AI artificial intelligence auxiliary driving system

Abstract

The invention relates to the technical field of artificial intelligence driving, in particular to an AI artificial intelligence auxiliary driving system. The AI sensing module is provided with a camera, a laser radar, a radar and an ABS intelligent sensor and used for collecting information of the surrounding environment of a vehicle in real time and processing and analyzing the collected data through deep learning and a computer vision algorithm so as to recognize roads, vehicles, pedestrians and obstacles. Through the AI algorithm, the vehicle surrounding environment can be accurately sensed, the driving state can be monitored in real time to make a more accurate driving decision, the risk of traffic accidents is reduced, vehicle control parameters are automatically adjusted in emergency, the driving safety is remarkably improved, the driving route can be intelligently optimized, the driving burden can be relieved, and the driving safety is improved. And more comfortable and convenient driving experience is provided for the driver. And meanwhile, personalized driving settings and services can be provided according to personal requirements and preferences of drivers, so that the requirements of different drivers are met.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence driving technology, and in particular to an AI artificial intelligence assisted driving system. Background Art

[0002] With the rapid development of the automobile industry and the continuous advancement of intelligent technology, assisted driving systems have become an important part of modern automobiles. Traditional assisted driving systems mainly rely on sensors and simple control algorithms to achieve basic vehicle assistance functions, such as automatic parking, lane keeping, blind spot monitoring, etc. However, these systems can often only handle relatively simple driving scenarios, and their performance and reliability are greatly limited for complex and changing road environments and traffic conditions.

[0003] In recent years, the rapid development of artificial intelligence (AI) technology, especially the application of advanced algorithms such as deep learning and reinforcement learning, has provided unprecedented opportunities for the upgrading and intelligentization of assisted driving systems. AI technology can automatically extract useful features and information through the analysis and learning of a large amount of driving data, thereby achieving accurate perception and intelligent decision-making in complex driving environments. In addition, with the continuous development of autonomous driving technology, people have also put forward higher requirements for the safety and reliability of assisted driving systems.

[0004] For example, an intelligent assisted driving system with publication number CN115626174A includes: a data acquisition module, a control module, a lighting module, a fatigue driving module, an automatic driving module, an identification auxiliary module, and a safety warning module. The control module is connected to the data acquisition module, the lighting module, the fatigue driving module, the automatic driving module, the identification auxiliary module and the safety warning module respectively; the data acquisition module is used to collect vehicle body information, vehicle surrounding information and path information in real time; the lighting module is used to judge the vehicle surrounding information in real time for lighting; the fatigue driving module is used to detect the driver's driving status in real time; the automatic driving module is used to perform automatic driving according to the vehicle surrounding information and the path information; the identification auxiliary module is used to identify the driver's situation to drive the vehicle in real time; the safety warning module is used to give real-time safety warnings to the vehicle; and the control module is used for safety control between various modules.

[0005] Although the above-mentioned existing technologies and other typical existing technologies can realize assisted driving, they still face many problems in practical applications. There is room for improvement in ensuring the real-time and accuracy of the system, how to effectively deal with emergencies and complex traffic environments, and how to interact and collaborate well with the driver. For this reason, we propose an AI artificial intelligence assisted driving system. Summary of the invention

[0006] The purpose of the present invention is to provide an AI artificial intelligence assisted driving system to solve the problems raised in the above background technology.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] An AI artificial intelligence assisted driving system, comprising: an AI perception module: configured with cameras, lidar, radar and ABS smart sensors, for collecting information about the vehicle's surroundings in real time, and processing and analyzing the collected data through deep learning and computer vision algorithms to identify roads, vehicles, pedestrians and obstacles;

[0009] AI decision-making module: receives information from the AI ​​perception module and uses machine learning algorithms and reinforcement learning models to formulate driving strategies and plan paths, including lane keeping, obstacle avoidance, and speed adjustment;

[0010] AI control module: Based on the output of the AI ​​decision module, the model predictive control MPC or adaptive cruise control ACC is used to accurately control the acceleration, braking and steering operations of the vehicle;

[0011] Execution module: The instructions of the AI ​​control module are specifically executed through the electric motor and hydraulic system actuator to realize intelligent driving of the vehicle.

[0012] Preferably, it also includes an AI misoperation detection and intervention module, and the AI ​​misoperation detection and intervention module is used to monitor the driver's driving operation in real time.

[0013] Preferably, the AI ​​misoperation detection and intervention module analyzes the deviation between the driver's intention and the actual vehicle response through an AI algorithm. When the accelerator is mistakenly used as the brake, according to the preset acceleration abnormality detection logic, the expression is:

[0014] Anomaly_Detection=Abs(Actual_Accel)>Threshold

[0015] Among them, Actual_Accel is the actual acceleration, Predicted_Accel is the acceleration predicted based on the current environment and driver operation, and Threshold is the set acceleration abnormality threshold. If the detection value exceeds the threshold, intervention measures are immediately initiated, forced deceleration or parking, and a warning is issued to the driver.

[0016] Preferably, it also includes an AI path planning and analysis module, which includes path simulation, path evaluation and path selection sub-modules. The AI ​​path planning and analysis module uses an AI algorithm to simulate, evaluate and sort the driving path to select the optimal path.

[0017] Preferably, the AI ​​algorithm is a Q-Learning algorithm, wherein the update expression of the Q-Learning algorithm is:

[0018]

[0019] Among them, s represents the current state, a represents the current action, Reward represents the reward, and s ′ Indicates the next state, a ′ represents the next action, α is the learning rate, and γ is the discount factor.

[0020] Preferably, the AI ​​decision-making module and the AI ​​path planning and analysis module are connected to an AI navigation prompt module, which provides real-time navigation prompts and driving suggestions to the driver based on the outputs of the AI ​​decision-making module and the AI ​​path planning and analysis module, and works in conjunction with the AI ​​misoperation detection and intervention module to provide additional safety prompts and intervention measures.

[0021] Preferably, the AI ​​perception module is further configured with an abnormality identification submodule for identifying abnormal conditions in environmental information and sending an alarm signal to the decision module. The abnormality identification submodule uses the following expression for abnormality detection:

[0022]

[0023] Among them, x i represents the current value of the i-th sensor data, u i represents the average value of the i-th sensor data, w i It represents the weight of the i-th sensor data, n represents the number of sensors, and when Anomaly_Score exceeds the preset threshold, it is judged as abnormal.

[0024] Preferably, the execution module includes a redundant design, which includes sensor redundancy, computing unit redundancy and actuator redundancy. The sensor redundancy is to install backup sensors at key sensor positions to deal with sensor failures; the computing unit redundancy adopts multi-core processors and distributed computing architecture to improve the reliability and fault tolerance of the computing unit; the actuator redundancy configures backup actuators in the actuator to ensure that control instructions can still be executed normally when a single actuator fails.

[0025] Preferably, the system is also provided with a user interaction interface, which is used to display the system status, driving suggestions and safety warnings, including vehicle speed, navigation information, emergency braking prompts, driver fatigue warnings, and the user interaction interface allows the driver to configure and personalize the system as needed.

[0026] It can be seen without a doubt that the above-mentioned technical solution of the present application can definitely solve the technical problem to be solved by the present application.

[0027] At the same time, through the above technical solutions, the present invention has at least the following beneficial effects:

[0028] The present invention can accurately perceive the vehicle's surrounding environment through AI algorithms, monitor the driving status in real time to make more accurate driving decisions, reduce the risk of traffic accidents, and automatically adjust vehicle control parameters in emergency situations, significantly improving driving safety.

[0029] The present invention can realize intelligent optimization of driving routes, reduce driving burden, and provide drivers with a more comfortable and convenient driving experience. At the same time, it can also provide personalized driving settings and services according to the personal needs and preferences of drivers to meet the needs of different drivers. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0031] Figure 1 It is a structural schematic diagram of the present invention;

[0032] Figure 2 It is a structural schematic diagram of the AI ​​control module of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0034] Example 1

[0035] Reference Figure 1-2 , an AI artificial intelligence assisted driving system, including: AI perception module: equipped with cameras, lidar, radar and ABS smart sensors, used to collect information about the vehicle's surrounding environment in real time, and process and analyze the collected data through deep learning and computer vision algorithms to identify roads, vehicles, pedestrians and obstacles;

[0036] AI decision-making module: receives information from the AI ​​perception module and uses machine learning algorithms and reinforcement learning models to formulate driving strategies and plan paths, including lane keeping, obstacle avoidance, and speed adjustment;

[0037] AI control module: Based on the output of the AI ​​decision module, the model predictive control MPC or adaptive cruise control ACC is used to accurately control the acceleration, braking and steering operations of the vehicle;

[0038] Execution module: The motor and hydraulic system actuators execute the instructions of the AI ​​control module to realize the intelligent driving of the vehicle. By integrating multiple modules, it realizes comprehensive perception of the vehicle's surrounding environment, intelligent decision-making and precise control. This greatly improves driving safety and comfort and reduces the risk of human error.

[0039] It also includes an AI misoperation detection and intervention module, which is used to monitor the driver's driving operations in real time. The AI ​​misoperation detection and intervention module can monitor the driver's driving operations in real time and immediately initiate intervention measures when misoperation is detected, thereby further enhancing driving safety.

[0040] The AI ​​misoperation detection and intervention module uses AI algorithms to analyze the deviation between the driver's intention and the actual vehicle response. When the accelerator is detected as the brake, according to the preset acceleration anomaly detection logic, the expression is:

[0041] Anomaly_Detection=Abs(Actual_Accel)>Threshold

[0042] Among them, Actual_Accel is the actual acceleration, Predicted_Accel is the acceleration predicted based on the current environment and driver operation, Threshold is the set acceleration abnormality threshold. If the detection value exceeds the threshold, intervention measures are immediately initiated, forced deceleration or parking, and a warning is issued to the driver. The AI ​​misoperation detection and intervention module analyzes the deviation between the driver's intention and the actual vehicle response through the AI ​​algorithm, and can accurately identify dangerous operations such as mistaking the accelerator for the brake, and take intervention measures in time according to the preset acceleration abnormality detection logic, further enhancing the safety of the system and effectively avoiding serious consequences caused by misoperation.

[0043] It also includes an AI path planning and analysis module, which includes path simulation, path evaluation and path selection submodules. The AI ​​path planning and analysis module uses AI algorithms to simulate, evaluate and sort the driving path to select the optimal path. Through the cooperation between the path simulation, path evaluation and path selection submodules, it can predict the changes in traffic flow in the future, which helps the AI ​​path planning and analysis module to plan the driving path more accurately, avoid congested sections, and improve driving efficiency. At the same time, it also helps the AI ​​decision-making module to consider traffic conditions more when formulating driving strategies to ensure smooth and safe driving.

[0044] The AI ​​algorithm is the Q-Learning algorithm, where the update expression of the Q-Learning algorithm is:

[0045]

[0046] Among them, s represents the current state, a represents the current action, Reward represents the reward, and s ′ Indicates the next state, a ′ represents the next action, α is the learning rate, and γ is the discount factor, which enables the system to continuously learn and optimize the driving path and adapt to different traffic environments and driving needs. This improves the intelligence and adaptability of the system and provides possibilities for the further development of intelligent driving.

[0047] The AI ​​decision-making module and the AI ​​path planning and analysis module are connected to the AI ​​navigation prompt module. The AI ​​navigation prompt module provides real-time navigation prompts and driving suggestions to the driver based on the output of the AI ​​decision-making module and the AI ​​path planning and analysis module. At the same time, it works with the AI ​​misoperation detection and intervention module to provide additional safety prompts and intervention measures. The AI ​​navigation prompt module enables the system to provide real-time navigation prompts and driving suggestions to the driver. At the same time, the collaborative work with the AI ​​misoperation detection and intervention module further enhances the safety and reliability of the system and can improve the driver's driving experience and satisfaction.

[0048] The AI ​​perception module is also equipped with an anomaly recognition submodule, which is used to identify anomalies in environmental information and send alarm signals to the decision module. The anomaly recognition submodule uses the following expression for anomaly detection:

[0049]

[0050] Among them, x i represents the current value of the i-th sensor data, u i represents the average value of the i-th sensor data, w i represents the weight of the i-th sensor data, n represents the number of sensors, and when Anomaly_Score exceeds the preset threshold, it is judged as abnormal. The abnormal identification submodule enables the system to identify abnormal situations in environmental information and send alarm signals to the decision-making module. This helps the system respond to potential dangerous situations in a timely manner and improves driving safety and reliability.

[0051] Example 2

[0052] Embodiment 1 is further optimized, specifically, as Figure 1As shown in the figure, the execution module includes redundant design, including sensor redundancy, computing unit redundancy and actuator redundancy. Sensor redundancy is to install backup sensors at key sensor locations to deal with sensor failures; computing unit redundancy uses multi-core processors and distributed computing architecture to improve the reliability and fault tolerance of computing units; actuator redundancy configures backup actuators in the actuator to ensure that control instructions can still be executed normally when a single actuator fails. Sensor redundancy, computing unit redundancy and actuator redundancy enable the system to still work normally when facing sensor failures, computing unit failures or actuator failures. This greatly improves the reliability and fault tolerance of the system and provides a strong guarantee for the widespread application of intelligent driving.

[0053] Example 3

[0054] Embodiment 1 is further optimized, specifically, as Figure 1 As shown, the system is also provided with a user interaction interface, which is used to display the system status, driving suggestions and safety warnings, including vehicle speed, navigation information, emergency braking prompts, and driver fatigue warnings. The user interaction interface allows the driver to configure and personalize the system as needed. The setting of the user interaction interface enables the driver to intuitively understand the system status, driving suggestions, safety warnings and other information. At the same time, allowing the driver to configure and personalize the system as needed improves the flexibility of the system and the user experience, which helps to improve the driver's trust and acceptance of the intelligent driving system.

[0055] From the above, we can know that:

[0056] The present invention is directed to the following technical problems: Although the existing technologies and other typical existing technologies can realize assisted driving, they still face many problems in practical applications. There is room for improvement in ensuring the real-time and accuracy of the system, how to effectively deal with emergencies and complex traffic environments, and how to interact and collaborate well with the driver. By adopting the technical solutions of the above embodiments and through the above settings, the present application can certainly solve the above technical problems and achieve the following technical effects:

[0057] The present invention can accurately perceive the vehicle's surrounding environment through AI algorithms, monitor the driving status in real time to make more accurate driving decisions, reduce the risk of traffic accidents, and automatically adjust vehicle control parameters in emergency situations, significantly improving driving safety.

[0058] The present invention can realize intelligent optimization of driving routes, reduce driving burden, and provide drivers with a more comfortable and convenient driving experience. At the same time, it can also provide personalized driving settings and services according to the personal needs and preferences of drivers to meet the needs of different drivers.

[0059] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection, an electrical connection, or communication with each other; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0060] Obviously, the embodiments described above are only some embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are given in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific embodiments, or to perform equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of the present invention, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. An AI artificial intelligence assisted driving system, characterized in that: include: AI perception module: Equipped with cameras, lidar, radar and ABS smart sensors, it collects information about the vehicle's surroundings in real time and processes and analyzes the collected data through deep learning and computer vision algorithms to identify roads, vehicles, pedestrians and obstacles; AI decision-making module: receives information from the AI ​​perception module and uses machine learning algorithms and reinforcement learning models to formulate driving strategies and plan paths, including lane keeping, obstacle avoidance, and speed adjustment; AI control module: Based on the output of the AI ​​decision module, the model predictive control MPC or adaptive cruise control ACC is used to accurately control the acceleration, braking and steering operations of the vehicle; Execution module: The instructions of the AI ​​control module are specifically executed through the electric motor and hydraulic system actuator to realize intelligent driving of the vehicle.

2. The AI ​​artificial intelligence assisted driving system according to claim 1, characterized in that: It also includes an AI misoperation detection and intervention module, which is used to monitor the driver's driving operation in real time.

3. The AI ​​artificial intelligence assisted driving system according to claim 2, characterized in that: The AI ​​misoperation detection and intervention module uses an AI algorithm to analyze the deviation between the driver's intention and the actual vehicle response. When the accelerator is detected as the brake, according to the preset acceleration anomaly detection logic, the expression is: Anomaly_Detection=Abs(Actual_Accel-Predicted_Accel)>Threshold Among them, Actual_Accel is the actual acceleration, Predicted_Accel is the acceleration predicted based on the current environment and driver operation, and Threshold is the set acceleration abnormality threshold. If the detection value exceeds the threshold, intervention measures are immediately initiated, forced deceleration or parking, and a warning is issued to the driver.

4. The AI ​​artificial intelligence assisted driving system according to claim 1, characterized in that: It also includes an AI path planning and analysis module, which includes path simulation, path evaluation and path selection sub-modules. The AI ​​path planning and analysis module uses an AI algorithm to simulate, evaluate and sort the driving path to select the optimal path.

5. The AI ​​artificial intelligence assisted driving system according to claim 4, characterized in that: The AI ​​algorithm is a Q-Learning algorithm, where the update expression of the Q-Learning algorithm is: Among them, s represents the current state, a represents the current action, Reward represents the reward, and s ′ Indicates the next state, a ′ represents the next action, α is the learning rate, and γ is the discount factor.

6. The AI ​​artificial intelligence assisted driving system according to claim 4, characterized in that: The AI ​​decision-making module and the AI ​​path planning and analysis module are connected to an AI navigation prompt module, which provides real-time navigation prompts and driving suggestions to the driver based on the outputs of the AI ​​decision-making module and the AI ​​path planning and analysis module, and works in conjunction with the AI ​​misoperation detection and intervention module to provide additional safety prompts and intervention measures.

7. The AI ​​artificial intelligence assisted driving system according to claim 1, characterized in that: The AI ​​perception module is also configured with an abnormality identification submodule for identifying abnormal situations in environmental information and sending an alarm signal to the decision module. The abnormality identification submodule uses the following expression for abnormality detection: Among them, x i represents the current value of the i-th sensor data, u i represents the average value of the i-th sensor data, w i It represents the weight of the i-th sensor data, n represents the number of sensors, and when Anomaly_Score exceeds the preset threshold, it is judged as abnormal.

8. The AI ​​artificial intelligence assisted driving system according to claim 1, characterized in that: The execution module includes a redundant design, which includes sensor redundancy, computing unit redundancy and actuator redundancy. The sensor redundancy is to install backup sensors at key sensor positions to deal with sensor failures; the computing unit redundancy adopts multi-core processors and distributed computing architecture to improve the reliability and fault tolerance of the computing unit; the actuator redundancy configures backup actuators in the actuator to ensure that control instructions can still be executed normally when a single actuator fails.

9. The AI ​​artificial intelligence assisted driving system according to claim 1, characterized in that: The system is also provided with a user interaction interface, which is used to display system status, driving suggestions and safety warnings, including vehicle speed, navigation information, emergency braking prompts, and driver fatigue warnings. The user interaction interface allows the driver to configure and personalize the system as needed.

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