# Key Technologies of Vehicle Active Safety System Based on Computer Vision

Authors: Mingzhu Qian; Xiaobao Wang
Journal: Journal of Advanced Transportation
 (2022)

---

## Abstract

Due to the increase in the number of urban vehicles and the irregular driving behavior of drivers, urban accidents frequently occur, causing serious casualties and economic losses. Active vehicle safety systems can monitor vehicle status and driver status online in real time. Computer vision technology simulates biological vision and can analyze, identify, detect, and track the data and information in the captured images. In terms of driving accident warning and vehicle status warning, the vehicle active safety system has the potential to enhance the driver’s ability to detect abnormal situations, prolong the processing time, and reduce the risk of safety accidents. In this paper, an active safety system is developed according to the existing vehicle electronic system framework, and the early warning decision is made by evaluating the relationship between the minimum early warning distance and the actual vehicle distance, speed, and other factors. In this paper, the kinematics model established by the vehicle active safety early warning system is designed. The results found that, within 400 ms of the driver’s judgment time, for the driver with the reaction time of 0.6 s and 0.9 s, the following distance of 20 m does not constitute a safety threat and no braking operation is required.


---

## Body

## 1. Introduction

Computer vision has been in development for nearly 60 years. As an application system, computer vision gradually evolves with the development of enterprise automation. Today, the global vision market is worth approximately $7-8 billion and is growing at an annual rate of 8.8%. With the development of China’s manufacturing industry, the demand for computer vision in China is also growing. With the rapid growth of vehicle ownership and the increase in traffic accidents in recent years, active vehicle safety systems have received increasing attention. The traffic control department’s analysis of the causes of accidents shows that the main causes of road accidents are still speeding, drunk driving, and fatigue management, and the driver’s subconscious speed in predicting and responding to emergencies is relatively slow. It is more vulnerable in emergency situations. Analysis of a large number of road accidents also shows that more than 80% of vehicles are caused by slow drivers, and more than 65% of road accidents are crashes. The active safety system of the vehicle can continuously monitor the environment and driving conditions in front of the vehicle, provide early warning of possible obstacles in normal driving, and perform emergency braking and automation of the vehicle in emergency situations to ensure the safety of the site and the vehicle.With the improvement of people’s living standards, vehicle safety has attracted increasing attention from the public, and it has also become the competitive advantage of various autogiants. One of the best tools for solving vehicle safety problems is the vehicle’s active safety system. Active vehicle safety is different from passive vehicle safety. It is a safety warning system that can prevent accidents before a collision, remind drivers to take preventive measures in time, make up for the driver’s slow response, and ensure safety. Vehicle safety technology research is the main research direction for future intelligent transportation and intelligent vehicle driving. Visual vehicle safety technology is more valuable in terms of economy and technology.According to the different running and braking conditions of the vehicle ahead, the vehicle monitoring model is classified and analyzed by using the principles of kinematics and dynamics. It examines the influence and adaptability of system parameters from three aspects: human-vehicle-environment. Trajectories are fused feature maps using directional saliency features and color saliency features to identify lanes. It analyzes the difficulties faced by lane detection and weights the inherent color and direction features of lane lines in the detection process to highlight their saliency in the image segmentation process.


## 2. Related Work

To improve road safety, Chang proposed a drowsiness fatigue detection system based on wearable smart glasses. The system consists of a pair of wearable smart glasses, an in-vehicle infotainment telematics platform, an onboard diagnostic II-based vehicle diagnostic bridge, an active vehicle taillight warning mechanism, and a cloud-based management platform. He also proposed and implemented a dedicated miniature bandpass infrared (IR) light sensor for low-cost, lightweight, wearable smart glasses. It can provide a higher signal-to-noise ratio than common commercial infrared light sensors, minimize ambient light images, and effectively improve detection accuracy. The system can detect the drowsiness or fatigue of the vehicle driver in real time. When drowsiness or fatigue is detected, the active vehicle light warning mechanism will automatically flash to warn the following vehicles. The relevant information will also be transferred to the cloud-based management platform at the same time. Therefore, the proposed system can improve road safety, but it has not been widely used [1]. It is important for automotive engineers to understand the interplay between human active maneuvering motion and vehicle dynamics and how vehicle controls affect the human driver’s physical sensations. Kimpara proposed a new system framework, Human Model-Based Active Driving System (HuMADS), for simulating human-vehicle interaction. HuMADS integrates vehicle controllers with vehicle dynamics and human biomechanical models. It has a layered closed-loop architecture of the driver-vehicle control system, including the structure and contact interfaces of the human body and the body. Based on the OpenSim simulation platform, HuMADS developed a system to adjust the dynamics of the human body model so that the human body model can respond realistically to the vehicle’s maneuvering motion. The usability of HuMADS is demonstrated by simulating coordinated accelerator/brake pedal operation and wheel steering in a highway driving task. The simulated vehicle dynamics and vehicle handling are comparable to previously published experimental data on car following. However, at present, it is more difficult to realize all aspects [2]. It is of great significance to obtain the accurate position of the vehicle in the intelligent transportation system for improving active safety and realizing autonomous driving. Aiming at the shortcomings of current global positioning system (GPS) and vehicle-to-infrastructure (V2I) positioning technologies, Cao proposed a new positioning system that combines radio frequency identification (RFID), vision, and ultrawideband (UWB) communication. The purpose is to achieve lane-level positioning in urban environments where GPS effects are poor. He analyzed the error of positioning of a single RSU V2 in a typical situation. RSU is a roadside unit, which is a device installed on the roadside to realize vehicle identification in the ETC system. The results show that a system with a reasonable arrangement of RSUs can achieve positioning accuracy with an error less than 0. However, the initial capital investment needs to be larger [3]. Autonomous driving is an active area of research in industry and academia. Automatic parking is the automatic driving in the restricted scene of low-speed control parking, and it is the key supporting product of the fully automatic driving system. Heimberger discussed the design and implementation of automated parking systems from the perspective of computer vision algorithms. Designing a low-cost system with functional safety is a challenge that leads to a large gap between the prototype and the final product to handle all corner cases. It demonstrates that camera systems are critical for addressing a range of automated parking use cases while also adding robustness to systems based on active ranging sensors such as ultrasonic and radar. The key vision modules that enable parking use cases are 3D reconstruction, parking space, marker recognition, free space, and vehicle/pedestrian detection. He presented important parking use cases and demonstrated them, but the practicality was not strong [4]. To ensure the safety of high-speed trains, Zhao studied a rollover index estimation method based on a preestimated driver model and designed a vehicle rollover prediction system based on a preestimated driver model. For vehicles with a high center of gravity, he developed a dynamic rollover model with three degrees of freedom and a driver model. He designed a rollover prediction system and simulated and analyzed rollovers under different operating conditions to verify the effectiveness of the system, but the research is not comprehensive [5]. Alvarez mainly studies Lane Keeping Assist System (LKAS) for 4WD electric vehicles. He proposed a new method of Lane Keeping Assist. This approach imposes an additional yaw moment on EVs to achieve lane keeping by actively distributing the 4WD/braking torque. Lane Keeping Assist is divided into three layers. At the upper level, it makes auxiliary control decisions and calculates the desired yaw rate by taking into account lane departure, vehicle dynamics, and road grip constraints. In the middle layer, he designed a sliding mode controller (SMC) to control the additional yaw moment. On the lower level, the yaw moment is created by distributing the drive/braking torque among the four wheels. Lane Keeping Assist works by tracking the desired yaw response. LKAS is evaluated via CarSim/Simulink. The simulation results of the single lane change test show that this method can make the vehicle have good dynamic stability and can limit the vehicle in the lane to avoid the occurrence of lane departure accidents, but the practicability is not strong [6]. Javaid addresses a key issue in automotive safety. With the increasing amount of road traffic, advanced driver assistance systems (ADAS) have gained a large reputation. He introduced the design of an automatic emergency overtaking (AEP) strategy for semiautonomous vehicles using active safety systems on a driving simulator. The idea of the system is that a moving vehicle equipped with the AEP system can pull over automatically to the safety shoulder of the road when the driver is deemed unable to drive. He expounded the application design, algorithm development, and component structure of the AEP system. He has observed all main variables affecting vehicle performance after AEP activation and remodeled it according to the control algorithm, but the research method is not clear enough [7]. These studies provide a detailed analysis of the application of computer vision techniques and autonomous vehicle driving. It is undeniable that these studies have greatly promoted the development of the corresponding fields. We can learn a lot from methodology and data analysis. However, there are relatively few studies on vehicle active safety systems in the field of computer technology, and it is necessary to fully apply these algorithms to the research in this field.


## 3. Method of Vehicle Active Safety System Based on Computer Vision

### 3.1. Computer Vision Technology

Vision is an important way for people to find information and understand the world. Humans receive, process, and understand visual information through their eyes and brain. The light emitted by the substances in the environment is projected onto the retina through the eyes, and after being reintroduced into the retina and retinal neurons by photoreceptor cells, it is then transmitted to the cerebral cortex through nerves for processing and interpretation [8]. Due to the irreplaceability of vision and its large proportion in human production and life and with the rapid development of science and technology, the field of computer vision has attracted much attention and achieved considerable research results. Computer vision can be achieved by simulating human vision to establish a related model of vision and using a computer system to realize the bionic method of the related model. The advent of computers has made great progress in human science and technology and created many new disciplines. People use cameras or video cameras to capture images of objects in the environment and convert them into digital signals and use computers to process the information to create a computer vision system [9].Computer vision, also known as machine vision, is a science and technology that uses computers to simulate biological appearance and macroscopic visual functions. The main purpose of computer vision analysis is to use plane images to create or reconstruct real-world models, that is, to study how to use two-dimensional images to reconstruct the three-dimensional visual world and then identify the world [10]. The theoretical methods used in computer vision are mainly computer vision theory based on geometry, probability, 3D motion, and reconstruction calculations. The biggest feature of computer vision technology is fast speed, large amount of information, and multiple functions.Computer vision analysis mainly includes three aspects: (1) Measure the distance from the target object to one or more two-dimensional images. (2) Calculate the motion model of the target object based on one or more two-dimensional projection images. (3) Calculate the physical properties of the target surface based on one or more two-dimensional projection images [11]. The ultimate goal of computer vision is to understand a three-dimensional view of the world, the analogous function of the human visual system. The essence of the study is to recreate the visible part of a three-dimensional object using a two-dimensional projected image. Computer vision is still mainly in the stage of image information expression and object recognition.When analyzing vision systems, distance sensors are often used to fuse information, which can directly obtain depth information and solve many visual problems. At present, computer vision systems can be roughly divided into four categories according to their applications: monitoring systems, recognition systems, detection systems, and guidance systems, as shown in Figure1 [12].Figure 1
Classification of computer vision systems.


### 3.2. Safety Early Warning System for People, Vehicles, and Environment

Vehicles running on the road, drivers, vehicles, and road traffic environment together constitute a typical road traffic system of human-vehicle-road trinity. The safe driving of the vehicle is jointly restricted by the factors of the road, the vehicle, and the driver [13]. Although the driver is the key factor in the traffic system and plays a leading role in driving safety, there is a large error in the driver’s visual judgment of the distance between the front and rear vehicles, and the response ability is far less accurate than the system. Therefore, the vehicle active safety anticollision warning system is a good assistant to ensure the driver’s safe driving and always escort the safe driving of the vehicle.Vehicle active safety systems include a wide range of applications; the vehicle itself has an antilock braking system (ABS), electronic driving steering system (MDPS), emergency brake warning system (ESS), and emergency brake assist (EBA). The function of the antilock braking system is to automatically control the braking force of the brakes when the car is braking so that the wheels are not locked and are in a state of rolling and slipping (slip rate is about 20%). It ensures that the adhesion between the wheel and the ground is at the maximum value [14]. The braking system has the following four basic components: (1) Energy supply device: it includes various components that supply and adjust the energy required for braking and improve the energy transfer medium. The part that generates braking energy is called braking energy. (2) Control device: it includes various components that generate braking action and control the braking effect. The brake pedal mechanism is the simplest type of control device. (3) Transmission device: it includes various components that transmit braking energy to the brake, such as the brake master cylinder (master cylinder) and the brake wheel cylinder (wheel cylinder). (4) Brake: it is a component that generates a force (braking force) that hinders the movement or movement of the vehicle and includes retarding devices in the auxiliary braking system. The more complete braking system now also includes braking force adjustment devices and additional devices such as alarm devices and pressure protection devices. Lane Departure Warning Systems (LDWS), Brake Warning Systems, exterior vehicle cameras, and other applications are part of a vehicle’s active safety system to ensure safe driving. More than 90% of road traffic accidents are caused by drivers not paying attention. This is why the vehicle collision warning system is the most important of the vehicle’s active safety systems. It consists of two parts: passive safety and active safety [15].


#### 3.2.1. Passive Safety of Automobiles

The passive safety system of automobiles focuses on the protection of the occupants of the vehicle after an accident, for example, airbags, seat belts, explosion-proof windows, collapsible steering columns, and characteristically reinforced body [16].


#### 3.2.2. Vehicle Active Safety

Active vehicle safety is a unique system designed to prevent dangerous accidents while driving, significantly reducing the number of accidents by proactively alerting car occupants, or taking steps to protect occupants before an accident occurs, such as ABS, EBD, HMWS, ASR, SAS, ACC, TCS, ESP, and VSA.


### 3.3. Lane Detection Technology

Active safety systems keep passengers safe while driving before an accident occurs. Features of car active safety system are as follows: improving the driving stability of the car and trying to prevent car accidents. Lanes describe a series of segments of a vehicle traveled on, and different routes are divided into different lanes. Lane detection technology is mainly used to predict vehicle distance and is suitable for structured routes with clear lines. In practice, the position of the vehicle relative to the head of the vehicle is monitored in real time to obtain the position of the vehicle in the lane and to assess whether the vehicle is moving out of the lane [17]. Drivers are instantly alerted to eliminate abnormal driving, prevent accidents, and promote safe driving. Lane detection relies heavily on machine vision technology.In recent years, the research of vehicle active safety anticollision system is mainly divided into three major parts, Europe, Japan, and China, and has also achieved remarkable achievements. A comprehensive analysis of each system shows that the sensors selected are mainly sonar, ultrasonic, infrared, Lidar, millimeter-wave radar, microwave radar, machine vision, and so on. Its main function is to perform image recognition, distance measurement, speed measurement, and calculation of safe vehicle distance. Its main working principle and its advantages and disadvantages are compared in Table1.Table 1
Comparison of various sensor performance.
Parameter indicatorsDistance detection (meters)Price Level (yuan)False alarm rateDarkness penetrating abilityResolution (mm)Machine visionOver 100 meters1000Relatively lowStrongDepending on image processing powerUltrasound10A few thousand yuanComparatively highRelatively low3Infrared15050Comparatively highRelatively low10Radar20080LowRelatively low1∼10Lane detection in structured roads is mainly based on the distinctive color features of marking lines for identification, and secondly, vanishing point detection is also performed using the direction of road texture to determine the direction of lane change. Recognition by color has strong real-time performance and can quickly separate the position of the marking line. However, the robustness is weak, and the texture-based method has strong adaptability to a series of disturbances in the image. However, to achieve a better detection effect, it needs to use the original image to convolve the multidirectional templates, respectively, which requires a large amount of computation. If the orientation template and scale are reduced, an effective primary segmentation result cannot be obtained [1].To address these issues, this paper proposes a detection method that uses a combination of color saliency and directional features. It uses two identifiable characteristics, the color and direction of the signal line. It marks the color feature and combines the direction features to divide the position of the signal line, then subtracts the feature points of the marked series, and changes the candidates to obtain the lane area [18], as shown in Figure 2.Figure 2
Lane line fitting algorithm flow.


#### 3.3.1. Gabor Transform

When the window function is assumed to be a Gaussian function, the Gabor transform is a short-time Fourier transform, which is a special case of the short-time Fourier transform. Because the Gabor function processes the results according to the human visual perception system, it can extract relevant features of different scales and directions in the frequency domain. This is a special case of the short-time Fourier transform [19]. Gabor is used to enhance the subtle features of orientation and scale, and its response format highlights the same locally important features as the desired orientation, allowing the creation of a local feature map characterized by luminance images. In recent years, Gabor filtering has been widely used in face and fingerprint recognition. The Gabor kernel function can extract relevant features at different scales and directions. The two-dimensional Gabor function can generally be expressed as(1)gαwx,y=l2σ2exp−l2x2+y22σ2⋅expilxy−exp−σ22,where(2)l=lxly=lwcosϕαlwsinϕα,lw=2w+2/2π,ϕα=πL⋅α,where w represents the scale of different wavelengths, α represents the direction of the kernel function, L represents the total number of all selected directions, and the parameter σ/l determines the size of the Gaussian window.It can also be expressed in the following form:(3)Gx,y=gx,yexp−2πid0x−x0+ey−y0.Among them,gx,y=exp−2πid0x−x0+ey−y0 and x0,y0 represent the position of the filter in space, d0 represents the short axis length of the Gaussian envelope, e represents the long axis length of the Gaussian envelope, w=d02+e2 represents the center frequency of the filter, and β=arctane/d0 represents the direction of the filter.According to the convolution theorem, if you want to calculate the convolution of two functions in a space field, you can first perform a Fourier transform on them. Then, it multiplies the Fourier transform results of the two functions and finally performs the Fourier transform on the multiplied result. The convolution in the spatial domain is equivalent to the multiplication of the Fourier transform in the frequency domain, and vice versa. Gabor transform is a special case of short-time Fourier transform. When the window function is a Gaussian function, it can also be used as a convolution model in image processing.For direction and scale, the expression of the Gabor filter function is as follows:(4)ϕw,αx,y=w2πkexp−w24a2+b28k2expiaw−exp−k22,where(5)a=xcosα+ysinα,b=−xcsinα+ycosα,k=2.2.Different scales and directions can obtain different results in the convolution process. Large-scale templates can highlight boundaries with larger areas and ignore boundaries with smaller areas. Small-scale templates, on the other hand, mainly highlight detailed textures. Templates in different directions correspond to larger texture response values in the same direction in the image, while texture responses in different directions are smaller.


#### 3.3.2. Feature Extraction of Lane Line Direction

The Gabor kernel function is used to transform the texture features to maximize the texture orientation features. In the process of path detection, detour detection of texture is required, so more directional patterns should be added. This brings a larger computational problem, but due to the principle of perspective, the number lines obtained in the path representation always intersect at a certain point. According to this pair of features, the two number lines that are actually parallel show the intersection ofπ/3 and 2π/3. The kernel function of the Gabor filter for direction α and scale w is given in formula (6).(6)ϕw,αx,y=w2πkexp−w24a2+b28k2expiaw−exp−k22.For the marked line, choose the Gabor kernel function of two scales convolved in the two directions ofπ/3 and 2π/3, and let ux,y be the value of point x,y in the figure. u is the convolution of the Gabor kernel in the α direction and scale w, as defined by formula (7).(7)Gw,α=u⊗ϕw,α.(8)Gw,α:w∈1,2,α∈1,2.The convolution result of pointf=x,y is divided into two parts, a real part and an imaginary part. To incorporate directionality more clearly, the value of the response is taken as the sum of the squares of the real and imaginary parts, as shown in formula (9).(9)uw,αf=ReGw,αf2+ImGw,αf2.The response value in a certain directionα is defined as the sum of the transformation results of different scales w in this direction. To obtain the two scales uniformly, the average value of the results of different scales is taken, as shown in formula (10).(10)hαf=Averageuw,αf.


#### 3.3.3. Saliency Map Generation with Specific Color Weighting

The smaller receptive field in the central part of the human retina is more sensitive to light intensity, color, and other pieces of information, while the peripheral part of the retina is relatively insensitive. The sampling density and visual resolution of the human eye decrease with increasing distance from the central part of the retina, where information around the retina is highly compressed [20]. In contrast, when looking at something, the object that first accumulates in the central receptive area of the retina is called the apparent target. In an image, prominent objects must be shot close up to get more information, while other parts are not regions of interest, and their information can be compressed. This is the mechanism of visual attention, which is a selection process-choosing salient objects because our vision is limited [21].Suppose an image containsn colors; first calculate the color histogram of the image, because usually an image contains a limited number of objects. Therefore, the colors contained in it are only a small fraction of the total color space used by the algorithm. Therefore, to reduce the number of computations and the influence of noise color on the result, the algorithm will remove the color that occupies a small part of the color in the image, that is, remove the possible color noise [22]. Finally, it chooses the primary color of the image to replace the color of less frequent pixels near the histogram. After the replacement is complete, suppose there are n colors, denoted as k1,k2,…,km,km+1,…,kn−1,kn,…,kp, where km,kn are denoted by white and yellow. Then according to the known properties of the white and yellow label lines, the expected meaning around the two color values C follows a normal distribution, and the meaning values after color enhancement are as follows:(11)Rσul=Rσki=∑j=1nwkjAjBki,kj,where(12)Aj=12πσej−m2/2σ2+12πσej−n2/2σ2.Among them,wkj is the position weight of color kj. In order to enhance the influence of the color of closer pixels, wkj is set as the pixel distance between color kj and the actual calculated ul.


#### 3.3.4. Saliency Map Fusion

Let C be a color-weighted saliency map andT be a directional saliency map. The color-weighted saliency map and the directional saliency map must be combined to further highlight the marker lines [23]. First, the color-weighted saliency map and the directional saliency map are normalized according to formula (13).(13)NC=C−minCmaxC−minC,where maxC,minC represent the maximum and minimum values in the saliency map, respectively.On this basis, the total saliency mapCTotal is obtained by merging the two.(14)CTotal=NC×NT.By binarizing the salient images, a segmented image can be obtained. Since the center of the image obtained with the vehicle camera must be located in the middle of the two marked lines, the search starts from the centerline of the image and selects 1/3 of the close range of the saliency map as the region of interest. And it separates out the two feature points of the lane markings defined byxi,yii=0,1,2,…n [24].Assuming that the parametric formula corresponding to featurey=Hx is y=kx+b, the error corresponding to the target line of any feature point xi,yi is ςi=Hxi−yi. The sum of squares of errors corresponding to all feature points is(15)hk,b=∑i=1nς2=min∑i=1nkxi+b−yi2.The function that makes formula (15) the minimum value is the required straight line parameter as shown in formula (16) and formula (17). The result of the lane line fitting is shown in Figure 3.(16)k=∑xiyi∑xi∑yin∑xi2∑xi∑xin,(17)b=∑xi2∑xiyi∑xi∑xi∑xi2∑xi∑xin.Figure 3
Algorithm detection results.
(a)(b)


### 3.4. Image Recognition of Obstacles Such as People and Cars

Image recognition refers to the technology that uses computers to process, analyze, and understand images to identify targets and objects in various patterns. And it performs a series of enhancement and reconstruction techniques on the images with poor quality to effectively improve the image quality. Image recognition technology is to let the computer simulate the human brain and have a certain memory function for the images stored by the computer. Image recognition techniques may be based on the main features of the image. Every image has its characteristics. In the process of image recognition, the perceptual mechanism must exclude the redundant information of the input and extract the key information. The purpose of image processing and detection is the final image recognition. And image recognition is the focus of vehicle active safety system research. It is a prerequisite for vehicle safety warning and collision avoidance. Whether it can automatically identify obstacles such as vehicles and pedestrians in the image determines the function and quality of the system. In the image recognition process of the system, it is necessary to distinguish the vehicles and pedestrians ahead and distinguish them according to their respective shapes, contour features such as shapes and points of interest, pattern recognition method, template matching model, and image recognition method based on human and vehicle characteristics.Image pattern recognition system mainly includes three categories: low-level processing, intermediate processing, and high-level processing. It mainly acquires images through a CCD sensor. In the process of image preprocessing, processing, image segmentation, and description and expression, it continuously trains the classifier through image recognition, compares it with the memory model in the knowledge base, and finally achieves the process of recognition. Its structural composition is shown in Figure4.Figure 4
Image pattern recognition system composition.Image pattern recognition is divided into supervised recognition and unsupervised recognition. The main difference between the two is whether the category to which each experimental sample belongs is known in advance. It uses prior knowledge and training skills to create a classifier, which is supervised, while recognition that uses the similarity of recognition vectors to automatically classify is unsupervised. Figure5 shows the image recognition trainer created by the supervised recognition method.Figure 5
Image recognition training classifier.In the knowledge base of the image recognition training classifier, the image contour information of obstacles such as people and vehicles in front of the vehicle in front is placed. The new image information is compared with the original template image information, whether it is from the features such as surface contour, size, and width or according to the image information of obstacles and their respective points of interest. It performs fuzzy reasoning according to the similarity of the images and finally judges the membership degree of each image.


## 3.1. Computer Vision Technology

Vision is an important way for people to find information and understand the world. Humans receive, process, and understand visual information through their eyes and brain. The light emitted by the substances in the environment is projected onto the retina through the eyes, and after being reintroduced into the retina and retinal neurons by photoreceptor cells, it is then transmitted to the cerebral cortex through nerves for processing and interpretation [8]. Due to the irreplaceability of vision and its large proportion in human production and life and with the rapid development of science and technology, the field of computer vision has attracted much attention and achieved considerable research results. Computer vision can be achieved by simulating human vision to establish a related model of vision and using a computer system to realize the bionic method of the related model. The advent of computers has made great progress in human science and technology and created many new disciplines. People use cameras or video cameras to capture images of objects in the environment and convert them into digital signals and use computers to process the information to create a computer vision system [9].Computer vision, also known as machine vision, is a science and technology that uses computers to simulate biological appearance and macroscopic visual functions. The main purpose of computer vision analysis is to use plane images to create or reconstruct real-world models, that is, to study how to use two-dimensional images to reconstruct the three-dimensional visual world and then identify the world [10]. The theoretical methods used in computer vision are mainly computer vision theory based on geometry, probability, 3D motion, and reconstruction calculations. The biggest feature of computer vision technology is fast speed, large amount of information, and multiple functions.Computer vision analysis mainly includes three aspects: (1) Measure the distance from the target object to one or more two-dimensional images. (2) Calculate the motion model of the target object based on one or more two-dimensional projection images. (3) Calculate the physical properties of the target surface based on one or more two-dimensional projection images [11]. The ultimate goal of computer vision is to understand a three-dimensional view of the world, the analogous function of the human visual system. The essence of the study is to recreate the visible part of a three-dimensional object using a two-dimensional projected image. Computer vision is still mainly in the stage of image information expression and object recognition.When analyzing vision systems, distance sensors are often used to fuse information, which can directly obtain depth information and solve many visual problems. At present, computer vision systems can be roughly divided into four categories according to their applications: monitoring systems, recognition systems, detection systems, and guidance systems, as shown in Figure1 [12].Figure 1
Classification of computer vision systems.


## 3.2. Safety Early Warning System for People, Vehicles, and Environment

Vehicles running on the road, drivers, vehicles, and road traffic environment together constitute a typical road traffic system of human-vehicle-road trinity. The safe driving of the vehicle is jointly restricted by the factors of the road, the vehicle, and the driver [13]. Although the driver is the key factor in the traffic system and plays a leading role in driving safety, there is a large error in the driver’s visual judgment of the distance between the front and rear vehicles, and the response ability is far less accurate than the system. Therefore, the vehicle active safety anticollision warning system is a good assistant to ensure the driver’s safe driving and always escort the safe driving of the vehicle.Vehicle active safety systems include a wide range of applications; the vehicle itself has an antilock braking system (ABS), electronic driving steering system (MDPS), emergency brake warning system (ESS), and emergency brake assist (EBA). The function of the antilock braking system is to automatically control the braking force of the brakes when the car is braking so that the wheels are not locked and are in a state of rolling and slipping (slip rate is about 20%). It ensures that the adhesion between the wheel and the ground is at the maximum value [14]. The braking system has the following four basic components: (1) Energy supply device: it includes various components that supply and adjust the energy required for braking and improve the energy transfer medium. The part that generates braking energy is called braking energy. (2) Control device: it includes various components that generate braking action and control the braking effect. The brake pedal mechanism is the simplest type of control device. (3) Transmission device: it includes various components that transmit braking energy to the brake, such as the brake master cylinder (master cylinder) and the brake wheel cylinder (wheel cylinder). (4) Brake: it is a component that generates a force (braking force) that hinders the movement or movement of the vehicle and includes retarding devices in the auxiliary braking system. The more complete braking system now also includes braking force adjustment devices and additional devices such as alarm devices and pressure protection devices. Lane Departure Warning Systems (LDWS), Brake Warning Systems, exterior vehicle cameras, and other applications are part of a vehicle’s active safety system to ensure safe driving. More than 90% of road traffic accidents are caused by drivers not paying attention. This is why the vehicle collision warning system is the most important of the vehicle’s active safety systems. It consists of two parts: passive safety and active safety [15].


### 3.2.1. Passive Safety of Automobiles

The passive safety system of automobiles focuses on the protection of the occupants of the vehicle after an accident, for example, airbags, seat belts, explosion-proof windows, collapsible steering columns, and characteristically reinforced body [16].


### 3.2.2. Vehicle Active Safety

Active vehicle safety is a unique system designed to prevent dangerous accidents while driving, significantly reducing the number of accidents by proactively alerting car occupants, or taking steps to protect occupants before an accident occurs, such as ABS, EBD, HMWS, ASR, SAS, ACC, TCS, ESP, and VSA.


## 3.2.1. Passive Safety of Automobiles

The passive safety system of automobiles focuses on the protection of the occupants of the vehicle after an accident, for example, airbags, seat belts, explosion-proof windows, collapsible steering columns, and characteristically reinforced body [16].


## 3.2.2. Vehicle Active Safety

Active vehicle safety is a unique system designed to prevent dangerous accidents while driving, significantly reducing the number of accidents by proactively alerting car occupants, or taking steps to protect occupants before an accident occurs, such as ABS, EBD, HMWS, ASR, SAS, ACC, TCS, ESP, and VSA.


## 3.3. Lane Detection Technology

Active safety systems keep passengers safe while driving before an accident occurs. Features of car active safety system are as follows: improving the driving stability of the car and trying to prevent car accidents. Lanes describe a series of segments of a vehicle traveled on, and different routes are divided into different lanes. Lane detection technology is mainly used to predict vehicle distance and is suitable for structured routes with clear lines. In practice, the position of the vehicle relative to the head of the vehicle is monitored in real time to obtain the position of the vehicle in the lane and to assess whether the vehicle is moving out of the lane [17]. Drivers are instantly alerted to eliminate abnormal driving, prevent accidents, and promote safe driving. Lane detection relies heavily on machine vision technology.In recent years, the research of vehicle active safety anticollision system is mainly divided into three major parts, Europe, Japan, and China, and has also achieved remarkable achievements. A comprehensive analysis of each system shows that the sensors selected are mainly sonar, ultrasonic, infrared, Lidar, millimeter-wave radar, microwave radar, machine vision, and so on. Its main function is to perform image recognition, distance measurement, speed measurement, and calculation of safe vehicle distance. Its main working principle and its advantages and disadvantages are compared in Table1.Table 1
Comparison of various sensor performance.
Parameter indicatorsDistance detection (meters)Price Level (yuan)False alarm rateDarkness penetrating abilityResolution (mm)Machine visionOver 100 meters1000Relatively lowStrongDepending on image processing powerUltrasound10A few thousand yuanComparatively highRelatively low3Infrared15050Comparatively highRelatively low10Radar20080LowRelatively low1∼10Lane detection in structured roads is mainly based on the distinctive color features of marking lines for identification, and secondly, vanishing point detection is also performed using the direction of road texture to determine the direction of lane change. Recognition by color has strong real-time performance and can quickly separate the position of the marking line. However, the robustness is weak, and the texture-based method has strong adaptability to a series of disturbances in the image. However, to achieve a better detection effect, it needs to use the original image to convolve the multidirectional templates, respectively, which requires a large amount of computation. If the orientation template and scale are reduced, an effective primary segmentation result cannot be obtained [1].To address these issues, this paper proposes a detection method that uses a combination of color saliency and directional features. It uses two identifiable characteristics, the color and direction of the signal line. It marks the color feature and combines the direction features to divide the position of the signal line, then subtracts the feature points of the marked series, and changes the candidates to obtain the lane area [18], as shown in Figure 2.Figure 2
Lane line fitting algorithm flow.


### 3.3.1. Gabor Transform

When the window function is assumed to be a Gaussian function, the Gabor transform is a short-time Fourier transform, which is a special case of the short-time Fourier transform. Because the Gabor function processes the results according to the human visual perception system, it can extract relevant features of different scales and directions in the frequency domain. This is a special case of the short-time Fourier transform [19]. Gabor is used to enhance the subtle features of orientation and scale, and its response format highlights the same locally important features as the desired orientation, allowing the creation of a local feature map characterized by luminance images. In recent years, Gabor filtering has been widely used in face and fingerprint recognition. The Gabor kernel function can extract relevant features at different scales and directions. The two-dimensional Gabor function can generally be expressed as(1)gαwx,y=l2σ2exp−l2x2+y22σ2⋅expilxy−exp−σ22,where(2)l=lxly=lwcosϕαlwsinϕα,lw=2w+2/2π,ϕα=πL⋅α,where w represents the scale of different wavelengths, α represents the direction of the kernel function, L represents the total number of all selected directions, and the parameter σ/l determines the size of the Gaussian window.It can also be expressed in the following form:(3)Gx,y=gx,yexp−2πid0x−x0+ey−y0.Among them,gx,y=exp−2πid0x−x0+ey−y0 and x0,y0 represent the position of the filter in space, d0 represents the short axis length of the Gaussian envelope, e represents the long axis length of the Gaussian envelope, w=d02+e2 represents the center frequency of the filter, and β=arctane/d0 represents the direction of the filter.According to the convolution theorem, if you want to calculate the convolution of two functions in a space field, you can first perform a Fourier transform on them. Then, it multiplies the Fourier transform results of the two functions and finally performs the Fourier transform on the multiplied result. The convolution in the spatial domain is equivalent to the multiplication of the Fourier transform in the frequency domain, and vice versa. Gabor transform is a special case of short-time Fourier transform. When the window function is a Gaussian function, it can also be used as a convolution model in image processing.For direction and scale, the expression of the Gabor filter function is as follows:(4)ϕw,αx,y=w2πkexp−w24a2+b28k2expiaw−exp−k22,where(5)a=xcosα+ysinα,b=−xcsinα+ycosα,k=2.2.Different scales and directions can obtain different results in the convolution process. Large-scale templates can highlight boundaries with larger areas and ignore boundaries with smaller areas. Small-scale templates, on the other hand, mainly highlight detailed textures. Templates in different directions correspond to larger texture response values in the same direction in the image, while texture responses in different directions are smaller.


### 3.3.2. Feature Extraction of Lane Line Direction

The Gabor kernel function is used to transform the texture features to maximize the texture orientation features. In the process of path detection, detour detection of texture is required, so more directional patterns should be added. This brings a larger computational problem, but due to the principle of perspective, the number lines obtained in the path representation always intersect at a certain point. According to this pair of features, the two number lines that are actually parallel show the intersection ofπ/3 and 2π/3. The kernel function of the Gabor filter for direction α and scale w is given in formula (6).(6)ϕw,αx,y=w2πkexp−w24a2+b28k2expiaw−exp−k22.For the marked line, choose the Gabor kernel function of two scales convolved in the two directions ofπ/3 and 2π/3, and let ux,y be the value of point x,y in the figure. u is the convolution of the Gabor kernel in the α direction and scale w, as defined by formula (7).(7)Gw,α=u⊗ϕw,α.(8)Gw,α:w∈1,2,α∈1,2.The convolution result of pointf=x,y is divided into two parts, a real part and an imaginary part. To incorporate directionality more clearly, the value of the response is taken as the sum of the squares of the real and imaginary parts, as shown in formula (9).(9)uw,αf=ReGw,αf2+ImGw,αf2.The response value in a certain directionα is defined as the sum of the transformation results of different scales w in this direction. To obtain the two scales uniformly, the average value of the results of different scales is taken, as shown in formula (10).(10)hαf=Averageuw,αf.


### 3.3.3. Saliency Map Generation with Specific Color Weighting

The smaller receptive field in the central part of the human retina is more sensitive to light intensity, color, and other pieces of information, while the peripheral part of the retina is relatively insensitive. The sampling density and visual resolution of the human eye decrease with increasing distance from the central part of the retina, where information around the retina is highly compressed [20]. In contrast, when looking at something, the object that first accumulates in the central receptive area of the retina is called the apparent target. In an image, prominent objects must be shot close up to get more information, while other parts are not regions of interest, and their information can be compressed. This is the mechanism of visual attention, which is a selection process-choosing salient objects because our vision is limited [21].Suppose an image containsn colors; first calculate the color histogram of the image, because usually an image contains a limited number of objects. Therefore, the colors contained in it are only a small fraction of the total color space used by the algorithm. Therefore, to reduce the number of computations and the influence of noise color on the result, the algorithm will remove the color that occupies a small part of the color in the image, that is, remove the possible color noise [22]. Finally, it chooses the primary color of the image to replace the color of less frequent pixels near the histogram. After the replacement is complete, suppose there are n colors, denoted as k1,k2,…,km,km+1,…,kn−1,kn,…,kp, where km,kn are denoted by white and yellow. Then according to the known properties of the white and yellow label lines, the expected meaning around the two color values C follows a normal distribution, and the meaning values after color enhancement are as follows:(11)Rσul=Rσki=∑j=1nwkjAjBki,kj,where(12)Aj=12πσej−m2/2σ2+12πσej−n2/2σ2.Among them,wkj is the position weight of color kj. In order to enhance the influence of the color of closer pixels, wkj is set as the pixel distance between color kj and the actual calculated ul.


### 3.3.4. Saliency Map Fusion

Let C be a color-weighted saliency map andT be a directional saliency map. The color-weighted saliency map and the directional saliency map must be combined to further highlight the marker lines [23]. First, the color-weighted saliency map and the directional saliency map are normalized according to formula (13).(13)NC=C−minCmaxC−minC,where maxC,minC represent the maximum and minimum values in the saliency map, respectively.On this basis, the total saliency mapCTotal is obtained by merging the two.(14)CTotal=NC×NT.By binarizing the salient images, a segmented image can be obtained. Since the center of the image obtained with the vehicle camera must be located in the middle of the two marked lines, the search starts from the centerline of the image and selects 1/3 of the close range of the saliency map as the region of interest. And it separates out the two feature points of the lane markings defined byxi,yii=0,1,2,…n [24].Assuming that the parametric formula corresponding to featurey=Hx is y=kx+b, the error corresponding to the target line of any feature point xi,yi is ςi=Hxi−yi. The sum of squares of errors corresponding to all feature points is(15)hk,b=∑i=1nς2=min∑i=1nkxi+b−yi2.The function that makes formula (15) the minimum value is the required straight line parameter as shown in formula (16) and formula (17). The result of the lane line fitting is shown in Figure 3.(16)k=∑xiyi∑xi∑yin∑xi2∑xi∑xin,(17)b=∑xi2∑xiyi∑xi∑xi∑xi2∑xi∑xin.Figure 3
Algorithm detection results.
(a)(b)


## 3.3.1. Gabor Transform

When the window function is assumed to be a Gaussian function, the Gabor transform is a short-time Fourier transform, which is a special case of the short-time Fourier transform. Because the Gabor function processes the results according to the human visual perception system, it can extract relevant features of different scales and directions in the frequency domain. This is a special case of the short-time Fourier transform [19]. Gabor is used to enhance the subtle features of orientation and scale, and its response format highlights the same locally important features as the desired orientation, allowing the creation of a local feature map characterized by luminance images. In recent years, Gabor filtering has been widely used in face and fingerprint recognition. The Gabor kernel function can extract relevant features at different scales and directions. The two-dimensional Gabor function can generally be expressed as(1)gαwx,y=l2σ2exp−l2x2+y22σ2⋅expilxy−exp−σ22,where(2)l=lxly=lwcosϕαlwsinϕα,lw=2w+2/2π,ϕα=πL⋅α,where w represents the scale of different wavelengths, α represents the direction of the kernel function, L represents the total number of all selected directions, and the parameter σ/l determines the size of the Gaussian window.It can also be expressed in the following form:(3)Gx,y=gx,yexp−2πid0x−x0+ey−y0.Among them,gx,y=exp−2πid0x−x0+ey−y0 and x0,y0 represent the position of the filter in space, d0 represents the short axis length of the Gaussian envelope, e represents the long axis length of the Gaussian envelope, w=d02+e2 represents the center frequency of the filter, and β=arctane/d0 represents the direction of the filter.According to the convolution theorem, if you want to calculate the convolution of two functions in a space field, you can first perform a Fourier transform on them. Then, it multiplies the Fourier transform results of the two functions and finally performs the Fourier transform on the multiplied result. The convolution in the spatial domain is equivalent to the multiplication of the Fourier transform in the frequency domain, and vice versa. Gabor transform is a special case of short-time Fourier transform. When the window function is a Gaussian function, it can also be used as a convolution model in image processing.For direction and scale, the expression of the Gabor filter function is as follows:(4)ϕw,αx,y=w2πkexp−w24a2+b28k2expiaw−exp−k22,where(5)a=xcosα+ysinα,b=−xcsinα+ycosα,k=2.2.Different scales and directions can obtain different results in the convolution process. Large-scale templates can highlight boundaries with larger areas and ignore boundaries with smaller areas. Small-scale templates, on the other hand, mainly highlight detailed textures. Templates in different directions correspond to larger texture response values in the same direction in the image, while texture responses in different directions are smaller.


## 3.3.2. Feature Extraction of Lane Line Direction

The Gabor kernel function is used to transform the texture features to maximize the texture orientation features. In the process of path detection, detour detection of texture is required, so more directional patterns should be added. This brings a larger computational problem, but due to the principle of perspective, the number lines obtained in the path representation always intersect at a certain point. According to this pair of features, the two number lines that are actually parallel show the intersection ofπ/3 and 2π/3. The kernel function of the Gabor filter for direction α and scale w is given in formula (6).(6)ϕw,αx,y=w2πkexp−w24a2+b28k2expiaw−exp−k22.For the marked line, choose the Gabor kernel function of two scales convolved in the two directions ofπ/3 and 2π/3, and let ux,y be the value of point x,y in the figure. u is the convolution of the Gabor kernel in the α direction and scale w, as defined by formula (7).(7)Gw,α=u⊗ϕw,α.(8)Gw,α:w∈1,2,α∈1,2.The convolution result of pointf=x,y is divided into two parts, a real part and an imaginary part. To incorporate directionality more clearly, the value of the response is taken as the sum of the squares of the real and imaginary parts, as shown in formula (9).(9)uw,αf=ReGw,αf2+ImGw,αf2.The response value in a certain directionα is defined as the sum of the transformation results of different scales w in this direction. To obtain the two scales uniformly, the average value of the results of different scales is taken, as shown in formula (10).(10)hαf=Averageuw,αf.


## 3.3.3. Saliency Map Generation with Specific Color Weighting

The smaller receptive field in the central part of the human retina is more sensitive to light intensity, color, and other pieces of information, while the peripheral part of the retina is relatively insensitive. The sampling density and visual resolution of the human eye decrease with increasing distance from the central part of the retina, where information around the retina is highly compressed [20]. In contrast, when looking at something, the object that first accumulates in the central receptive area of the retina is called the apparent target. In an image, prominent objects must be shot close up to get more information, while other parts are not regions of interest, and their information can be compressed. This is the mechanism of visual attention, which is a selection process-choosing salient objects because our vision is limited [21].Suppose an image containsn colors; first calculate the color histogram of the image, because usually an image contains a limited number of objects. Therefore, the colors contained in it are only a small fraction of the total color space used by the algorithm. Therefore, to reduce the number of computations and the influence of noise color on the result, the algorithm will remove the color that occupies a small part of the color in the image, that is, remove the possible color noise [22]. Finally, it chooses the primary color of the image to replace the color of less frequent pixels near the histogram. After the replacement is complete, suppose there are n colors, denoted as k1,k2,…,km,km+1,…,kn−1,kn,…,kp, where km,kn are denoted by white and yellow. Then according to the known properties of the white and yellow label lines, the expected meaning around the two color values C follows a normal distribution, and the meaning values after color enhancement are as follows:(11)Rσul=Rσki=∑j=1nwkjAjBki,kj,where(12)Aj=12πσej−m2/2σ2+12πσej−n2/2σ2.Among them,wkj is the position weight of color kj. In order to enhance the influence of the color of closer pixels, wkj is set as the pixel distance between color kj and the actual calculated ul.


## 3.3.4. Saliency Map Fusion

Let C be a color-weighted saliency map andT be a directional saliency map. The color-weighted saliency map and the directional saliency map must be combined to further highlight the marker lines [23]. First, the color-weighted saliency map and the directional saliency map are normalized according to formula (13).(13)NC=C−minCmaxC−minC,where maxC,minC represent the maximum and minimum values in the saliency map, respectively.On this basis, the total saliency mapCTotal is obtained by merging the two.(14)CTotal=NC×NT.By binarizing the salient images, a segmented image can be obtained. Since the center of the image obtained with the vehicle camera must be located in the middle of the two marked lines, the search starts from the centerline of the image and selects 1/3 of the close range of the saliency map as the region of interest. And it separates out the two feature points of the lane markings defined byxi,yii=0,1,2,…n [24].Assuming that the parametric formula corresponding to featurey=Hx is y=kx+b, the error corresponding to the target line of any feature point xi,yi is ςi=Hxi−yi. The sum of squares of errors corresponding to all feature points is(15)hk,b=∑i=1nς2=min∑i=1nkxi+b−yi2.The function that makes formula (15) the minimum value is the required straight line parameter as shown in formula (16) and formula (17). The result of the lane line fitting is shown in Figure 3.(16)k=∑xiyi∑xi∑yin∑xi2∑xi∑xin,(17)b=∑xi2∑xiyi∑xi∑xi∑xi2∑xi∑xin.Figure 3
Algorithm detection results.
(a)(b)


## 3.4. Image Recognition of Obstacles Such as People and Cars

Image recognition refers to the technology that uses computers to process, analyze, and understand images to identify targets and objects in various patterns. And it performs a series of enhancement and reconstruction techniques on the images with poor quality to effectively improve the image quality. Image recognition technology is to let the computer simulate the human brain and have a certain memory function for the images stored by the computer. Image recognition techniques may be based on the main features of the image. Every image has its characteristics. In the process of image recognition, the perceptual mechanism must exclude the redundant information of the input and extract the key information. The purpose of image processing and detection is the final image recognition. And image recognition is the focus of vehicle active safety system research. It is a prerequisite for vehicle safety warning and collision avoidance. Whether it can automatically identify obstacles such as vehicles and pedestrians in the image determines the function and quality of the system. In the image recognition process of the system, it is necessary to distinguish the vehicles and pedestrians ahead and distinguish them according to their respective shapes, contour features such as shapes and points of interest, pattern recognition method, template matching model, and image recognition method based on human and vehicle characteristics.Image pattern recognition system mainly includes three categories: low-level processing, intermediate processing, and high-level processing. It mainly acquires images through a CCD sensor. In the process of image preprocessing, processing, image segmentation, and description and expression, it continuously trains the classifier through image recognition, compares it with the memory model in the knowledge base, and finally achieves the process of recognition. Its structural composition is shown in Figure4.Figure 4
Image pattern recognition system composition.Image pattern recognition is divided into supervised recognition and unsupervised recognition. The main difference between the two is whether the category to which each experimental sample belongs is known in advance. It uses prior knowledge and training skills to create a classifier, which is supervised, while recognition that uses the similarity of recognition vectors to automatically classify is unsupervised. Figure5 shows the image recognition trainer created by the supervised recognition method.Figure 5
Image recognition training classifier.In the knowledge base of the image recognition training classifier, the image contour information of obstacles such as people and vehicles in front of the vehicle in front is placed. The new image information is compared with the original template image information, whether it is from the features such as surface contour, size, and width or according to the image information of obstacles and their respective points of interest. It performs fuzzy reasoning according to the similarity of the images and finally judges the membership degree of each image.


## 4. Experimental Design of Vehicle Active Safety System

In the kinematic model established by the vehicle active safety warning system, the vehicle safety warning distance and the automatic braking distance need to be determined separately according to different conditions and working conditions. There are many relevant factors, and it should be comprehensively considered to obtain data that is consistent with the specific actual process, and then it has an application value. The main parameters to be determined are the speed of the two vehicles, the deceleration, the driver’s reaction time, the brake coordination time and the brake deceleration increase time, and the safe distance between the final stop and the safe driving distance. These elements are related to the early warning of the vehicle, whether the braking is timely, and whether the vehicle can achieve the function of anticollision. The early warning model considering road conditions and driver conditions is shown in Figure6.Figure 6
Early warning decision system.


### 4.1. Speed of Front and Rear Vehicles

The problem of vehicle speed plays a vital role in vehicle active safety anticollision system. The occurrence of vehicle traffic accidents, serious consequences, and heavy losses are all due to the high speed. The speed of the front and rear vehicles needs to be input into the active safety warning system for calculation. The speed of the self-vehicle is captured by the speed sensor installed near the wheel. The speed of the car in front is obtained through the speed measurement of dual CCD stereo cameras, but the speed is the relative speed of the two cars, namely,va=v1−v2, so as to obtain the speed of the car in front. The relative speed of the front and rear vehicles is also a direct response to the danger level of the vehicle, so it is essential to control the relative speed between vehicles.


### 4.2. Driver Reaction Time, Brake Coordination Time, and Braking Deceleration Increase Time

Under normal circumstances, the driver’s reaction time varies greatly due to differences in individual age, gender, mood, and so on, and the degree of difference in reaction time between individuals is also large.


## 4.1. Speed of Front and Rear Vehicles

The problem of vehicle speed plays a vital role in vehicle active safety anticollision system. The occurrence of vehicle traffic accidents, serious consequences, and heavy losses are all due to the high speed. The speed of the front and rear vehicles needs to be input into the active safety warning system for calculation. The speed of the self-vehicle is captured by the speed sensor installed near the wheel. The speed of the car in front is obtained through the speed measurement of dual CCD stereo cameras, but the speed is the relative speed of the two cars, namely,va=v1−v2, so as to obtain the speed of the car in front. The relative speed of the front and rear vehicles is also a direct response to the danger level of the vehicle, so it is essential to control the relative speed between vehicles.


## 4.2. Driver Reaction Time, Brake Coordination Time, and Braking Deceleration Increase Time

Under normal circumstances, the driver’s reaction time varies greatly due to differences in individual age, gender, mood, and so on, and the degree of difference in reaction time between individuals is also large.


## 5. Data Analysis of Vehicle Active Safety System

### 5.1. Influence of the Driver’s Reaction Time on the Early Warning Decision

The response speed of drivers of different ages related to the driver himself is different, as shown in Figure7. The driver’s reaction time decreased with age.Figure 7
Changes in driver reaction time with age.It can be seen from Figure7 that the driver’s judgment time is within 400 ms, and the driver’s reaction time is almost the same for drivers of different genders according to their respective physical conditions. The influence of the external environment will delay the driver’s reaction time. For example, when a driver is drunk driving, his reaction ability is greatly reduced, and traffic accidents are often the most likely to occur, which is also the reason the traffic control department strictly investigates and punishes “drinking driving.” There is also the driver’s fatigue driving, and the phenomenon of dozing off while driving is very dangerous.The driving distance increases with the increase of vehicle speed and speed difference. In general, a larger distance between cars is required to maintain the safety of each other on the highway. The greater speed difference not only hinders the traffic capacity of the road but also poses a great safety hazard. According to the different distances between vehicles, the simulation analysis results of the vehicle following models with a maximum speed of 100 Km/h and a speed difference of 45 Km/h and 60 Km/h, respectively, illustrate the conclusion, as shown in Figure8.Figure 8
Crash probability of vehicles with different speed differences at different workshop distances.According to the motion model formula, the actual distance, early warning distance, and real-time speed change curve can be obtained. Assume that the preceding vehicle 1 and the rear vehicle 2 are both at a constant speed of 18/m s, and the distance between the two vehicles is 20 m. At timet = 1 s, vehicle 1 decelerates at an acceleration of 6 m/s2. Figure 9 shows the minimum warning distance and real-time speed when the driver’s reaction time is 0.3 s, 0.6 s, and 0.9 s, respectively.Figure 9
Effect of driver response time on early warning decision.It can be seen from Figure9 that, for drivers with reaction times of 0.6 s and 0.9 s, the following distance is 20 m, which does not constitute a safety threat, and no braking operation is required. However, for a driver with a reaction time of 0.9 s, the following distance of 20 m is already a threat, requiring early warning and braking. If the same early warning decision-making method is used for different drivers, it will affect the driver’s confidence in the early warning system and make the driver lose confidence in the early warning system, resulting in the early warning system not working well. To adapt to the driving habits and physiological reaction speed of different drivers, adding the driver’s reaction time to the early warning system can improve the early warning accuracy of the early warning system.


### 5.2. Influence of Road Conditions on Early Warning Decision-Making

Assuming that the speed of the preceding vehicle 1 and the rear vehicle 2 is 20/m s and the speed is constant, and the distance between the two vehicles is 20 m; att = 1 s, the first vehicle decelerates at an acceleration of 6 m/s2. The acceleration of vehicle B is determined by the road surface conditions a1=ϕg, ϕ is the peak adhesion coefficient, and its value is 0.3, 0.7, and 1.0. The minimum warning distance and real-time speed are shown in Figure 10 and Tables 2 and 3.Figure 10
Impact of pavement conditions on early warning decisions.Table 2
Speed of vehicle 1 at different peak adhesion coefficients (Km/h).
Peak adhesion coefficient of 0.3Peak adhesion coefficient of 1.00202012020216103121480540Table 3
Speed of vehicle 2 at different peak adhesion coefficients (Km/h).
Peak adhesion coefficient of 0.3Peak adhesion coefficient of 1.0020201202022020316104120580It can be seen from Figure10 and Tables 2 and 3 that, for the road surface with peak adhesion coefficients of 0.7 and 1.0, the following distance does not pose a safety threat when the vehicle following distance is 20 m, and no braking operation is required. However, for a road with a peak adhesion coefficient of 0.3, the following distance of 20 m is already a threat, requiring early warning and braking.


## 5.1. Influence of the Driver’s Reaction Time on the Early Warning Decision

The response speed of drivers of different ages related to the driver himself is different, as shown in Figure7. The driver’s reaction time decreased with age.Figure 7
Changes in driver reaction time with age.It can be seen from Figure7 that the driver’s judgment time is within 400 ms, and the driver’s reaction time is almost the same for drivers of different genders according to their respective physical conditions. The influence of the external environment will delay the driver’s reaction time. For example, when a driver is drunk driving, his reaction ability is greatly reduced, and traffic accidents are often the most likely to occur, which is also the reason the traffic control department strictly investigates and punishes “drinking driving.” There is also the driver’s fatigue driving, and the phenomenon of dozing off while driving is very dangerous.The driving distance increases with the increase of vehicle speed and speed difference. In general, a larger distance between cars is required to maintain the safety of each other on the highway. The greater speed difference not only hinders the traffic capacity of the road but also poses a great safety hazard. According to the different distances between vehicles, the simulation analysis results of the vehicle following models with a maximum speed of 100 Km/h and a speed difference of 45 Km/h and 60 Km/h, respectively, illustrate the conclusion, as shown in Figure8.Figure 8
Crash probability of vehicles with different speed differences at different workshop distances.According to the motion model formula, the actual distance, early warning distance, and real-time speed change curve can be obtained. Assume that the preceding vehicle 1 and the rear vehicle 2 are both at a constant speed of 18/m s, and the distance between the two vehicles is 20 m. At timet = 1 s, vehicle 1 decelerates at an acceleration of 6 m/s2. Figure 9 shows the minimum warning distance and real-time speed when the driver’s reaction time is 0.3 s, 0.6 s, and 0.9 s, respectively.Figure 9
Effect of driver response time on early warning decision.It can be seen from Figure9 that, for drivers with reaction times of 0.6 s and 0.9 s, the following distance is 20 m, which does not constitute a safety threat, and no braking operation is required. However, for a driver with a reaction time of 0.9 s, the following distance of 20 m is already a threat, requiring early warning and braking. If the same early warning decision-making method is used for different drivers, it will affect the driver’s confidence in the early warning system and make the driver lose confidence in the early warning system, resulting in the early warning system not working well. To adapt to the driving habits and physiological reaction speed of different drivers, adding the driver’s reaction time to the early warning system can improve the early warning accuracy of the early warning system.


## 5.2. Influence of Road Conditions on Early Warning Decision-Making

Assuming that the speed of the preceding vehicle 1 and the rear vehicle 2 is 20/m s and the speed is constant, and the distance between the two vehicles is 20 m; att = 1 s, the first vehicle decelerates at an acceleration of 6 m/s2. The acceleration of vehicle B is determined by the road surface conditions a1=ϕg, ϕ is the peak adhesion coefficient, and its value is 0.3, 0.7, and 1.0. The minimum warning distance and real-time speed are shown in Figure 10 and Tables 2 and 3.Figure 10
Impact of pavement conditions on early warning decisions.Table 2
Speed of vehicle 1 at different peak adhesion coefficients (Km/h).
Peak adhesion coefficient of 0.3Peak adhesion coefficient of 1.00202012020216103121480540Table 3
Speed of vehicle 2 at different peak adhesion coefficients (Km/h).
Peak adhesion coefficient of 0.3Peak adhesion coefficient of 1.0020201202022020316104120580It can be seen from Figure10 and Tables 2 and 3 that, for the road surface with peak adhesion coefficients of 0.7 and 1.0, the following distance does not pose a safety threat when the vehicle following distance is 20 m, and no braking operation is required. However, for a road with a peak adhesion coefficient of 0.3, the following distance of 20 m is already a threat, requiring early warning and braking.


## 6. Conclusion

Although computer vision has received a lot of research and development, the processing power of computer vision is still very naive compared to human vision. On many occasions, computer vision is still limited by computer processing power and cannot meet the requirements of real time and precision. In today’s society, vehicles play an important role in facilitating mobility and transportation of goods, and people are increasingly relying on them. With the rapid development of social economy, the number of vehicles increases rapidly. Road traffic accidents are also becoming increasingly serious with the increase in the number of vehicles, becoming a major threat to today’s society. Therefore, driving safety is an important concern of modern society, and vehicle safety and vehicle condition monitoring are one of the important indicators to measure vehicle performance. In this paper, a simplified linear model is used to fit the detected lane lines. Although it can achieve better detection of lane lines, in some curved lanes, the detection results have a certain impact on the lane departure warning.



---

*Source: 4749086-2022-05-20.xml*