Low-carbon environment-friendly trolley with intelligent path planning and obstacle avoidance functions

Abstract

The invention discloses a low-carbon environment-friendly trolley with intelligent path planning and obstacle avoidance functions, and relates to the technical field of low-carbon environment protection. The control system comprises a sensor module, a fault diagnosis module, an alarm module, an automatic repair module, an operation degradation module, an algorithm library, a central control module, an output monitoring module and an emergency braking module. According to the low-carbon environment-friendly trolley with the intelligent path planning and obstacle avoidance functions, a plurality of different types of sensors are adopted as redundant backups, when a certain sensor breaks down, other sensors can still work continuously, and necessary environment information is provided; the fault diagnosis module is matched to monitor the working state of the sensor in real time, when the sensor has a serious fault and cannot be repaired immediately, the sensor can be switched to a degradation mode for operation, and path planning and obstacle avoidance can be realized by depending on a simpler algorithm and less sensor data.

Description

Technical Field

[0001] The present invention relates to the field of low-carbon and environmentally friendly technology, and in particular to a low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions. Background Art

[0002] As the name suggests, low-carbon, environmentally friendly cars are those that have low carbon emissions and minimal environmental pollution during production, use, and recycling. These cars typically use environmentally friendly energy sources such as electricity, hydrogen, and other clean energy sources rather than traditional fossil fuels. They are generally small-displacement or lightweight designs, and by optimizing engine displacement or using lightweight materials, they reduce energy consumption and emissions during operation. Currently, common low-carbon, environmentally friendly car types include pure electric vehicles, hybrid vehicles, and solar-powered vehicles. The development of these low-carbon, environmentally friendly cars is of great significance for promoting green travel, reducing carbon emissions, and protecting the ecological environment. Low-carbon, environmentally friendly cars will gradually become a mainstream trend in the future automotive market.

[0003] With the development of intelligent technology, more and more smart cars are coming into people's view, which undoubtedly further promotes the development of low-carbon and environmentally friendly vehicles. For example, the currently more common intelligent path planning and obstacle avoidance functions can reduce the distance and time of vehicle driving, thereby reducing energy consumption. The optimal path selection allows the vehicle to avoid unnecessary detours and waiting, improving energy efficiency. Combined with intelligent obstacle avoidance functions, it significantly reduces the increased energy consumption and emissions caused by collisions or emergency braking.

[0004] However, in actual use, since the existing intelligent path planning and obstacle avoidance technology relies on sensors to obtain information, once the sensor fails, the car's intelligent algorithm will not be able to execute normally, resulting in the car being unable to accurately plan the path or avoid obstacles in time. In addition, after the algorithm fails, the car will be automatically returned to the user. Under certain extreme conditions, the user may not have enough reaction time, which may lead to traffic accidents. Summary of the Invention

[0005] The purpose of the present invention is to provide a low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a low-carbon, environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions, wherein the low-carbon, environmentally friendly vehicle is equipped with a control system for controlling the vehicle's movement, and the control system includes the following modules:

[0007] Sensor module: Integrates multiple different types of sensors to sense and collect data about the car's surroundings in real time, such as distance, obstacle location, road conditions, etc., to achieve comprehensive perception and data collection of the surrounding environment;

[0008] Fault diagnosis module: responsible for real-time monitoring of the working status of the sensor, performing fault detection on the sensor, and immediately triggering the alarm module and automatic repair module once a sensor fault is found;

[0009] Alarm module: Responsible for issuing warning information to users in case of sensor failure or system abnormality, and providing manual control options to allow users to manually intervene in emergency situations;

[0010] Automatic repair module: attempts to restore the function of the faulty sensor, and triggers the run-down module if the sensor failure is serious and cannot be repaired immediately, to ensure that the car can continue to operate in the event of a fault;

[0011] Degraded operation module: When a sensor failure cannot be repaired, it sends a request to the central control module to try to switch the car to degraded mode. In degraded mode, the car can rely on simpler algorithms and less sensor data to plan paths and avoid obstacles, ensuring basic driving functions.

[0012] Algorithm library: Responsible for storing path planning and obstacle avoidance algorithms, providing algorithm support for the central control module. When the main algorithm fails to work properly due to sensor failure, it switches to the backup algorithm to ensure the accuracy and reliability of path planning and obstacle avoidance.

[0013] Central control module: Combines currently functioning sensors to determine the sensor data that can be obtained normally. Based on this data, it matches the algorithms in the algorithm library to generate new path planning and obstacle avoidance instructions to ensure that the car can drive intelligently and accurately.

[0014] Output monitoring module: responsible for monitoring whether the instructions generated by the central control module are output on time. Once the instruction output is delayed, the emergency braking module is immediately triggered to ensure the accurate transmission and execution of the instructions;

[0015] Emergency braking module: When the control command is not output normally, emergency braking measures are immediately taken to park the car safely on the roadside or in a safe area to avoid accidents.

[0016] Furthermore, the sensor module specifically integrates the following sensors:

[0017] LiDAR measures distance by emitting a laser beam and receiving the reflected signal. The time difference between emitting and receiving the laser beam is proportional to the distance, and distance information is obtained by calculating the time difference. The LiDAR uses multiple transmitting and receiving channels to construct a three-dimensional map of the environment through scanning.

[0018] Camera: Captures light through a lens and converts it into electrical signals. These signals undergo analog-to-digital conversion to form digital image data. The camera uses image processing algorithms, such as edge detection and feature extraction, to identify and track objects in the image.

[0019] Ultrasonic sensor: measures distance by emitting ultrasonic waves and receiving reflected signals. The propagation speed of ultrasonic waves depends on the medium and is approximately 340 m / s in air. Specifically, the distance between the sensor and the obstacle is calculated by measuring the time difference between emitting and receiving ultrasonic waves and combining it with the propagation speed of ultrasonic waves.

[0020] Infrared sensor: determines the presence and location of an object by detecting infrared radiation from the surrounding environment (infrared radiation is a form of thermal radiation from an object, and its intensity is related to the object's temperature). The infrared sensor consists of an infrared transmitter and an infrared receiver. The transmitter emits infrared light, and the receiver receives the reflected infrared light. When an object approaches the sensor, it reflects more infrared light to the receiver, thereby triggering the sensor's output signal.

[0021] Furthermore, the fault diagnosis module detects whether the sensor has an abnormality or fault by continuously monitoring the data output and response of the sensor, and is programmed with trigger logic, which determines whether to trigger the alarm module and the automatic repair module according to the sensor status.

[0022] Furthermore, the alarm module sends warning information to the user via a display screen, speaker or network communication, and the alarm information is in the form of text, sound prompt or image, and the alarm module implements a manual control function in the software, allowing the user to manually turn off the alarm through the interface or physical switch.

[0023] Furthermore, the automatic repair module has a built-in self-diagnosis and repair program for automatically diagnosing and repairing specific fault types, including detecting the sensor's hardware connection, circuit status, signal transmission, etc., and attempting to repair the discovered problems by adjusting parameters. The specific operations are: according to the specific manifestations of the fault, adjust the sensor's parameter settings, including adjusting the sensor's sensitivity, sampling rate, working range, etc., to restore it to normal working state, and during the repair process, monitor the sensor's response and output in real time to evaluate the effectiveness of the repair measures.

[0024] Furthermore, the algorithm library includes the following path planning algorithms:

[0025] Dijkstra's algorithm: uses a priority queue (such as a binary heap) to select the node closest to the source among the currently unvisited nodes and updates the shortest path estimates of its neighboring nodes. It is applicable to weighted graphs and is used to find the shortest path from a single source to all other nodes.

[0026] A* algorithm: Combines heuristic functions (such as Manhattan distance and Euclidean distance) with priority queues to gradually expand the optimal path until the target node is found. The heuristic function is used to estimate the cost from the current node to the target node.

[0027] Probabilistic Roadmap (PRM) algorithm: First build a graph representation consisting of randomly sampled points and edges connecting these points, and then use the Graph Dijkstra or A* algorithm on the graph to find a path;

[0028] Dynamic Window Method: Based on the current speed and acceleration limits of the car, a set of feasible velocity vectors (i.e., "dynamic window") are generated, and the trajectories corresponding to these velocity vectors are evaluated, and the optimal trajectory is selected as the next action;

[0029] Fast Random Search Tree (RRT) algorithm: constructs a search tree by random sampling, and each iteration attempts to expand the tree towards the target point until a feasible path is found. In actual operation, different metrics (such as Euclidean distance) can be used to select the nearest neighbor node.

[0030] Furthermore, the algorithm library includes the following obstacle avoidance algorithms:

[0031] Artificial potential field method: define an attractive potential field for the target point and a repulsive potential field for the obstacle. The car moves in the direction of the potential field gradient descent, thus avoiding obstacles and heading towards the target point.

[0032] Vector field histogram: Divides the environment around the robot into multiple sectors and calculates the obstacle density and velocity vector in each sector. Based on this information, a comprehensive vector field is generated to guide the robot's movement.

[0033] Elastic band algorithm: The path is represented as a set of elastic bands connecting nodes. By adjusting the position of the nodes and the tension of the elastic bands, obstacles can be avoided and the path can be made smoother.

[0034] Behavior-based control: Define a series of basic behaviors (such as obstacle avoidance, target tracking, speed control, etc.), assign a weight to each behavior, and then choose the next action of the car based on the combination of these weights.

[0035] Furthermore, the central control module includes the following submodules:

[0036] Data integration submodule: Receives data from various sensors, verifies them, removes abnormal or erroneous data, and then fuses the data to obtain comprehensive environmental information;

[0037] Algorithm matching submodule: Based on the fused sensor data, it performs real-time analysis of the current environment, identifies key information such as obstacles, road boundaries, and feasible paths, and selects the path planning and obstacle avoidance algorithm that best suits the current scenario from the algorithm library based on the environmental analysis results. For example, in open areas, the more efficient A* algorithm may be selected; in complex environments, the more flexible RRT algorithm may be selected. At the same time, the algorithm parameters are adjusted in real time based on environmental characteristics and vehicle status to ensure that the algorithm can achieve optimal performance.

[0038] Instruction generation submodule: Use the selected algorithm to generate an optimal path from the current position to the target position. During the path planning process, it combines obstacle information to generate corresponding obstacle avoidance strategies to ensure that the car can safely bypass obstacles. The path planning and obstacle avoidance strategies are then converted into specific driving instructions, such as steering angle, driving speed, etc., and sent to the car's actuator.

[0039] Furthermore, the output monitoring module records the timestamp of each instruction generated by the central control module, and continuously tracks the sending and execution status of these instructions, and determines whether there is a delay in the output of the instruction by comparing it with a preset time threshold. The time threshold is negatively correlated with the speed of the car, that is, the faster the speed of the car, the smaller the time threshold, and the slower the car timing, the larger the time threshold.

[0040] Furthermore, the emergency braking process of the emergency braking module is as follows:

[0041] Braking signal sending: Once the triggering conditions are met, the emergency braking module immediately sends a braking signal to the vehicle's braking system;

[0042] Braking system response: After receiving the braking signal, the braking system is activated quickly, and the vehicle is decelerated and eventually stopped by increasing friction or adjusting motor output;

[0043] Safe parking: During braking, the system continuously monitors the vehicle's speed and position to ensure it can park smoothly and safely on the roadside or in a pre-set safety zone.

[0044] Status feedback: After emergency braking is completed, the module will feedback the braking status and results to the central control module and other related systems for subsequent processing.

[0045] The present invention provides a low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions, which has the following beneficial effects:

[0046] The present invention uses multiple different types of sensors as redundant backups. When a sensor fails, other sensors can continue to work and provide necessary environmental information. In conjunction with the fault diagnosis module, the working status of the sensor is monitored in real time. Once a fault is detected, it immediately attempts self-repair and can switch to a degraded mode when the sensor failure is serious and cannot be repaired immediately. At the same time, the present invention designs a variety of path planning algorithms and obstacle avoidance algorithms as backups. When the main algorithm cannot work normally due to a sensor failure, it can switch to the backup algorithm, so that the car can rely on simpler algorithms and less sensor data to plan paths and avoid obstacles in degraded mode. In particular, when a sensor failure causes the car to be unable to accurately judge the surrounding environment, emergency braking measures can be taken to safely park the car on the roadside or in a safe area to avoid potential dangers. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of a control system for a low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions according to the present invention;

[0048] Figure 2 This is a schematic diagram of the central control module of a low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions according to the present invention. DETAILED DESCRIPTION

[0049] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0050] like Figure 1-Figure 2 As shown, a low-carbon and environmentally friendly car with intelligent path planning and obstacle avoidance functions is equipped with a control system to control the car's activities. The control system includes the following modules:

[0051] Sensor module: Integrates multiple different types of sensors to sense and collect data about the car's surroundings in real time, such as distance, obstacle location, road conditions, etc., to achieve comprehensive perception and data collection of the surrounding environment.

[0052] In this embodiment, the sensor module specifically integrates the following sensors:

[0053] LiDAR: It measures distance by emitting a laser beam and receiving the reflected signal. The time difference between emitting and receiving the laser beam is proportional to the distance, and the distance information is obtained by calculating the time difference. LiDAR uses multiple transmitting and receiving channels to construct a three-dimensional map of the environment through scanning.

[0054] Camera: Captures light through a lens and converts it into electrical signals. These signals undergo analog-to-digital conversion to form digital image data. The camera uses image processing algorithms, such as edge detection and feature extraction, to identify and track objects in the image.

[0055] Ultrasonic sensor: measures distance by emitting ultrasonic waves and receiving reflected signals. The propagation speed of ultrasonic waves depends on the medium and is approximately 340 m / s in air. Specifically, the distance between the sensor and the obstacle is calculated by measuring the time difference between emitting and receiving ultrasonic waves and combining it with the propagation speed of ultrasonic waves.

[0056] Infrared sensor: It determines the presence and location of an object by detecting infrared radiation from the surrounding environment (infrared radiation is a form of thermal radiation from an object, and its intensity is related to the object's temperature). The infrared sensor consists of an infrared transmitter and an infrared receiver. The transmitter emits infrared light, and the receiver receives the reflected infrared light. When an object approaches the sensor, it reflects more infrared light to the receiver, thereby triggering the sensor's output signal.

[0057] Fault Diagnosis Module: This module monitors the sensor's operating status in real time and detects sensor faults. Once a sensor fault is detected, it immediately triggers the alarm module and the automatic repair module. This module continuously monitors the sensor's data output and response to detect any sensor anomalies or faults. It also includes trigger logic that determines whether to trigger the alarm module and the automatic repair module based on the sensor's status.

[0058] Alarm Module: This module issues warnings to the user regarding sensor failures or system anomalies, while also providing manual override options to allow the user to manually intervene in emergency situations. In this embodiment, the alarm module sends warning messages to the user via a display, speaker, or network communication. The warning messages are in the form of text, audio prompts, or images. The alarm module also implements a manual override function in the software, allowing the user to manually disable the alarm via the interface or a physical switch.

[0059] Automatic repair module: attempts to restore the function of the faulty sensor, and triggers the operation of the degradation module when the sensor failure is serious and cannot be repaired immediately to ensure that the car can continue to operate in the event of a fault. The automatic repair module has a built-in self-diagnosis and repair program for automatic diagnosis and repair of specific fault types, including detecting the sensor's hardware connection, circuit status, signal transmission, etc., and attempting to repair the found problems by adjusting parameters. Specific operations: According to the specific manifestations of the fault, adjust the sensor's parameter settings, including adjusting the sensor's sensitivity, sampling rate, operating range, etc., to restore it to normal working state. During the repair process, monitor the sensor's response and output in real time to evaluate the effectiveness of the repair measures.

[0060] Run the degraded module: When a sensor failure cannot be repaired, a request is sent to the central control module to try to switch the car to degraded mode. In degraded mode, the car can rely on simpler algorithms and less sensor data to plan paths and avoid obstacles to ensure basic driving functions.

[0061] Algorithm library: stores path planning and obstacle avoidance algorithms, and provides algorithm support for the central control module. When the main algorithm fails to work properly due to sensor failure, it switches to the backup algorithm to ensure the accuracy and reliability of path planning and obstacle avoidance.

[0062] The algorithm library includes the following path planning algorithms:

[0063] Dijkstra's algorithm: uses a priority queue (such as a binary heap) to select the node closest to the source among the currently unvisited nodes and updates the shortest path estimates of its neighboring nodes. It is applicable to weighted graphs and is used to find the shortest path from a single source to all other nodes.

[0064] A* algorithm: Combines heuristic functions (such as Manhattan distance and Euclidean distance) with priority queues to gradually expand the optimal path until the target node is found. The heuristic function is used to estimate the cost from the current node to the target node.

[0065] Probabilistic Roadmap (PRM) algorithm: First, a graph representation is constructed, which consists of randomly sampled points and edges connecting these points. Then, a graph Dijkstra or A* algorithm is used on the graph to find a path. In this embodiment, data structures such as k-nearest neighbor search can be used to accelerate the construction of the graph.

[0066] Dynamic Window Method: Based on the current speed and acceleration limits of the car, a set of feasible velocity vectors (i.e., "dynamic window") are generated, and the trajectories corresponding to these velocity vectors are evaluated, and the optimal trajectory is selected as the next action;

[0067] Fast Random Search Tree (RRT) algorithm: constructs a search tree by random sampling. Each iteration attempts to expand the tree toward the target point until a feasible path is found. In practice, different metrics (such as Euclidean distance) can be used to select the nearest neighbor node.

[0068] In this embodiment, the probabilistic roadmap algorithm is the main algorithm, and the Dijkstra algorithm, A* algorithm, dynamic window method, fast random search tree algorithm, dynamic window method, fast random search tree algorithm, etc. are backup algorithms.

[0069] The algorithm library includes the following obstacle avoidance algorithms:

[0070] Artificial potential field method: define an attractive potential field for the target point and a repulsive potential field for the obstacle. The car moves in the direction of the potential field gradient descent, thus avoiding obstacles and heading towards the target point.

[0071] Vector field histogram: Divides the environment around the robot into multiple sectors and calculates the obstacle density and velocity vector in each sector. Based on this information, a comprehensive vector field is generated to guide the robot's movement.

[0072] Elastic band algorithm: The path is represented as a set of elastic bands connecting nodes. By adjusting the position of the nodes and the tension of the elastic bands, obstacles can be avoided and the path can be made smoother.

[0073] Behavior-based control: Define a series of basic behaviors (such as obstacle avoidance, target tracking, speed control, etc.), assign a weight to each behavior, and then choose the next action of the car based on the combination of these weights;

[0074] In this embodiment, behavior-based control is the main algorithm, and artificial potential field method, vector field histogram, elastic band algorithm, etc. are backup algorithms.

[0075] Central control module: Combines the currently functioning sensors to determine the sensor data that can be obtained normally, and based on this data, matches it with the algorithms in the algorithm library to generate new path planning and obstacle avoidance instructions to ensure that the car can drive intelligently and accurately. This module includes the following submodules:

[0076] Data integration submodule: Receives data from various sensors, verifies them, removes abnormal or erroneous data, and then fuses the data to obtain comprehensive environmental information;

[0077] Algorithm matching submodule: Based on the fused sensor data, it performs real-time analysis of the current environment, identifies key information such as obstacles, road boundaries, and feasible paths, and selects the path planning and obstacle avoidance algorithm that best suits the current scenario from the algorithm library based on the environmental analysis results. For example, in open areas, the more efficient A* algorithm may be selected; in complex environments, the more flexible RRT algorithm may be selected. At the same time, the algorithm parameters are adjusted in real time based on environmental characteristics and vehicle status to ensure that the algorithm can achieve optimal performance.

[0078] Instruction generation submodule: Use the selected algorithm to generate an optimal path from the current position to the target position. During the path planning process, it combines obstacle information to generate corresponding obstacle avoidance strategies to ensure that the car can safely bypass obstacles. The path planning and obstacle avoidance strategies are then converted into specific driving instructions, such as steering angle, driving speed, etc., and sent to the car's actuator.

[0079] Output Monitoring Module: This module monitors whether the instructions generated by the central control module are output on time. If the instruction output is delayed, it immediately triggers the emergency braking module to ensure the accurate transmission and execution of the instruction. In this embodiment, the output monitoring module records the timestamp of each instruction generated by the central control module and continuously tracks the transmission and execution status of these instructions. By comparing this time with a preset time threshold, it determines whether there is a delay in the output of the instruction. The time threshold is negatively correlated with the vehicle speed. That is, the faster the vehicle speed, the smaller the time threshold, and the slower the vehicle timing, the larger the time threshold.

[0080] Emergency braking module: When the control command is not output normally, emergency braking measures are immediately taken to safely stop the car on the roadside or in a safe area to avoid accidents. The emergency braking process is as follows:

[0081] Braking signal sending: Once the triggering conditions are met, the emergency braking module immediately sends a braking signal to the vehicle's braking system;

[0082] Braking system response: After receiving the braking signal, the braking system is activated quickly, and the vehicle is decelerated and eventually stopped by increasing friction or adjusting motor output;

[0083] Safe parking: During braking, the system continuously monitors the vehicle's speed and position to ensure it can park smoothly and safely on the roadside or in a pre-set safety zone.

[0084] Status feedback: After emergency braking is completed, the module will feedback the braking status and results to the central control module and other related systems for subsequent processing.

[0085] The embodiments of the present invention are presented for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described in order to better illustrate the principles of the invention and its practical application and to enable those skilled in the art to understand the invention and design various embodiments with various modifications as suited for specific applications.

Claims

1. A low-carbon and environmentally friendly car with intelligent path planning and obstacle avoidance functions, characterized in that: The low-carbon and environmentally friendly car is equipped with a control system for controlling the car's activities. The control system includes the following modules: Sensor module: Integrates multiple different types of sensors to sense and collect data about the car's surroundings in real time, enabling comprehensive perception and data collection of the surrounding environment. Fault diagnosis module: responsible for real-time monitoring of the working status of the sensor, performing fault detection on the sensor, and immediately triggering the alarm module and automatic repair module once a sensor fault is found; Alarm module: responsible for issuing warning information to users when sensor failure occurs, and providing manual control options to allow users to manually intervene in emergency situations; Automatic repair module: attempts to restore the function of the faulty sensor and triggers the run of the degradation module if the sensor failure is serious and cannot be repaired immediately; Operation degradation module: When the sensor failure cannot be repaired, it sends a request to the central control module to try to switch the car to the degraded mode; Algorithm library: responsible for storing path planning and obstacle avoidance algorithms, providing algorithm support for the central control module; Central control module: This module combines currently functioning sensors to determine the sensor data that can be obtained normally. Based on this data, it matches the algorithms in the algorithm library to generate new path planning and obstacle avoidance instructions. Output monitoring module: responsible for monitoring whether the instructions generated by the central control module are output on time. If the instruction output is delayed, the emergency braking module will be triggered immediately. Emergency braking module: When the control command is not output normally, emergency braking measures are immediately taken to safely stop the car on the roadside or in a safe area.

2. The low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions according to claim 1, characterized in that: The sensor module specifically integrates the following sensors: LiDAR measures distance by emitting a laser beam and receiving the reflected signal. The time difference between emitting and receiving the laser beam is proportional to the distance, and distance information is obtained by calculating the time difference. The LiDAR uses multiple transmitting and receiving channels to construct a three-dimensional map of the environment through scanning. Camera: Captures light through a lens, converts it into electrical signals, and forms digital image data after analog-to-digital conversion. The camera uses image processing algorithms to identify and track objects in the image. Ultrasonic sensor: measures distance by emitting ultrasonic waves and receiving reflected signals. Specifically, the sensor calculates the distance between the sensor and the obstacle by measuring the time difference between emitting and receiving the ultrasonic wave and combining it with the propagation speed of the ultrasonic wave. Infrared sensor: Determines the presence and location of objects by detecting infrared radiation from the surrounding environment. The infrared sensor consists of an infrared transmitter and an infrared receiver. The transmitter emits infrared light, and the receiver receives the reflected infrared light. When an object approaches the sensor, it reflects more infrared light to the receiver, thereby triggering the sensor's output signal.

3. The low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions according to claim 1 is characterized in that: The fault diagnosis module detects whether the sensor has an abnormality or fault by continuously monitoring the data output and response of the sensor, and is programmed with trigger logic. The trigger logic determines whether to trigger the alarm module and the automatic repair module based on the sensor status.

4. The low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions according to claim 1, characterized in that: The alarm module sends warning information to the user via a display screen, speaker or network communication, and the alarm information is in the form of text, sound prompt or image. The alarm module implements a manual control function in the software, allowing the user to manually turn off the alarm through the interface or physical switch.

5. The low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions according to claim 1 is characterized in that: The automatic repair module has a built-in self-diagnosis and repair program for automatically diagnosing and repairing specific fault types, including detecting the sensor's hardware connection, circuit status, and signal transmission, and attempting to repair the problems found by adjusting parameters. The specific operation is: according to the specific manifestation of the fault, adjust the sensor's parameter settings, including adjusting the sensor's sensitivity, sampling rate, and operating range to restore it to normal working state. During the repair process, the sensor's response and output are monitored in real time to evaluate the effectiveness of the repair measures.

6. The low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions according to claim 1, characterized in that: The algorithm library includes the following path planning algorithms: Dijkstra algorithm: Use a priority queue to select the node closest to the source among the currently unvisited nodes and update the shortest path estimate of its neighboring nodes; A* algorithm: Combines a heuristic function and a priority queue to gradually expand the optimal path until the target node is found. The heuristic function is used to estimate the cost from the current node to the target node. Probabilistic roadmap algorithms: First, a graph representation is constructed, which consists of randomly sampled points and edges connecting these points, and then the graph Dijkstra or A* algorithm is used on the graph to find a path; Dynamic Window Method: Based on the current speed and acceleration limit of the car, a set of feasible velocity vectors are generated, and the trajectories corresponding to these velocity vectors are evaluated, and the optimal trajectory is selected as the next action; Fast Random Search Tree Algorithm: Build a search tree by random sampling, and each iteration tries to expand the tree toward the goal point until a feasible path is found.

7. The low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions according to claim 1, characterized in that: The algorithm library includes the following obstacle avoidance algorithms: Artificial potential field method: define an attractive potential field for the target point and a repulsive potential field for the obstacle. The car moves in the direction of the potential field gradient descent, thus avoiding obstacles and heading towards the target point. Vector field histogram: Divides the environment around the robot into multiple sectors and calculates the obstacle density and velocity vector in each sector. Based on this information, a comprehensive vector field is generated to guide the robot's movement. Elastic band algorithm: The path is represented as a set of elastic bands connecting nodes. By adjusting the position of the nodes and the tension of the elastic bands, obstacles can be avoided and the path can be made smoother. Behavior-based control: Define a series of basic behaviors and assign a weight to each behavior. Then, the next action of the car is selected based on the combination of these weights.

8. The low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions according to claim 1, characterized in that: The central control module includes the following submodules: Data integration submodule: Receives data from various sensors, verifies them, removes abnormal or erroneous data, and then fuses the data to obtain comprehensive environmental information; Algorithm matching submodule: Based on the fused sensor data, it performs real-time analysis of the current environment, identifies key information, and selects the path planning and obstacle avoidance algorithm that best suits the current scenario from the algorithm library based on the environmental analysis results. Instruction generation submodule: Use the selected algorithm to generate the optimal path from the current position to the target position, and combine it with obstacle information to generate the corresponding obstacle avoidance strategy. Then, the path planning and obstacle avoidance strategy are converted into specific driving instructions and sent to the actuator of the car.

9. The low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions according to claim 1, characterized in that: The output monitoring module records the timestamp of each instruction generated by the central control module, and continuously tracks the sending and execution status of these instructions. By comparing with the preset time threshold, it determines whether there is a delay in the output of the instruction. The time threshold is negatively correlated with the speed of the vehicle, that is, the faster the vehicle speed, the smaller the time threshold, and the slower the vehicle timing, the larger the time threshold.

10. The low-carbon and environmentally friendly vehicle with intelligent path planning and obstacle avoidance functions according to claim 1, characterized in that: The emergency braking process of the emergency braking module is as follows: Braking signal sending: Once the triggering conditions are met, the emergency braking module immediately sends a braking signal to the vehicle's braking system; Braking system response: After receiving the braking signal, the braking system is activated quickly, slowing down the vehicle and eventually stopping it by increasing friction or adjusting the motor output; Safe parking: During braking, the system continuously monitors the vehicle's speed and position to ensure it stops at the roadside or in a pre-set safe area. Status feedback: After emergency braking is completed, the module will feedback the braking status and results to the central control module and other related systems.

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