Vehicle auxiliary safe driving method

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a vehicle-assisted safe driving method, which comprises the following steps: S1, acquiring image and video data of a surrounding environment in real time through a plurality of cameras mounted around a vehicle; according to the vehicle-assisted safe driving method, through data fusion of a plurality of cameras, more comprehensive environment perception is provided, the coverage range and accuracy of the system are improved, various targets in the surrounding environment of the vehicle are identified by using an AI identification technology, the identified targets are classified, different types are distinguished, and the safety of the vehicle is improved. According to the method, the driving assistance suggestions are provided, a response strategy can be dynamically adjusted based on risk assessment of real-time environment change, so that the driving assistance suggestions better conform to the actual situation, and meanwhile, dangerous information can be quickly transmitted to a vehicle driver, surrounding vehicles and pedestrians through various modes of a display screen, sound prompt and tactile feedback and through information broadcast by the display screen outside the vehicle, so that the driving assistance suggestions are more accurate. And the overall level of traffic safety is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method for assisting safe driving of a vehicle. Background Art

[0002] Artificial intelligence is a computer science technology that aims to simulate, extend and expand human intelligence. The goal of AI is to enable computers to perform tasks that usually require human intelligence, such as learning, reasoning, perception, understanding language, recognizing patterns, etc. It has a wide range of applications. For example, AI technology is used to achieve autonomous navigation and driving of vehicles, AI is used for diagnosis, treatment recommendations, drug discovery and personalized medicine, voice assistants, intelligent control systems, etc.

[0003] However, existing vehicle-assisted safe driving methods may not be accurate enough in target recognition, and the recognition speed may not be fast enough, resulting in untimely response. Moreover, existing technologies may be limited to providing warning information within the vehicle, but cannot convey danger information to external vehicles and pedestrians. In addition, existing technologies may only provide a single feedback method and lack multi-dimensional feedback, which affects the efficiency of the driver in receiving information in different situations. Therefore, a vehicle-assisted safe driving method is proposed to solve the above problems. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a method for vehicle assisted safe driving, which utilizes AI recognition technology to identify targets faster and more accurately, reduce the probability of misjudgment and missed judgment, and provide information to the driver in multiple ways such as display screens, sound prompts and tactile feedback, thereby improving the efficiency of information transmission and the driver's perception ability. It solves the problem that the existing method for vehicle assisted safe driving may not be accurate enough in target recognition, the recognition speed may not be fast enough, resulting in untimely response, and the existing technology may be limited to providing warning information in the vehicle, but cannot transmit danger information to external vehicles and pedestrians. In addition, the existing technology may only provide a single feedback method and lacks multi-dimensional feedback, which affects the efficiency of the driver in receiving information in different situations.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a method for assisting safe driving of a vehicle, comprising the following steps:

[0008] S1, collects image and video data of the surrounding environment in real time through multiple cameras installed around the vehicle;

[0009] S2. Use artificial intelligence recognition technology to analyze the collected data, identify various targets in the surrounding environment, classify the identified targets, and distinguish different types;

[0010] S3. Based on the relevant information of different types of targets, assess the risks that the above targets may pose to vehicle driving;

[0011] S4. Develop corresponding response strategies based on risk assessment results, and provide real-time safe driving advice to the owner, surrounding vehicles and pedestrians through display screens inside and outside the vehicle, sound prompts and tactile feedback;

[0012] S5. The system continuously monitors the surrounding environment, adjusts the response strategy in real time according to environmental changes, and updates the information on the display screen.

[0013] Preferably, the specific steps of using artificial intelligence recognition technology to analyze and identify the collected data in S2 are as follows:

[0014] S2.1, Image preprocessing: Get the original image data from the vehicle camera, adjust the brightness and contrast of the image to improve the image quality, and use filters to remove noise in the image;

[0015] S2.2, Object Detection: Use a region proposal network to generate candidate regions that may contain objects, and use a convolutional neural network to extract high-dimensional features from the image;

[0016] S2.3, Target classification: Input the extracted features into the classifier, determine which type of target each candidate region belongs to, and output the probability of each target category to ensure the accuracy of the recognition result;

[0017] S2.4, Target localization: Adjust the bounding box of the candidate region to more accurately surround the target, remove overlapping prediction boxes, and retain the most likely target location

[0018] S2.5, Feature fusion: Integrate the perspective information from different cameras to perform multi-perspective target recognition and positioning;

[0019] S2.6. Identification result output: output the identified target information to the vehicle's control system or display system.

[0020] Preferably, the targets in S2 include pedestrians, vehicles, animals, obstacles and traffic signs.

[0021] Preferably, the relevant information in S3 includes the type, size, shape, distance, speed, acceleration and direction of the target.

[0022] Preferably, the specific steps of evaluating the risk that the target may pose to vehicle driving in S3 are as follows:

[0023] S3.1. Collect real-time data about the target from sensors on the vehicle and use historical driving data, accident records, and traffic flow analysis to understand the target's common behavior patterns;

[0024] S3.2. Extract features that may affect driving risk from the data collected in S1, establish a risk scoring system based on the above features, evaluate the immediate risk of the current target to vehicle driving based on the model output, and evaluate the risks that may occur in the future based on the trajectory prediction of the target;

[0025] S3.3. Divide and determine risk levels.

[0026] Preferably, the risk levels of S3.3 include low risk, medium risk and high risk.

[0027] Preferably, the low risk is that the vehicle driving is almost not affected and the vehicle can continue to drive normally, the medium risk is that the driver or the system needs to take certain preventive measures, and the high risk is that emergency measures need to be taken immediately.

[0028] Preferably, the tactile feedback in S4 is steering wheel vibration.

[0029] (III) Beneficial effects

[0030] Compared with the prior art, the present invention provides a method for assisting safe driving of a vehicle, which has the following beneficial effects:

[0031] This vehicle-assisted safe driving method provides more comprehensive environmental perception through data fusion of multiple cameras, improves the coverage and accuracy of the system, uses AI recognition technology to identify various targets in the vehicle's surrounding environment, classifies the identified targets, distinguishes different types, and dynamically adjusts the response strategy based on risk assessment of real-time environmental changes, so that driving assistance suggestions are more in line with actual conditions and improve driving safety. At the same time, through multiple methods such as display screens, sound prompts and tactile feedback, information is broadcast on the external display screen, which can quickly convey danger information to surrounding vehicles and pedestrians, enhance the overall level of traffic safety, and provide information to the driver, improve the efficiency of information transmission and the driver's perception ability, so as to achieve the purpose of safe driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The present invention is a flow chart of a method for assisting safe driving of a vehicle. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] See also Figure 1 , a method for assisting safe driving of a vehicle, comprising the following steps:

[0035] S1, collects image and video data of the surrounding environment in real time through multiple cameras installed around the vehicle;

[0036] S2. Use artificial intelligence recognition technology to analyze the collected data, identify various targets in the surrounding environment, classify the identified targets, and distinguish different types;

[0037] S3. Based on the relevant information of different types of targets, assess the risks that the above targets may pose to vehicle driving;

[0038] S4. Develop corresponding response strategies based on the risk assessment results, and provide real-time safe driving advice to the owner, surrounding vehicles and pedestrians through display screens inside and outside the vehicle, sound prompts and tactile feedback. The tactile feedback is steering wheel vibration, and the identified targets and recommended driving responses are displayed on the display screen, such as using icons or text to indicate that there are pedestrians or obstacles ahead;

[0039] S5. The system continuously monitors the surrounding environment, adjusts the response strategy in real time according to environmental changes, and updates the information on the display screen.

[0040] Specifically, the specific steps of using artificial intelligence recognition technology to analyze and identify the collected data in S2 are as follows:

[0041] S2.1, Image preprocessing: Get the original image data from the vehicle camera, adjust the brightness and contrast of the image to improve the image quality, and use filters to remove noise in the image;

[0042] S2.2, Object Detection: Use a region proposal network to generate candidate regions that may contain objects, and use a convolutional neural network to extract high-dimensional features from the image;

[0043] S2.3, Target classification: Input the extracted features into the classifier, determine which type of target each candidate region belongs to, and output the probability of each target category to ensure the accuracy of the recognition result;

[0044] S2.4, Target localization: Adjust the bounding box of the candidate region to more accurately surround the target, remove overlapping prediction boxes, and retain the most likely target location

[0045] S2.5, Feature fusion: Integrate the perspective information from different cameras to perform multi-perspective target recognition and positioning;

[0046] S2.6. Identification result output: output the identified target information to the vehicle's control system or display system.

[0047] Specifically, the targets in S2 include pedestrians, vehicles, animals, obstacles, and traffic signs.

[0048] Specifically, the relevant information in S3 includes the type, size, shape, distance, speed, acceleration and direction of the target.

[0049] Specifically, the specific steps of evaluating the risk that the target may pose to vehicle driving in S3 are as follows:

[0050] S3.1. Collect real-time data about the target from sensors on the vehicle and use historical driving data, accident records, and traffic flow analysis to understand the target's common behavior patterns;

[0051] S3.2. Extract features that may affect driving risk from the data collected in S1, such as distance, speed, acceleration, direction, etc., and establish a risk scoring system based on the above features. According to the model output, evaluate the immediate risk of the current target to vehicle driving, and based on the trajectory prediction of the target, evaluate the risks that may occur in the future;

[0052] S3.3. Divide and determine risk levels.

[0053] Specifically, the risk levels of S3.3 include low risk, medium risk and high risk.

[0054] Specifically, low risk means that there is almost no impact on vehicle driving and the vehicle can continue to drive normally. Medium risk means that the driver or the system needs to take certain preventive measures. High risk means that emergency measures need to be taken immediately.

[0055] Furthermore, the specific steps for establishing a risk scoring system based on the above characteristics are as follows:

[0056] 1) Collect data containing target features (such as distance, speed, acceleration, direction, etc.) and corresponding risk labels, handle missing values, outliers and data inconsistency, and divide the dataset into a training set.

[0057] Validation set and test set;

[0058] 2) Select the features most relevant to risk assessment, reduce dimensions, improve model efficiency, and standardize or normalize numerical features to ensure that different features have a fair impact on the model;

[0059] 3) According to the characteristics of the problem and the amount of data, use random forests to improve prediction accuracy and robustness through ensemble learning, use the training set to train the model, adjust the hyperparameters, and use the validation set for model selection and hyperparameter tuning;

[0060] 4) Perform model evaluation, select appropriate evaluation indicators such as precision, recall, F1 score, AUC-ROC, etc., and use cross-validation to evaluate the generalization ability of the model;

[0061] 5) Save the trained model as a file for subsequent loading, and integrate the model into the vehicle's assisted safety driving system to ensure real-time response.

[0062] Example 1: Identifying a road collapse ahead

[0063] S1: collects images and video data of the road ahead in real time through a camera installed in front of the vehicle;

[0064] S2: Use artificial intelligence recognition technology to analyze the collected data and identify abnormal conditions on the road ahead, such as road collapse;

[0065] S3: The risk posed by the road collapse to vehicle driving is assessed as high risk, including the possibility that the road is blocked and the vehicle cannot continue to move forward;

[0066] S4: Inside the vehicle:

[0067] The text prompt "Road collapse ahead, please stop immediately" is displayed on the in-car display screen;

[0068] Issue a voice warning: "Attention! The road ahead has collapsed, please stop immediately.";

[0069] The steering wheel vibrates to prompt the driver to make an emergency stop.

[0070] Outside the vehicle:

[0071] The text and icon "Danger ahead, road collapse!" are displayed on the display screen outside the vehicle body to alert surrounding vehicles and pedestrians;

[0072] S5: The system continuously monitors the road conditions ahead. If the landslide situation changes (such as the landslide area expands or the road is reopened to traffic), the information on the display screen is updated in real time.

[0073] Example 2: Recognizing a child crossing the road ahead

[0074] S1: collects images and video data of the road ahead in real time through a camera installed in front of the vehicle;

[0075] S2: Use artificial intelligence recognition technology to analyze the collected data and identify the child crossing the road ahead;

[0076] S3: The risk posed by children crossing the road to vehicle driving is assessed as high, including the need to slow down or stop to give way;

[0077] S4: Inside the vehicle:

[0078] A text prompt "There is a child crossing the road ahead, please slow down or stop" is displayed on the in-car display screen;

[0079] Issue an audio warning: "Attention! There is a child crossing the road ahead, please slow down or stop.";

[0080] The steering wheel vibrates slightly to remind the driver to pay attention to the situation ahead.

[0081] Outside the vehicle:

[0082] The text and icon "Children crossing the road, please give way!" are displayed on the display screen outside the vehicle body to alert surrounding vehicles;

[0083] S5: The system continuously monitors the road conditions ahead. Once the child has crossed the road, the information on the display screen is updated to prompt the vehicle to resume normal driving.

[0084] In summary, the method for assisting safe driving of a vehicle provides more comprehensive environmental perception through data fusion of multiple cameras, improves the coverage and accuracy of the system, uses AI recognition technology to identify various targets in the environment around the vehicle, classifies the identified targets, distinguishes different types, and dynamically adjusts the response strategy based on risk assessment of real-time environmental changes, so that the driving assistance suggestions are more in line with the actual situation and improve driving safety. At the same time, through multiple methods such as display screens, sound prompts and tactile feedback, information is broadcast through the external display screen, which can quickly convey danger information to surrounding vehicles and pedestrians, enhance the overall level of traffic safety, and provide information to the driver, improve the efficiency of information transmission and the driver's perception ability, so as to achieve the purpose of safe driving, and solve the problem that the existing method for assisting safe driving of a vehicle may not be accurate enough in target recognition, and the recognition speed may not be fast enough, resulting in untimely response, and the existing technology may be limited to providing warning information in the car, but cannot convey danger information to external vehicles and pedestrians. In addition, the existing technology may only provide a single feedback method, lack of multi-dimensional feedback, and affect the efficiency of the driver in receiving information in different situations.

[0085] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0086] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for assisting safe driving of a vehicle, characterized in that: The following steps are involved: S1, collects image and video data of the surrounding environment in real time through multiple cameras installed around the vehicle; S2. Use artificial intelligence recognition technology to analyze the collected data, identify various targets in the surrounding environment, classify the identified targets, and distinguish different types; S3. Based on the relevant information of different types of targets, assess the risks that the above targets may pose to vehicle driving; S4. Develop corresponding response strategies based on risk assessment results, and provide real-time safe driving advice to the owner, surrounding vehicles and pedestrians through display screens inside and outside the vehicle, sound prompts and tactile feedback; S5. The system continuously monitors the surrounding environment, adjusts the response strategy in real time according to environmental changes, and updates the information on the display screen.

2. A vehicle assisted safe driving method according to claim 1, characterized in that: The specific steps of using artificial intelligence recognition technology to analyze and identify the collected data in S2 are as follows: S2.1, Image preprocessing: Get the original image data from the vehicle camera, adjust the brightness and contrast of the image to improve the image quality, and use filters to remove noise in the image; S2.2, Object Detection: Use a region proposal network to generate candidate regions that may contain objects, and use a convolutional neural network to extract high-dimensional features from the image; S2.3, Target classification: Input the extracted features into the classifier, determine which type of target each candidate region belongs to, and output the probability of each target category to ensure the accuracy of the recognition result; S2.4, Target localization: Adjust the bounding box of the candidate region to more accurately surround the target, remove overlapping prediction boxes, and retain the most likely target location S2.5, Feature fusion: Integrate the perspective information from different cameras to perform multi-perspective target recognition and positioning; S2.

6. Identification result output: output the identified target information to the vehicle's control system or display system.

3. A vehicle assisted safe driving method according to claim 1, characterized in that: The objects in S2 include pedestrians, vehicles, animals, obstacles and traffic signs.

4. The method for assisting safe driving of a vehicle according to claim 1, characterized in that: The relevant information in S3 includes the type, size, shape, distance, speed, acceleration and direction of the target.

5. The method for assisting safe driving of a vehicle according to claim 1, characterized in that: The specific steps of evaluating the risk that the target may pose to vehicle driving in S3 are as follows: S3.

1. Collect real-time data about the target from sensors on the vehicle and use historical driving data, accident records, and traffic flow analysis to understand the target's common behavior patterns; S3.

2. Extract features that may affect driving risk from the data collected in S1, establish a risk scoring system based on the above features, evaluate the immediate risk of the current target to vehicle driving based on the model output, and evaluate the risks that may occur in the future based on the trajectory prediction of the target; S3.

3. Divide and determine risk levels.

6. A vehicle assisted safe driving method according to claim 5, characterized in that: The risk levels of S3.3 include low risk, medium risk and high risk.

7. A vehicle assisted safe driving method according to claim 6, characterized in that: The low risk target has almost no impact on vehicle driving and can continue to drive normally. The medium risk requires the driver or the system to take certain preventive measures. The high risk requires immediate emergency measures.

8. The method for assisting safe driving of a vehicle according to claim 5, characterized in that: The haptic feedback in the S4 is steering wheel vibration.

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