Workshop anti-collision device

Abstract

The invention relates to the technical field of vehicle collision avoidance, and discloses a workshop collision avoidance device which comprises a workshop wireless communication distance measurement module, a sensor data acquisition module, a Kalman filtering module, a collision prediction module, an optimal control module and an emergency braking and warning module. The invention further provides an anti-collision method of the workshop anti-collision device, and the anti-collision method comprises the following steps: S1, sensor data acquisition: acquiring real-time data of the vehicle and the surrounding environment through a plurality of sensors, S2, workshop wireless communication distance measurement: carrying out timing and directional wireless signal communication by utilizing directional antenna array communication module units mounted at the vehicle head and the vehicle tail, and carrying out vehicle collision avoidance. And calculating the inter-vehicle distance and the inter-vehicle relative speed. According to the invention, a multi-sensor data acquisition and fusion technology is adopted, comprehensive perception of the surrounding environment of the vehicle is ensured, real-time data are acquired through a plurality of sensors at the same time, and the problem of a blind area possibly existing in a single sensor is effectively avoided.

Description

Technical Field

[0001] The invention relates to the technical field of vehicle anti-collision, in particular to a workshop anti-collision device. Background Art

[0002] With the continuous advancement of vehicle technology, the research and application of vehicle collision avoidance systems have gradually become an important means to ensure road safety. Existing technologies mainly focus on collision avoidance methods based on a single sensor. Although they can provide collision warnings to a certain extent, the single sensor they rely on is easily affected by environmental factors and has problems such as insufficient accuracy and delayed response.

[0003] Existing vehicle collision avoidance systems usually use single-type sensors such as ultrasonic sensors, lidar or cameras to collect data and issue warnings based on these data. However, a single sensor is often unable to fully perceive the complex environment of a workshop, such as the type, shape and motion characteristics of obstacles, resulting in poor adaptability of the collision avoidance system to the workshop environment, low accuracy and timeliness. These traditional technologies have failed to effectively solve the problem of inaccurate information caused by limited sensor viewing angles and insufficient data processing capabilities, and it is difficult to provide reliable collision warnings in dynamic environments. In addition, most existing collision warning and obstacle avoidance systems rely on simple trigger mechanisms, which issue warnings when the collision risk reaches a preset threshold, or brake using fixed control strategies. However, this simple mechanism A single warning mechanism cannot fully consider the actual state and dynamic changes of the vehicle. When facing a complex workshop environment, it is often unable to make an accurate and timely response. Especially in an emergency, the existing technology often lacks the ability to adjust the braking force according to the real-time vehicle state, resulting in an unsatisfactory collision avoidance effect. Moreover, the traditional collision prediction module often relies on rough distance and speed measurements to judge the collision risk, lacking the ability to adapt to environmental changes in real time. Even if some systems use algorithms such as Kalman filtering to improve state estimation, these technologies still fail to solve the problems of insufficient multi-sensor data fusion and long processing delays. Therefore, the existing technology has the problem of insufficient accuracy and reliability, and it is difficult to meet the requirements of high real-time and high precision of the collision avoidance system in the workshop environment. For this reason, those skilled in the art have proposed a workshop collision avoidance device to solve the above problems. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a vehicle collision avoidance device, which solves the problems of incomplete information from a single sensor, inaccurate collision prediction and delayed braking response in the prior art.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a workshop anti-collision device, comprising: The inter-vehicle wireless communication ranging module is installed at the front and rear of the vehicle and is used to send and receive wireless signals in a timely and directional manner to calculate the inter-vehicle distance and inter-vehicle relative speed; A sensor data acquisition module is used to collect real-time data of the vehicle and its surroundings, including but not limited to vehicle position, speed, acceleration, obstacle position and speed information; A Kalman filter module is used to process and fuse the sensor data and the vehicle wireless communication ranging data, and to estimate the status of the vehicle and the obstacle in real time based on the sensor data; A collision prediction module, used to calculate the collision risk and perform collision prediction based on the state estimation output by the Kalman filter module and the relative motion between the vehicle and the obstacle; an optimal control module, for calculating an optimal control strategy to adjust the acceleration or braking force of the vehicle to avoid collision according to the collision prediction result of the collision prediction module; The emergency brake and warning module is used to perform emergency braking operations and warn the driver when the risk of collision is unavoidable.

[0006] Preferably, the sensor data acquisition module includes: A radar unit to measure the relative distance and speed between the vehicle and obstacles; A LiDAR unit that detects the precise structure of the surrounding environment and provides three-dimensional spatial data; A camera unit for image recognition and providing position information of dynamic objects; Ultrasonic sensor unit for close-range obstacle detection at low speeds; The GPS unit is used to provide the vehicle's positioning information.

[0007] Preferably, the Kalman filter module includes: A state estimation unit, used to update the position information, speed information and acceleration information of the vehicle and obstacles in real time; A noise filtering unit, used to eliminate noise in sensor data and output accurate state estimation results; The state transfer matrix describes the relationship between the motion states of the vehicle and obstacles over time, and calculates the process noise covariance and measurement noise covariance.

[0008] Preferably, the collision prediction module includes: A collision risk assessment unit, used to calculate the risk value of a future collision based on the relative speed, distance, acceleration and other relevant information between the vehicle and the obstacle; The path prediction unit is used to predict the future movement trajectory of the vehicle and obstacles, and determine whether there is a potential collision risk based on the current driving status; The collision detection algorithm calculates the time and location of the expected collision based on the relative motion information between the vehicle and the obstacle.

[0009] Preferably, the optimal control module includes: A control input calculation unit, used to calculate the optimal control input to avoid the collision based on the collision prediction result and the current state of the vehicle; The acceleration optimization unit is used to determine the optimal value of vehicle acceleration to ensure that the vehicle completes braking or avoidance operations in a short time; The vehicle dynamics model is used to establish the dynamics model of the vehicle under different control inputs, including the changing relationship between the vehicle acceleration, speed and position.

[0010] Preferably, the emergency braking and warning module includes: The emergency brake control unit is used to execute the braking strategy calculated by the optimal control module and automatically adjust the vehicle's braking force when the collision risk reaches a set threshold; The driver warning unit is used to warn the driver through visual, audible and vibration means when a collision is unavoidable.

[0011] Preferably, the collision prediction module calculates the collision risk by the following steps: Get the current position and speed of the vehicle and the obstacle, and calculate the relative distance between them; Based on the acceleration and motion state of the vehicle and obstacle, predict the relative distance between the two in the future time period; Based on the expected distance and set thresholds, the risk of collision is assessed and an alarm is generated.

[0012] Preferably, the optimal control module calculates the optimal control strategy through the following steps: According to the output of the collision prediction module, the relative distance and motion trajectory between the vehicle and the obstacle are calculated; Evaluate the impact of different control inputs on collision outcomes through vehicle dynamics models; The optimal control inputs are calculated based on the objectives of minimizing the risk of collision and minimizing the response time.

[0013] Preferably, the workshop wireless communication ranging module includes: Directional antenna array communication module unit, used for wireless communication distance measurement between vehicles and calculation of relative speed between vehicles; The signal analysis unit is used to analyze the wireless communication signal and extract the signal characteristic parameters required for ranging.

[0014] The anti-collision method of the workshop anti-collision device includes the following steps: S1, sensor data acquisition, obtains real-time data of the vehicle and surrounding environment through multiple sensors; S2, inter-vehicle wireless communication ranging, using the directional antenna array communication module units installed at the front and rear of the vehicle to perform timed directional wireless signal communication to calculate the inter-vehicle distance and inter-vehicle relative speed; S3, Kalman filter processing, Kalman filtering is performed on the collected data and the inter-vehicle wireless communication ranging data to obtain an accurate state estimation of the vehicle and the obstacle; S4, collision prediction, predicting the collision risk between the vehicle and the obstacle based on the state estimation information output by the Kalman filter; S5, optimal control calculation, based on the collision prediction results and the current state of the vehicle, calculate the optimal acceleration or braking force to avoid collision; S6, emergency braking and warning, when the risk of collision is unavoidable, execute the optimal braking strategy and send a warning signal to the driver.

[0015] The present invention provides a workshop anti-collision device, which has the following beneficial effects: 1. The present invention adopts multi-sensor data acquisition and fusion technology to ensure comprehensive perception of the vehicle's surrounding environment. It obtains real-time data through multiple sensors at the same time, effectively avoiding the blind spot problem that may exist in a single sensor. Compared with the existing technology, this solution provides more accurate environmental information, solves the problem of insufficient perception of complex environments in traditional systems, and improves the accuracy of collision prediction.

[0016] 2. The present invention dynamically corrects sensor data through Kalman filtering processing technology to obtain more accurate vehicle and obstacle state estimation. This technology effectively reduces the impact of sensor errors on the system. Compared with the problem of inaccurate data processing in the prior art, the present invention can better support the decision-making of the collision prediction module and make the assessment of collision risk more reliable.

[0017] 3. The collision prediction module of the present invention can combine the precise status information of the vehicle and the obstacle to accurately predict the risk of collision. By calculating the relative speed, position and acceleration, the present invention can detect potential dangers earlier and accurately judge the timing of collision compared to the traditional simple collision warning system. This prediction capability effectively improves the reaction speed of the system and solves the problem of insufficient timely alarm response in the prior art.

[0018] 4. The present invention calculates accurate braking force through the optimal control algorithm, and can take rapid and efficient emergency braking when the risk of collision is inevitable. Compared with the problem of traditional technology relying only on fixed braking strategy, the present invention can adjust the braking force according to the real-time calculation results to ensure that the vehicle can minimize the collision damage, and at the same time send timely prompts to the driver through emergency warnings, thereby avoiding the situation where the driver cannot react in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the module structure of the device of the present invention; Figure 2 It is a schematic diagram of the sensor data acquisition module architecture of the present invention; Figure 3 This is a schematic diagram of the Kalman filter module architecture of the present invention; Figure 4 This is a schematic diagram of the collision prediction module framework of the present invention; Figure 5 It is a schematic diagram of the optimal control module architecture of the present invention; Figure 6 It is a schematic diagram of the emergency brake and warning module framework of the present invention; Figure 7 This is a schematic diagram of the workshop wireless communication ranging module architecture of the present invention; Figure 8 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the specification 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.

[0021] Please refer to the attached Figure 1 -Attached Figure 7 , an embodiment of the present invention provides a workshop anti-collision device, comprising: The inter-vehicle wireless communication ranging module is installed at the front and rear of the vehicle and is used to send and receive wireless signals in a timely and directional manner to calculate the inter-vehicle distance and inter-vehicle relative speed; Specifically, the vehicle-to-vehicle wireless communication ranging module is used to measure the distance and relative speed between vehicles. Through the directional antenna array wireless communication modules at the front and rear of the vehicle, the vehicle can achieve high-precision real-time measurement of vehicle-to-vehicle distance and relative speed, and incorporate this data into the entire collision avoidance system, providing basic data support for subsequent collision prediction and optimal control. In order to avoid interference from signals from vehicles beside, the directional antenna array is designed with clear directionality, so that it only communicates with the vehicles in front and behind, and avoids unnecessary communication interactions with other vehicles.

[0022] In the inter-vehicle wireless communication ranging module, the vehicle uses the directional antenna array wireless communication module to perform timed and directional signal transmission and reception. When two vehicles are within the set distance range, the modules will calculate the distance between the vehicles and the relative speed of the vehicles through directional wireless signal exchange, ensuring real-time and accurate distance and speed data, thereby supporting the core functions of the anti-collision system.

[0023] In this embodiment, the components of the vehicle wireless communication ranging module include: Directional antenna array communication module unit: The directional antenna array wireless communication module unit is installed at the front and rear of the vehicle and is used to send and receive wireless signals in a timely and directional manner. The unit uses standardized transmission signal power settings to ensure that a communication connection is established only when the two vehicles approach the set standard distance and the communication signal strength reaches a certain threshold. The directional antenna array has clear directionality, which effectively avoids signal interference from neighboring vehicles, thereby ensuring accurate communication ranging only with the vehicle in front or behind.

[0024] Signal transmission: Signals are transmitted and received bidirectionally and time-directedly between the directional antenna array wireless communication modules at the front and rear of the vehicle. Vehicles exchange distances and relative speeds in real time through wireless signals. The system can estimate the relative distance and speed between vehicles based on the propagation delay and signal strength of the wireless signals.

[0025] Distance between vehicles It can be calculated as follows: ; in: is the speed of light, It is the time difference between sending and receiving wireless signals.

[0026] The distance between vehicles can be accurately estimated through the time delay of signal propagation.

[0027] Through multiple distance measurement data, the relative speed of the vehicle It can be calculated as follows: ; in: and are the vehicle-to-vehicle distances during the two measurements; is the time interval between two measurements.

[0028] Standardized power control: To avoid mutual interference between multiple vehicles on the lane, the transmission power of the directional antenna array wireless communication module at the front and rear of each vehicle is set to standardized control. When two vehicles approach to a preset standard distance, the transmission power is automatically adjusted to ensure that the signal strength is sufficient for effective ranging without interfering with surrounding vehicles.

[0029] Signal analysis unit: The wireless signal will be processed by the signal analysis unit during the transmission and reception process. The analysis unit is responsible for converting the received wireless signal into usable signal characteristics, including signal propagation time, signal strength and other information. These signal characteristics are an important basis for calculating the distance and relative speed between vehicles.

[0030] Signal analysis: The signal analysis unit analyzes the received wireless signal through a decoding algorithm, extracts the propagation time and signal strength data, and through these data, the relative position change and relative speed between vehicles can be further calculated to ensure the accuracy of the communication data.

[0031] Utilization of timing information: The analysis unit not only analyzes a single signal, but also needs to use the timing data of multiple communication cycles to calculate a more accurate relative speed. This process is achieved by calculating the changes in signal propagation delay and signal strength.

[0032] A sensor data acquisition module is used to collect real-time data of the vehicle and its surroundings, including but not limited to vehicle position, speed, acceleration, obstacle position and speed information; Specifically, the workshop anti-collision device in this embodiment obtains real-time data of the vehicle and its surrounding environment through the sensor data acquisition module. The function of this module is to provide accurate environmental information for subsequent collision prediction, optimal control and emergency braking. The sensor data acquisition module is the core component of the vehicle-mounted anti-collision system and is closely connected with other modules. The collected sensor data is first processed and fused through the Kalman filter module to ensure the accuracy and real-time performance of subsequent calculations. Through these sensors, the system can monitor the distance, speed and other environmental factors between the vehicle and surrounding objects in real time, thereby providing data support for the decision-making of the entire anti-collision system.

[0033] In some embodiments, the sensor data acquisition module includes a radar unit, a lidar unit, a camera unit, an ultrasonic sensor unit and a GPS unit. Each unit is responsible for collecting a specific type of data, which will be aggregated and transmitted to the Kalman filter module for further processing. The technical implementation of each unit is described in detail below.

[0034] The radar unit detects the relative position and speed of surrounding objects by emitting electromagnetic waves. The radar can work under complex environmental conditions, such as low visibility and bad weather. The radar system generally includes a transmitting antenna, a receiving antenna and a signal processing module. The radar can accurately measure the relative distance and relative speed between the target object and the vehicle, and can quickly identify obstacles using the Doppler effect.

[0035] In a specific implementation, the measurement process of the radar unit is performed based on the following principles: Transmit electromagnetic waves and receive reflected waves; By calculating the time difference between the sent and received waves, the distance is obtained; The relative speed is calculated using the wave frequency deviation (Doppler effect).

[0036] Through these calculations, the radar unit is able to measure the position and speed of surrounding obstacles in real time and generate obstacle-related data to support subsequent collision prediction and control.

[0037] The working principle of the LiDAR unit is to scan the environment through a laser beam and measure the distance through reflected light. LiDAR can obtain very high-precision three-dimensional data, so it is very suitable for monitoring complex environments in workshops. The unit can provide an accurate environmental model to help the system identify static and dynamic obstacles.

[0038] In some embodiments, the lidar unit uses a single-line laser or a multi-line laser for scanning. By measuring the time of laser reflection, the three-dimensional coordinates of the target object can be accurately calculated. Within a certain range, the lidar has a high resolution and can detect tiny objects.

[0039] Specifically, the laser beam of the lidar unit will continuously scan the environment, and the resulting three-dimensional data point cloud will help the system accurately build an environmental model around the vehicle. This model is used to dynamically track obstacles, detect and predict their movement trajectories, and respond in advance before a collision occurs.

[0040] The camera unit is used to capture real-time video images in front of and around the vehicle. Through image recognition technology, the camera can identify pedestrians, other vehicles and other potential obstacles around it. Unlike radar and lidar, the advantage of the camera is that it can provide rich visual information, which is important for accurately identifying and classifying obstacles.

[0041] Specifically, the camera unit, combined with image processing technology, can extract features such as the shape, color, and texture of objects from video images, and then identify pedestrians, vehicles, and other obstacles. In some embodiments, the camera is also combined with a deep learning algorithm to analyze image data and identify potential risks in complex scenes.

[0042] As an option, the camera unit can achieve panoramic monitoring through a multi-camera layout to ensure that blind spots around the vehicle are effectively covered.

[0043] Ultrasonic sensor units are mainly used for close-range obstacle detection under low-speed driving conditions, and are particularly suitable for environmental monitoring when the vehicle is parking or reversing. Ultrasonic sensors measure the distance to obstacles by emitting sound waves and receiving reflected waves. Compared with radar and lidar, ultrasonic sensors have a shorter measurement range and are suitable for close-range obstacle detection.

[0044] In some embodiments, multiple ultrasonic sensors are installed on the front, back and sides of the vehicle. By continuously collecting data on the surrounding environment, the ultrasonic sensors can provide real-time distance between the vehicle and close obstacles. When it is detected that the obstacle is too close, the system will trigger an alarm or adjust the vehicle behavior.

[0045] The GPS unit is used to provide real-time positioning data for the vehicle. The GPS receiver receives satellite signals and calculates the vehicle's position. Usually, the GPS unit is combined with other sensors (such as radar and lidar) to provide the vehicle's precise position in the workshop, especially in complex workshop environments, to ensure that the system can accurately track the vehicle's position.

[0046] In some embodiments, the GPS unit can be used in conjunction with differential GPS (DGPS) technology to significantly improve positioning accuracy, which can reach centimeter level, providing high-precision geographic location information for collision prediction and control.

[0047] The data from all sensor units will be processed and transmitted in real time through the on-board computing platform. These data will first enter the Kalman filter module for fusion and denoising to generate an accurate state estimate of the vehicle and obstacles. Then, the processed data will be transmitted to the collision prediction module to assess the current collision risk. Finally, the optimal control module will calculate the control strategy based on the collision prediction results and adjust the vehicle's acceleration or braking measures.

[0048] Kalman filter module, used to process and fuse sensor data and inter-vehicle wireless communication ranging data, and to estimate the status of vehicles and obstacles in real time based on sensor data; Specifically, in the implementation of the workshop anti-collision device, the Kalman filter module, as an important processing unit, is responsible for the real-time estimation of the status of the vehicle and obstacles. This module filters, fuses and optimizes the sensor data to ensure that the subsequent collision prediction and control modules can make decisions based on accurate data. Since the vehicle is driving in a dynamic environment, the data of various sensors will be affected by noise and errors. Therefore, effective estimation and denoising of sensor data through Kalman filtering is the key to the successful operation of the system.

[0049] In this embodiment, the Kalman filter module accurately processes and fuses data from multiple sensors through the collaborative work of three sub-modules: the state estimation unit, the noise filtering unit, and the state transfer matrix, providing high-precision vehicle and obstacle status information for the workshop anti-collision device.

[0050] The state estimation unit is used to estimate the state of the vehicle and obstacles in real time based on the data from the on-board sensors through the Kalman filter algorithm. These states include key parameters such as position, speed, acceleration, etc. Specifically, the state estimation unit estimates the state of the vehicle and obstacles through the following steps: In this embodiment, the state estimation unit receives raw data from the sensor data acquisition module, including location information, speed information, etc. provided by radar, laser radar, camera, ultrasonic sensor, and GPS, etc. The state estimation unit uses these data as input and calculates the precise state estimation results of the vehicle and the obstacle through the Kalman filter algorithm. These results will be used for subsequent collision prediction and control decisions.

[0051] The mathematical model of Kalman filtering is as follows: ; ; in: The predicted value of the state of the vehicle and the obstacle; is the state transfer matrix; is the estimated value of the state in the previous step; is the control input matrix, which represents the influence of acceleration and other external control inputs on the system state; For control input (such as acceleration, braking, etc.); is the predicted error covariance matrix; is the covariance matrix of the state, indicating the credibility of the current state estimate; is the process noise covariance, which represents the error of the system model; Representation Matrix The transpose of .

[0052] The output of the state estimation unit is the precise position, velocity, acceleration and other state variables of the vehicle and obstacles. These estimated values ​​serve as the basis for subsequent processing.

[0053] Noise filtering unit: As a key part of the Kalman filter module, the noise filtering unit is mainly responsible for removing noise from the sensor signal to ensure the high quality of the system output data. There are inevitably noise sources in the sensor data, including measurement errors, external environmental interference, etc. The role of the noise filtering unit is to dynamically adjust and optimize through the Kalman filter.

[0054] In the specific implementation, the noise filtering unit optimizes the sensor data in the following ways: Prediction: Predict the state of the vehicle and obstacles based on the vehicle's motion model (such as speed, acceleration, etc.).

[0055] Update: The observation results of the current sensor are corrected through the Kalman gain to obtain the updated state estimate.

[0056] Through this process, the noise filtering unit can gradually reduce the error according to the changes in measurement error and process noise to achieve more accurate state estimation.

[0057] The basic steps of noise filtering include: ; ; in: is the Kalman gain, which represents the weight between measurement error and process noise; is the actual observation value of the sensor; is the measurement matrix, which represents the relationship between the sensor and the state; To measure the noise covariance, it represents the noise intensity of the sensor data; is the state estimate at the current moment, indicating the system state obtained after updating according to the Kalman filter; The transpose of the measurement matrix, often used to compute the covariance matrix adjustment during the update process.

[0058] This process ensures that the state estimation at each step is as close to the true value as possible and effectively reduces the noise in the data.

[0059] The state transfer matrix is ​​a key part of the Kalman filter process, which determines how the current state is related to the state of the previous moment. In the process of vehicle and obstacle state estimation, the state transfer matrix represents the relative motion between the vehicle and the obstacle.

[0060] In general, the state transfer matrix includes the influence of the time step and the vehicle dynamics model. In this embodiment, the state transfer matrix is ​​used to describe the movement of the vehicle in each time step and predict the state at the next moment. The definition of the state transfer matrix can be adjusted according to the vehicle's motion model. For example, if the acceleration of the vehicle is constant, the state transfer matrix is ​​in the form of: ; in: is the time step, which represents the time interval from one moment to the next; the state transfer matrix The relationship between the vehicle's speed, position and acceleration is modeled to predict the vehicle's state at the next moment.

[0061] Specifically, each item in the state transfer matrix corresponds to the relationship between the vehicle or obstacle states. The design of the matrix needs to fully consider the vehicle dynamics model, including factors such as speed, acceleration, and direction. These factors will affect the state prediction at each moment and ensure that the Kalman filter can make accurate state estimates based on historical data and current observations.

[0062] In this embodiment, the state estimation unit, the noise filtering unit and the state transfer matrix work together to ultimately output accurate state information of the vehicle and obstacles. The state estimation unit provides real-time state estimation of the vehicle and obstacles, the noise filtering unit ensures the high quality of the sensor data, and the state transfer matrix ensures that the future state of the vehicle can be correctly predicted in time.

[0063] Through the synergy of these units, the Kalman filter module can provide high-precision state estimation for the entire workshop collision avoidance device, thereby providing accurate input data for subsequent collision prediction, optimal control and emergency braking modules.

[0064] The collision prediction module is used to calculate the collision risk and make collision prediction based on the state estimation output by the Kalman filter module and the relative motion between the vehicle and the obstacle; Specifically, in the workflow of the workshop anti-collision device, the collision prediction module plays a vital role. Its main task is to predict potential collision risks by analyzing the relative motion between the vehicle and the obstacle. Through timely collision prediction, the system can take necessary preventive measures, such as adjusting the vehicle speed, activating the vehicle's emergency braking system, or avoiding collisions by other means. The efficiency of the collision prediction module directly affects the performance of the entire workshop anti-collision device.

[0065] In this embodiment, the collision prediction module uses the state estimation results from the sensor data acquisition module and the Kalman filter module to calculate the relative position, speed and acceleration between the vehicle and the obstacle, so as to determine whether there is a risk of collision. Based on the relative motion between the vehicle and the obstacle, combined with the vehicle's dynamic model, the module calculates the time and position of a possible collision in real time.

[0066] The collision risk assessment unit assesses the proximity of the vehicle and the obstacle in the future based on the current state data (position, speed, acceleration, etc.) and determines whether there is a risk of collision based on this information. In this embodiment, the collision risk assessment unit works based on the following steps: Relative motion analysis: First, the relative speed and acceleration between the vehicle and the obstacle are evaluated. Relative motion analysis calculates the relative speed between the two. To do: ; in: and are the time of vehicle and obstacle respectively The speed of time.

[0067] Calculation of expected distance: By calculating the current relative speed and acceleration, the collision risk assessment unit can predict the relative distance between the vehicle and the obstacle at a certain moment in the future. According to the motion model of the vehicle and the obstacle, the future relative distance can be calculated by the following formula: : ; in: and is the current position of the vehicle; and is the current position of the obstacle.

[0068] Collision risk judgment: When the predicted relative distance When the distance is less than the set safety distance threshold, the evaluation unit will determine the risk of collision. Specifically, if at some point in the future , the distance between the two will be less than the preset collision threshold , the system will consider the collision inevitable.

[0069] The path prediction unit further confirms the possibility of collision by calculating the future movement trajectory of the vehicle and the obstacle. The unit predicts the future movement path based on the vehicle's dynamic model and the movement trajectory of the obstacle, combined with the precise position and speed data provided by the Kalman filter module. The specific implementation method is as follows: Vehicle path prediction: The prediction of the vehicle path is based on the calculation of the current speed and acceleration. For example, when the vehicle is driving in a straight line, its path can be predicted by the following formula: ; in: is the current position of the vehicle; and are the speed and acceleration of the vehicle respectively.

[0070] Obstacle path prediction: Obstacle path prediction depends on the speed and acceleration of the obstacle. Through similar formulas, the future position of the obstacle can be calculated. If the behavior of the obstacle is more complex, a more complex motion model may need to be used, such as considering changes in steering angles or random disturbances.

[0071] By predicting the future paths of the vehicle and obstacles, the path prediction unit can predict whether the two will come into contact and provide data support for subsequent collision risk calculations.

[0072] The collision detection algorithm combines the results of the path prediction unit to evaluate the proximity in the future. If the trajectory of the vehicle and the obstacle intersect and the relative speed is too high, the collision detection algorithm will determine it as a high-risk collision. The core of the collision detection algorithm is the following steps: Track intersection calculation: Based on the predicted paths of the vehicle and the obstacle, calculate whether the tracks of the two intersect. If they do, further calculate the time and location of the intersection.

[0073] Collision time estimation: Predicts the exact moment of collision through time difference and relative speed. Specifically, if the distance between the vehicle and the obstacle is close to zero at a certain moment in the future, the system determines that the collision risk is high.

[0074] Collision risk level judgment: Evaluate the severity of the collision risk based on the time and location of the collision. The risk level is divided into three levels: high, medium, and low. The collision prediction module will decide whether to trigger emergency braking based on this level.

[0075] An optimal control module, used for calculating an optimal control strategy to adjust the acceleration or braking force of the vehicle to avoid collision according to the collision prediction result of the collision prediction module; Specifically, in the implementation process of the workshop anti-collision device, the optimal control module plays a vital role. Its main task is to calculate the optimal control input based on the collision risk information provided by the collision prediction module to ensure that the vehicle takes the most appropriate response measures when encountering potential collision risks. The optimal control module accurately calculates the control input so that the vehicle can avoid collision in the best way. The work of this module directly affects the reaction time, decision-making accuracy and safety of the anti-collision system.

[0076] In some embodiments, the optimal control module is composed of three main units, namely, a control input calculation unit, an acceleration optimization unit and a vehicle dynamic model. These units work together to calculate the optimal control strategy required for the vehicle in real time based on the output of the collision prediction module. The working principle of each unit and its technical implementation are described in detail below.

[0077] The control input calculation unit is responsible for calculating the optimal control input, especially acceleration and braking force, based on the collision risk assessment results provided by the collision prediction module. Its working principle is to determine the appropriate acceleration or braking force by real-time analysis of the time of collision, the relative speed, distance and acceleration between the vehicle and the obstacle, so that the vehicle can respond in the shortest time and avoid collision.

[0078] In this embodiment, the control input calculation unit performs calculation by the following steps: Obtaining collision risk assessment results: The unit first receives collision risk assessment information from the collision prediction module, including the estimated collision time, collision location, and risk level.

[0079] Calculate optimal control input: The control input calculation unit uses an optimization algorithm to calculate the most appropriate control input based on the collision risk assessment results. For example, the required acceleration or braking force is calculated by optimizing the objective function.

[0080] Specifically, the calculation of the control input can be achieved through the objective function in optimal control theory. The objective function is in the following form: ; in: is the distance between the vehicle and the obstacle; is the speed of the vehicle; is the acceleration of the vehicle; , , are weighting coefficients, used to balance the effects of distance, speed, and acceleration; The total time considered during the optimization process, expressed as the time from time 0 to All the moments in between.

[0081] By solving this objective function, the control input calculation unit can obtain the appropriate control strategy, that is, the acceleration or braking force that the vehicle should take in the next period of time to ensure that the vehicle can safely avoid collision.

[0082] The acceleration optimization unit further optimizes the vehicle's acceleration to ensure that the vehicle's movement remains smooth while avoiding collisions. The core goal of the acceleration optimization unit is to calculate the minimum acceleration value to ensure that the vehicle decelerates or stops in the shortest possible time while avoiding unnecessary over-acceleration or sudden braking.

[0083] Specifically, the vehicle's acceleration optimization is performed through the following steps: Minimize acceleration target: The acceleration optimization unit first sets a goal, which is to minimize the magnitude of acceleration while ensuring safety. This process not only considers avoiding collisions but also takes into account the comfort of the driver.

[0084] Constraint consideration: During the optimization process, the acceleration optimization unit needs to consider the physical limitations of the vehicle, such as maximum acceleration and braking capacity. For example, if the acceleration required at a certain moment exceeds the physical limit of the vehicle, the system will automatically adjust the calculation results to ensure that the vehicle is within the acceptable range.

[0085] Dynamically adjust acceleration: The acceleration optimization unit dynamically adjusts the vehicle's acceleration according to the optimal control calculation results, so that the vehicle can travel smoothly while avoiding collisions.

[0086] Through calculations by the acceleration optimization unit, the system can use the smallest acceleration possible to decelerate when a collision risk occurs, ensuring the shortest reaction time and reducing passenger discomfort.

[0087] The vehicle dynamics model is used to describe the physical motion characteristics of the vehicle, especially how the vehicle's motion state changes under control inputs (such as acceleration and braking). The vehicle's dynamic model is usually represented by a set of dynamic equations. Based on this model, the state changes of the vehicle under different control inputs can be accurately calculated.

[0088] In this embodiment, the basic form of the vehicle dynamics model is as follows: ; ; ; ; in: and are the lateral and longitudinal positions of the vehicle in meters; is the speed of the vehicle in meters per second; is the heading angle of the vehicle in radians; is the vehicle's acceleration in meters per second squared; is the air resistance coefficient, with the unit of 1 / (m·s 2 ); is the angular velocity of the vehicle in radians per second.

[0089] The vehicle dynamics model describes how the vehicle moves in space under given control inputs (such as acceleration and braking). Through this model, the vehicle's motion state at different time steps can be predicted, providing accurate calculation results for the optimal control module.

[0090] In addition, the vehicle dynamics model also takes into account factors such as air resistance and braking ability to ensure that the vehicle's movement behavior can be accurately simulated in complex environments.

[0091] The optimal control module can calculate the optimal control input required by the vehicle in real time through the close collaboration of the control input calculation unit, the acceleration optimization unit and the vehicle dynamic model, ensuring that the vehicle can effectively avoid collisions. The control input calculation unit calculates the preliminary control input based on the risk assessment results provided by the collision prediction module. The acceleration optimization unit further optimizes the control input to ensure smooth driving of the vehicle, and the vehicle dynamic model is used to predict the actual impact of the control input on the vehicle's motion.

[0092] Through the coordinated work of these units, the optimal control module can adjust the vehicle's motion state in time before a collision occurs to ensure safe driving, and take necessary countermeasures, such as emergency braking, when a collision is inevitable.

[0093] The emergency brake and warning module is used to perform emergency braking operations and warn the driver when the risk of collision is unavoidable.

[0094] Specifically, in the workshop anti-collision device, the emergency response module is mainly used to take emergency braking, warn the driver or other emergency operations under the coordinated action of the collision prediction module and the optimal control module, so as to ensure that the vehicle can respond quickly when the risk of collision occurs and effectively avoid the occurrence of accidents. The function of this module is to convert the results of collision prediction and optimal control into specific operating instructions to ensure the safety of the vehicle in emergency situations.

[0095] The emergency response module consists of two main units: the emergency brake control unit and the driver warning unit, which are responsible for executing emergency braking or warning the driver when the risk of collision is high so that timely action can be taken. The implementation of these two units will be described in detail below.

[0096] The emergency brake control unit is responsible for issuing braking commands when the risk of collision is high and unavoidable, quickly reducing the speed of the vehicle and trying to avoid or reduce the severity of the collision. The core goal of emergency brake control is to achieve the greatest possible deceleration in a short period of time through precise adjustment of acceleration and braking force.

[0097] In this embodiment, the emergency brake control unit calculates the required braking force based on the output of the collision prediction module (such as collision time, distance and risk level) through the following steps: Obtaining collision risk assessment results: The emergency brake control unit first receives the estimated time and location of the collision, as well as the level of collision risk, from the collision prediction module. If the risk level reaches a certain threshold, the unit enters emergency response mode.

[0098] Calculate braking requirements: The emergency brake control unit calculates the required braking intensity based on the vehicle's current speed, predicted collision time and distance. Specifically, if a collision is unavoidable, the required acceleration is calculated according to the following formula : ; in: is the current speed of the vehicle; is the relative distance between the vehicle and the obstacle.

[0099] Braking intensity regulation: Calculated braking acceleration As a brake control instruction, the braking force is adjusted by controlling the vehicle's braking system. The adjustment of the braking intensity is optimized according to the vehicle's physical limitations, such as maximum braking force and road conditions.

[0100] As an option, the emergency brake control unit can also simulate the braking effect in real time in conjunction with the vehicle dynamics model to ensure a smooth braking process and effective speed reduction in complex situations, avoiding adverse consequences caused by excessive braking, such as loss of vehicle control or passenger discomfort.

[0101] The driver warning unit is responsible for issuing timely warnings to the driver when the risk of collision is high, reminding him to take appropriate actions. This unit uses a variety of warning methods, such as sound, visual prompts or vibration feedback to remind the driver of the current dangerous situation.

[0102] In this embodiment, the driver warning unit is implemented as follows: Obtaining collision risk assessment results: The warning unit first receives the collision risk assessment information output by the collision prediction module, including collision time, relative distance and risk level. Based on the risk level, the warning unit determines the method and intensity of the warning.

[0103] Warning level judgment: Based on the collision time and risk level output by the collision prediction module, the warning unit judges the urgency of the warning. Specifically, if the predicted collision time is short and the risk level is high, the warning intensity will increase and various forms of warning methods will be used, such as: Visual warning: Eye-catching warning messages are displayed on the vehicle instrument panel or head-up display.

[0104] Sound warning: emits a continuous high-pitched warning sound to alert the driver of the current emergency situation.

[0105] Vibration warning: Vibrates the steering wheel or seat to alert the driver.

[0106] Warning response time: Under a certain risk threshold, the warning unit will start to issue warnings within a certain time window before the collision occurs. Usually, this time window is dynamically adjusted by the collision prediction module according to the relative distance and speed between the vehicle and the obstacle to ensure the effective communication of the warning information.

[0107] In some embodiments, the driver warning unit can be dynamically adjusted according to the driver's response behavior. For example, if the driver immediately brakes after receiving the warning, the warning unit may reduce the warning intensity. On the contrary, if the driver does not respond in time, the system may increase the warning intensity and prompt emergency braking.

[0108] The emergency response module provides multiple protections for the vehicle and driver when a collision risk occurs through close collaboration between the emergency brake control unit and the driver warning unit. The control unit calculates the braking force or braking acceleration required for the vehicle through the collision risk information provided by the collision prediction module, and implements it through the vehicle's braking system. The warning unit reminds the driver to take emergency response measures in a variety of ways to ensure that he can respond quickly in dangerous situations.

[0109] Through the joint work of these two units, the emergency response module can take emergency braking measures in a timely manner when a collision is inevitable, and effectively alert the driver to ensure the safety of the vehicle and avoid major accidents.

[0110] The anti-collision method of a workshop anti-collision device described below and the workshop anti-collision device described above can correspond to each other.

[0111] Please see attached Figure 8 , the anti-collision method of the workshop anti-collision device comprises the following steps: S1, sensor data acquisition, obtains real-time data of the vehicle and surrounding environment through multiple sensors; S2, inter-vehicle wireless communication ranging, using the directional antenna array communication modules installed at the front and rear of the vehicle to perform timed directional wireless signal communication to calculate the inter-vehicle distance and inter-vehicle relative speed; S3, Kalman filter processing, Kalman filtering is performed on the collected data and the inter-vehicle wireless communication ranging data to obtain an accurate state estimation of the vehicle and the obstacle; S4, collision prediction, predicting the collision risk between the vehicle and the obstacle based on the state estimation information output by the Kalman filter; S5, optimal control calculation, based on the collision prediction results and the current state of the vehicle, calculate the optimal acceleration or braking force to avoid collision; S6, emergency braking and warning, when the risk of collision is unavoidable, execute the optimal braking strategy and send a warning signal to the driver.

[0112] Specifically, S1, sensor data collection: In this step, the workshop anti-collision device collects information about the vehicle and its surrounding environment in real time through multiple sensor systems (such as lidar, camera, ultrasonic sensor, etc.). The sensor can not only provide dynamic parameters such as the vehicle's current position, speed, acceleration, but also obtain information such as the position, shape, and motion status of obstacles. The working principle and deployment method of the sensor can be selected according to actual conditions to ensure the accuracy and real-time nature of data collection.

[0113] S2. Use the directional antenna array wireless communication modules installed at the front and rear of the vehicle to perform timed directional wireless signal communication. When two vehicles approach each other to the set standard distance, a communication connection will be established between the wireless communication modules to exchange signals and calculate the distance and relative speed between vehicles. The directional antenna array design avoids interference from surrounding vehicles, improves ranging accuracy, and calculates the propagation delay and intensity change of the signal to obtain the real-time vehicle distance and relative speed between vehicles.

[0114] S3, Kalman filter processing: In this step, the collected sensor data is processed by the Kalman filter algorithm to obtain an accurate state estimate of the vehicle and surrounding obstacles. Due to the measurement error and noise of the sensor itself, the original data may not be completely accurate, so Kalman filtering provides an effective recursive estimation method.

[0115] S4, Collision prediction: Based on the precise state estimation information output by the Kalman filter, the system evaluates the collision risk between the vehicle and the obstacle in real time through the collision prediction module. The core task of this step is to predict the movement trajectory of the vehicle and the obstacle in the future based on their relative speed, position, acceleration and other information, and determine whether there is a risk of collision.

[0116] S5, optimal control calculation: In this step, the system uses the optimal control algorithm to calculate the most appropriate acceleration or braking force based on the collision prediction results and the current state information of the vehicle to effectively avoid collision. The optimal control calculation determines the size and direction of the control input (such as acceleration, braking force) by optimizing the objective function to ensure that the vehicle can make the best response in the shortest time.

[0117] S6, emergency braking and warning: When the system determines that the risk of collision is unavoidable, the emergency braking and warning unit will be activated. In this step, the system will first immediately perform emergency braking operations based on the optimal control calculation results, adjust the braking force of the vehicle, and reduce the speed or stop it as much as possible before the collision occurs, thereby reducing the severity of the collision.

[0118] The method of this embodiment can be used to execute the above-mentioned device embodiment. Its principle and technical effect are similar and will not be described in detail here.

[0119] 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. Workshop anti-collision device, characterized in that: include: The inter-vehicle wireless communication ranging module is installed at the front and rear of the vehicle and is used to send and receive wireless signals in a timely and directional manner to calculate the inter-vehicle distance and inter-vehicle relative speed; A sensor data acquisition module is used to collect real-time data of the vehicle and its surroundings, including but not limited to vehicle position, speed, acceleration, obstacle position and speed information; A Kalman filter module is used to process and fuse the sensor data and the vehicle wireless communication ranging data, and to estimate the status of the vehicle and the obstacle in real time based on the sensor data; A collision prediction module, used to calculate the collision risk and perform collision prediction based on the state estimation output by the Kalman filter module and the relative motion between the vehicle and the obstacle; an optimal control module, for calculating an optimal control strategy to adjust the acceleration or braking force of the vehicle to avoid collision according to the collision prediction result of the collision prediction module; The emergency brake and warning module is used to perform emergency braking operations and warn the driver when the risk of collision is unavoidable.

2. The workshop anti-collision device according to claim 1, characterized in that: The sensor data acquisition module comprises: A radar unit to measure the relative distance and speed between the vehicle and obstacles; A LiDAR unit that detects the precise structure of the surrounding environment and provides three-dimensional spatial data; A camera unit for image recognition and providing position information of dynamic objects; Ultrasonic sensor unit for close-range obstacle detection at low speeds; The GPS unit is used to provide the vehicle's positioning information.

3. The workshop anti-collision device according to claim 1, characterized in that: The Kalman filter module includes: A state estimation unit, used to update the position information, speed information and acceleration information of the vehicle and obstacles in real time; A noise filtering unit, used to eliminate noise in sensor data and output accurate state estimation results; The state transfer matrix describes the relationship between the motion states of the vehicle and obstacles over time, and calculates the process noise covariance and measurement noise covariance.

4. The workshop anti-collision device according to claim 1, characterized in that: The collision prediction module comprises: A collision risk assessment unit, used to calculate a risk value of a future collision based on the relative speed, distance and acceleration information between the vehicle and the obstacle; The path prediction unit is used to predict the future movement trajectory of the vehicle and obstacles, and determine whether there is a potential collision risk based on the current driving status; The collision detection algorithm calculates the time and location of the expected collision based on the relative motion information between the vehicle and the obstacle.

5. The workshop anti-collision device according to claim 1, characterized in that: The optimal control module comprises: A control input calculation unit, used to calculate the optimal control input to avoid the collision based on the collision prediction result and the current state of the vehicle; The acceleration optimization unit is used to determine the optimal value of vehicle acceleration to ensure that the vehicle completes braking or avoidance operations in a short time; The vehicle dynamics model is used to establish the dynamics model of the vehicle under different control inputs, including the changing relationship between the vehicle acceleration, speed and position.

6. The workshop anti-collision device according to claim 1, characterized in that: The emergency brake and warning module comprises: The emergency brake control unit is used to execute the braking strategy calculated by the optimal control module and automatically adjust the vehicle's braking force when the collision risk reaches a set threshold; The driver warning unit is used to warn the driver through visual, audible and vibration means when a collision is unavoidable.

7. The workshop anti-collision device according to claim 1, characterized in that: The collision prediction module calculates the collision risk by the following steps: Get the current position and speed of the vehicle and the obstacle, and calculate the relative distance between them; Based on the acceleration and motion state of the vehicle and obstacle, predict the relative distance between the two in the future time period; Based on the expected distance and set thresholds, the risk of collision is assessed and an alarm is generated.

8. The workshop anti-collision device according to claim 1, characterized in that: The optimal control module calculates the optimal control strategy through the following steps: According to the output of the collision prediction module, the relative distance and motion trajectory between the vehicle and the obstacle are calculated; Evaluate the impact of different control inputs on collision outcomes through vehicle dynamics models; The optimal control inputs are calculated based on the objectives of minimizing the risk of collision and minimizing the response time.

9. The workshop anti-collision device according to claim 1, characterized in that: The workshop wireless communication ranging module includes: Directional antenna array communication module unit, used for wireless communication distance measurement between vehicles and calculation of relative speed between vehicles; The signal analysis unit is used to analyze the wireless communication signal and extract the signal characteristic parameters required for ranging.

10. An anti-collision method for a workshop anti-collision device, applied to the workshop anti-collision device according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1, sensor data acquisition, obtains real-time data of the vehicle and surrounding environment through multiple sensors; S2, inter-vehicle wireless communication ranging, using the directional antenna array communication module units installed at the front and rear of the vehicle to perform timed directional wireless signal communication to calculate the inter-vehicle distance and inter-vehicle relative speed; S3, Kalman filter processing, Kalman filtering is performed on the collected data and the inter-vehicle wireless communication ranging data to obtain an accurate state estimation of the vehicle and the obstacle; S4, collision prediction, predicting the collision risk between the vehicle and the obstacle based on the state estimation information output by the Kalman filter; S5, optimal control calculation, based on the collision prediction results and the current state of the vehicle, calculate the optimal acceleration or braking force to avoid collision; S6, emergency braking and warning, when the risk of collision is unavoidable, execute the optimal braking strategy and send a warning signal to the driver.

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