistance.
With the rapid development of China's automobile industry, the number of motor vehicles has increased year by year, and the major harm caused by road traffic accidents to human life and property safety has also become increasingly prominent. According to the World Health Organization's Global Status Report on Road Safety 2013, about 1.24 million people die every year from road traffic, and road traffic injuries are one of the eighth leading causes of death in the world.
In order to improve the road traffic safety situation, many domestic and foreign scientific research institutions and automobile companies have invested a lot of energy in the research and development of automotive safety protection systems. The research and development content has evolved from the earliest mechanical and electronic devices to the hot spot of today's attention--the advanced assisted driving system (ADAS).
The system represented by ADAS applies various sensors on the hardware, such as ultrasonic sensors, vision sensors, radar, GPS, etc., to sense the vehicle's own state and environmental changes during the driving process, and collect vehicle data and environmental data. Based on these data, Conduct traffic scene identification, traffic incident prediction, and give corresponding driving suggestions and emergency measures to assist drivers to make decisions, avoid traffic accidents, and reduce the damage caused by accidents.
In the actual driving process, the driver gets most of the information from the visual, such as: road conditions, traffic signs, markings and signals, obstacles, etc. Research shows that about 90% of environmental information comes from vision, if Good use of visual sensors to understand the road environment is a good choice for vehicle intelligence. Vehicle driving assistance system based on visual navigation for traffic sign detection, road detection, pedestrian detection and obstacle detection can reduce the driver's labor intensity, improve driving safety and reduce traffic accidents.
The driver assistance system uses a large amount of visual information data in the process of providing decision-making advice to the driver. In this respect, visual images have incomparable advantages:
· The visual image contains a large amount of information, such as distance information of objects within the visible range, object shape, texture and color, etc.
· The acquisition of visual information is non-contact, does not damage the road surface and the surrounding environment, and does not require extensive construction of existing road facilities;
· A visual image acquisition can achieve multiple tasks such as road detection, traffic sign detection, obstacle detection, etc.
• There is no mutual interference between vehicles during the acquisition of visual information.
In summary, intelligent vehicle machine vision technology has broad application prospects in intelligent transportation, vehicle safety assisted driving, and automatic driving of vehicles.
At present, visual sensors and machine vision technology are widely used in various advanced assisted driving systems. Among them, the perception of the driving environment is one of the important components of the advanced assisted driving system based on machine vision.
The perception of the driving environment mainly relies on visual technology to perceive the road information, road condition information and driver status when the vehicle is driving, and provides the basic data necessary for the decision-making of the assisted driving system. among them,
· Road information mainly refers to static information outside the vehicle, including: lane lines, road edges, traffic signs and signal lights;
· The road condition information mainly refers to the dynamic information outside the vehicle, including: obstacles in front of the road, pedestrians, vehicles, etc.;
· The driver's status belongs to the in-vehicle information, which mainly includes: driver's fatigue, abnormal driving behavior, etc., to avoid the safety accident caused by the vehicle by reminding the driver of unsafe behavior.
Using machine vision technology to perceive the driving environment, you can obtain static information and dynamic information inside and outside the vehicle to help the driver assistance system make decision-making judgments.
According to the above classification, it can be seen that the key technologies of the advanced machine-assisted advanced assisted driving system currently include: lane line detection technology, traffic sign recognition technology, vehicle identification technology, pedestrian detection technology and driver state detection technology.
1.1 lane detection technology
At present, the research results of lane line detection technology mainly involve two aspects of equipment and algorithms. The data acquisition of lane line detection technology is based on different sensor devices such as laser radar, stereo vision, monocular vision and the like. For the collected information, it is necessary to match suitable algorithms, such as model-based methods and feature-based methods for calculation and decision making.
·The principle of machine vision of lidar is to identify roads by different colors or materials with different reflectivity;
· Stereo vision is more accurate than Lidar, but it is difficult to achieve image matching, equipment cost is high, and due to the complexity of the algorithm, the real-time performance is poor;
· Monocular vision is mainly implemented in the application based on features, models, fusion and machine learning. It is the most mainstream method for lane line recognition.
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