In order to enhance road traffic safety, numerous domestic and international research institutions and automotive companies have invested significant resources into the development of vehicle safety systems. Over time, the focus has shifted from basic mechanical and electronic devices to more advanced technologies, particularly Advanced Driver Assistance Systems (ADAS). These systems integrate various sensors—such as ultrasonic sensors, vision sensors, radar, and GPS—to monitor both the vehicle’s internal state and its external environment. By analyzing this data, ADAS can identify traffic scenarios, predict potential incidents, and provide timely driving recommendations or emergency responses to help prevent accidents and reduce their impact.
In real-world driving conditions, most of the information that drivers rely on comes from visual input. Studies show that approximately 90% of environmental information is obtained through vision, making visual sensors a crucial component in achieving vehicle intelligence. Vehicle assistance systems that use visual navigation for tasks like traffic sign detection, road recognition, pedestrian identification, and obstacle detection can significantly reduce driver workload, improve safety, and minimize accidents.
Machine vision plays a key role in providing decision-making support to drivers. It offers several advantages: it captures rich information such as object distances, shapes, textures, and colors; it is non-invasive, does not damage roads or require infrastructure changes; it can simultaneously perform multiple tasks like road inspection and obstacle detection; and it allows for data collection without interference between vehicles.
In summary, intelligent machine vision technology holds great promise in smart transportation, vehicle safety assistance, and autonomous driving.
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1. Application of Machine Vision in Advanced Assisted Driving Systems
Currently, visual sensors and machine vision technologies are widely used in various advanced driver assistance systems. One of the core components of these systems is environmental perception, which relies heavily on visual technology to gather data about road conditions, surrounding objects, and the driver's status.
Road information includes static elements such as lane markings, road edges, traffic signs, and signals. Traffic information refers to dynamic elements like other vehicles, pedestrians, and obstacles. Meanwhile, the driver's status involves in-vehicle data, such as fatigue levels or abnormal behavior, which helps prevent accidents caused by human error.
By using machine vision, these systems can collect both static and dynamic data, enabling more accurate and informed decision-making. Key technologies in modern ADAS include lane line detection, traffic sign recognition, vehicle detection, pedestrian detection, and driver state monitoring.
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1.1 Lane Line Detection Technology
Lane line detection technology currently involves two main areas: sensor equipment and algorithm development. Different types of sensors, including laser radar, stereo vision, and monocular vision, are used to collect image data, which is then processed using algorithms like model-based or feature-based methods.
Laser radar identifies roads based on differences in reflectivity, while stereo vision provides higher accuracy but is more complex and expensive. Monocular vision, often based on features, models, or machine learning, is the most commonly used method today.
Feature-based algorithms extract edge information and use predefined rules to detect lane lines. For example, Lee proposed a method using global gradient angle cumulative differentiation, which is robust against noise. Lopez later introduced "ridge" detection, which is more effective than traditional edge detection.
Model-based approaches use mathematical modeling to describe lane lines. Zhou et al. developed a method combining Gabor filters and geometric models, allowing for accurate lane line estimation even under varying conditions.
In practice, different models are chosen based on specific road conditions, with simple linear models being common for lane departure warnings and more complex curves for tracking applications.
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1.2 Traffic Sign Recognition Technology
Traffic sign recognition helps drivers make informed decisions by identifying signs in the environment. These signs often have distinct visual features like color and shape, which are used to detect them. However, factors like lighting, weather, and occlusion can affect recognition accuracy.
Most current methods involve color threshold segmentation to isolate regions of interest. Converting images to HSV or HIS color spaces improves performance by reducing the impact of lighting variations. The University of Massachusetts developed a system with 99.2% accuracy, though it lacks real-time capabilities.
Ciresan’s deep learning approach in 2011 outperformed human recognition rates, while Greenhalghd et al. focused on real-time performance using MSER and SVM. Kim JB introduced a saliency model for faster detection, showing the ongoing evolution of this field.
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1.3 Vehicle Identification Technology
Vehicle identification often uses multi-sensor fusion due to the complexity of detecting vehicles in dynamic environments. Radar is effective for detecting position and speed, while vision systems provide depth information. However, stereo vision faces challenges with real-time performance and calibration issues.
Monocular vision is popular for its real-time capabilities and includes methods like prior knowledge-based detection, motion-based detection, and statistical learning. Prior knowledge methods use features like symmetry and color, while motion-based methods rely on optical flow to detect moving objects.
Statistical learning involves training classifiers like neural networks and SVMs using diverse samples. These techniques are continuously refined to improve accuracy and adaptability in real-world conditions.
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1.4 Pedestrian Detection Technology
Pedestrian detection is unique because people are both rigid and flexible objects. Their behavior, clothing, and posture make detection challenging. Common methods include template matching, shape-based detection, and machine learning-based approaches.
HOG, Haar, and LBP are widely used for feature extraction, while SVM and boosting methods are popular for classification. HOG combined with SVM achieves near-perfect detection in controlled environments, demonstrating the effectiveness of these techniques.
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1.5 Driver Status Detection Technology
Early driver state detection relied on vehicle behavior, such as lane departure or steering wheel movement, but these methods lacked sensitivity to driver-specific traits. Modern systems use facial feature detection, such as eye movements and head posture, to assess fatigue.
FaceLAB uses multi-feature fusion to detect driver fatigue in real-time, even under poor lighting. The EU project “AWAKE†integrates multiple sensors to analyze driver behavior comprehensively. Nissan’s system uses sound, light, and seat vibrations to alert drivers when fatigue is detected.
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2. Conclusion
The automotive industry has entered an era of intelligent technology, with machine vision playing a central role in driving assistance systems. As machine vision advances, it will continue to drive improvements in vehicle safety and autonomy. Future developments will focus on enhancing image quality, optimizing processing algorithms, and improving real-time performance. With continued innovation in sensors and algorithms, machine vision will become even more accurate and efficient in supporting safe and intelligent driving.
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