Mining vehicle anti-collision system based on millimeter wave radar and safe distance modeling method

Abstract

The invention relates to a mining vehicle anti-collision system based on a millimeter wave radar and a safe distance modeling method, belongs to the field of vehicle anti-collision systems, and aims to solve the problems of high collision accident rate, difficulty in dynamic target identification and poor adaptability of an existing system in an underground complex environment. According to the technical scheme, the system comprises a 77GHz millimeter wave radar detection module installed on the rear body of a vehicle; a digital signal processing circuit board integrated with a voltage-controlled oscillator; the OBD control box interacts with the CAN bus through a UART; and a safe distance estimation module containing a self-learning mechanism. The technical effects are that omnibearing environment perception, moving target accurate early warning and system adaptive optimization are realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of vehicle anti-collision system, and relates to a mine vehicle anti-collision system based on millimeter wave radar and a safety distance modeling method. BACKGROUND

[0002] With the rapid development of underground transportation system in coal mines, underground explosion-proof trackless rubber-tyred vehicles, as key transportation equipment, have become an indispensable part of coal mine production. However, the underground working environment is complex and changeable, and there are many safety hazards, among which vehicle collision accidents are particularly prominent. The low visibility and dense lane bends in underground coal mines make vehicle and personnel safety problems seriously restrict the normal operation of underground working faces. Traditional anti-collision measures mainly rely on the experience and attention of the driver, but their limitations are obvious and they are difficult to effectively respond to the complex underground environment.

[0003] The current intelligent anti-collision system used by underground explosion-proof trackless rubber-tyred vehicles mainly includes infrared distance measuring sensors, personnel proximity switches, and image systems. These systems have significant defects: limited measurement range, susceptible to environmental influences, and insufficient obstacle positioning accuracy. Although the application of radar technology has improved driving safety, the existing vehicle-mounted millimeter wave radar anti-collision system still has obvious problems: the system can only receive sensor information and fails to integrate vehicle operating data, resulting in a single form of early warning and the inability to achieve variable early warning under complex working conditions.

[0004] Especially noteworthy is the reversing working condition: the existing anti-collision warning system can only provide early warning for static obstacles and cannot effectively analyze the collision risk of moving targets such as electric vehicles and pedestrians, making it difficult to provide early warning for drivers. For example, the millimeter wave radar anti-collision system disclosed in the existing patent CN107356925A can detect the relative distance and speed of obstacles in front of the vehicle, but it lacks accuracy in obstacle identification and classification; the anti-collision system proposed in patent CN114750775A has dynamic analysis capability, but the identification accuracy of dangerous targets still needs to be improved.

[0005] The main defects of the existing technology can be summarized as follows:

[0006] 1. The traditional underground anti-collision system relies too much on the subjective judgment of the driver, resulting in a collision accident rate of more than 15% in the reversing blind area;

[0007] 2. The existing sensor system has a small measurement range and weak environmental interference resistance, making it difficult to achieve all-around perception of underground trackless equipment;

[0008] 3. The system lacks vehicle information fusion mechanism, and the early warning function is single, which cannot adapt to the changing environment in underground mines;

[0009] 4. There is a lack of dynamic anti-collision analysis capability for moving targets, which cannot effectively warn pedestrians, electric vehicles, and other moving obstacles.

[0010] 5. The obstacle recognition algorithm is not accurate enough, and the signal processing precision or multi-sensor fusion capability needs to be improved.

[0011] Therefore, it is urgent to develop a collision avoidance system fusing millimeter wave radar detection and vehicle operation data, to solve the dynamic collision warning problem under complex working conditions in the mine through an innovative safety distance modeling method. SUMMARY

[0012] In the prior art, the underground collision avoidance system relies on the experience and attention of the driver, resulting in a high collision accident rate in the reversing blind area, and the existing millimeter wave radar collision avoidance system has the problems of small measurement range, great influence of environment, and poor obstacle positioning capability. Therefore, the present application provides a mine vehicle collision avoidance system based on millimeter wave radar and a safety distance modeling method.

[0013] The purpose of the present application is to overcome the shortcomings in the prior art, and to provide a mine vehicle collision avoidance system based on millimeter wave radar and a safety distance modeling method. The system comprises a millimeter wave radar sensor, a digital signal processing circuit board, an OBD control box, a vehicle CAN bus, a vehicle body braking system and a safety distance estimation module.

[0014] The millimeter wave radar sensor adopts a 77GHz millimeter wave radar with an accuracy of 0.5°, is installed at the left and right corners of the rear body of the vehicle, and is at an angle of 60-70° to the forward direction of the vehicle, with an elevation angle of 85-90° in the vertical direction and a distance from the ground height of 500-600mm. The millimeter wave radar sensor improves the signal-to-noise ratio (SNR) of the system by 12dB through a multipath interference suppression algorithm, significantly improving the accuracy of obstacle recognition.

[0015] The output end of the digital signal processing circuit board is a TTL interface, which has the advantages of high speed and low power consumption, and is internally integrated with a voltage-controlled oscillator, a mixer, an amplifier, a loop filter and an antenna. The digital signal processing circuit board, the voltage-controlled oscillator, the mixer, the amplifier, the loop filter and the antenna are all placed in the shell, and signal processing and control are performed through the OBD control box.

[0016] The OBD control box has a UART interface, a CAN interface and a RS-485 redundant communication channel; when the bit error rate BER of the CAN bus is greater than 10 CAN >10 -4The automatic switching protocol is switched with a delay of less than 50 ms, wherein the UART interface is used for communication with the millimeter wave radar sensor, and the CAN interface is used for communication with the vehicle body braking system and the safety distance estimation module. The output end of the OBD control box is a UART interface, the timing requirements of the communication parties are not strict, so different devices can be easily combined, the communication signal is stable, and the communication speed is fast. The input end and the interactive end of the OBD control box are CAN interfaces, and the CAN interface has strong anti-interference ability when processing complex information.

[0017] The safety distance estimation module comprises a preceding vehicle brake deceleration preset value submodule, a self-learning submodule and a road surface identification submodule. The preceding vehicle brake deceleration preset value submodule presets the brake deceleration parameters of different vehicles according to the vehicle type and the road type. The self-learning submodule corrects and adjusts the parameters through the evaluation results of the brake performance of the vehicle, including adjusting the brake deceleration of the vehicle, the driver response time and the brake system coordination time. The road surface identification submodule identifies the current driving road surface through the road surface identification sensor, and adjusts the brake parameters to adapt to different road conditions.

[0018] During the driving of the vehicle, the safety distance estimation module calculates the minimum safety distance d after eliminating the relative speed of the vehicle and the preceding vehicle in real time, adjusts the brake deceleration of the vehicle through the adjustment coefficient k, so that the measured value D is close to the estimated value Dh. At the same time, the driver response time Td preset value is adjusted through the evaluation results of the self-learning module on the driver response time. The brake system coordination time Tz preset value is adjusted through the evaluation results of the self-learning module on the brake system coordination time.

[0019] During the reversing of the vehicle, the system adopts a dynamic obstacle trajectory prediction model, combines the relative speed and the real-time vehicle speed information collected by the millimeter wave radar in real time, forms a closed-loop estimation model, adjusts the relative safety distance through the real-time adjustment of the brake deceleration parameter coefficient l of the vehicle, so that the relative distance d after eliminating the relative speed tends to be more reasonable. The k is adjusted and corrected through the road surface identification module, and the brake deceleration ab preset value of the vehicle and the preceding vehicle in the model is adjusted to match the current driving road surface.

[0020] The system realizes omnibearing anti-collision monitoring of the driving and reversing processes of the vehicle through the combination of various sensors and algorithms, can accurately identify and locate the moving target, realizes multivariable early warning, and improves the safety performance of the underground explosion-proof trackless rubber-tyred vehicle.

[0021] The beneficial effects of the present application are as follows:

[0022] 1. By optimizing the installation angle and height configuration of the millimeter wave radar, the detection coverage range of the complex underground environment is significantly expanded, the missing detection problem caused by the measurement blind area of the traditional system is overcome, and omnibearing monitoring of the roadway curve and low visibility area is realized.

[0023] 2. Innovatively fuse vehicle's own operation data with radar detection information, establish dynamic obstacle trajectory prediction mechanism, effectively identify moving pedestrian, vehicle and other target motion trend, solve the technical limitation that the existing system can only warn static obstacles.

[0024] 3. Adopt advanced multipath interference suppression algorithm, greatly improve the signal-to-noise ratio of obstacle identification, significantly reduce the influence of dust, moisture and other factors in the underground environment on the detection accuracy, and ensure stable operation under complex working conditions.

[0025] 4. Based on road surface state identification and parameter self-learning mechanism, dynamically adjust key parameters such as braking deceleration and driver response time, realize real-time optimization of safety distance model, and enhance the adaptability of the system to the variable working conditions in the mine.

[0026] 5. Through the vehicle bus, realize the deep cooperation of millimeter wave radar, braking system and control module, establish a hierarchical warning and braking response mechanism, and maximize the continuity of transportation operation under the premise of safety.

[0027] Other advantages, objects and features of the present application will be in part apparent and in part pointed out hereinafter in the specification, and it is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application, as claimed. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to make the objects, technical solutions and advantages of the present application clearer, the preferred detailed description of the present application will be combined with the drawings as follows, wherein:

[0029] Figure 1 is the overall structure block diagram of the system;

[0030] Figure 2 is the safety distance estimation module workflow diagram. DETAILED DESCRIPTION

[0031] The embodiments of the present application are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure of the present specification. The present application can also be implemented or applied through other different embodiments, and the details in the specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the diagrams provided in the following examples only illustrate the basic concept of the present application in a schematic manner, and the following examples and features in the examples can be combined with each other without conflict.

[0032] The drawings are only used for exemplary illustration, and the representation is only a schematic diagram, and cannot be understood as a limitation on the present application; in order to better illustrate the embodiments of the present application, some components in the drawings are omitted, enlarged or reduced, and do not represent the size of the actual product; it is understandable to those skilled in the art that some well-known structures in the drawings and their descriptions can be omitted.

[0033] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it should be understood that if the orientations or positional relationships indicated by the terms "upper", "lower", "left", "right", "front", "back" and the like are based on the orientations or positional relationships shown in the drawings, they are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore the terms describing the positional relationship in the drawings are only used for exemplary illustration, and cannot be understood as a limitation on the present application, and for those skilled in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0034] As shown in Figure 1 , the system of the present application comprises a millimeter wave radar sensor, a digital signal processing circuit board, an OBD control box, a vehicle CAN bus, a safety distance estimation module and a vehicle body braking system. The millimeter wave radar sensor is installed at the left and right corners of the rear body of the vehicle, and is connected to the input end of the digital signal processing circuit board through a radio frequency line; the digital signal processing circuit board is internally integrated with a voltage-controlled oscillator, a mixer, an amplifier and a loop filter, and its output end is connected to the UART interface of the OBD control box through a TTL interface; the CAN interface of the OBD control box is connected to the safety distance estimation module and the vehicle body braking system through the vehicle CAN bus. The safety distance estimation module comprises a front vehicle braking deceleration preset value submodule, a self-learning submodule and a road surface recognition submodule. The components are connected through the vehicle CAN bus to realize bidirectional data transmission, forming a closed-loop control system.

[0035] Figure 2 The core working logic of the safety distance estimation module is shown. The process starts with the millimeter wave radar sensor collecting real-time obstacle distance and speed information, and simultaneously acquiring the real-time vehicle speed; the obstacle classification link: calculate the millimeter wave radar point cloud density p (implementation value: p = 25 / m 2 ) and the motion variance s 2 (implementation value: s 2 = 0.6), when p < 30 and s 2 > 0.5, it is determined as a pedestrian, triggering the safety factor correction k new=k*1.5; subsequent according to target type into mode judgment; in driving mode, the minimum safety distance is calculated by using the safety distance formula; in reverse mode, the dynamic obstacle trajectory prediction model is started to generate trajectory equation, and the reverse safety distance is calculated; both modes are corrected through the parameter real-time adjustment link, combined with the self-learning sub-module to correct the driver response time and brake coordination time, and the road surface recognition sub-module synchronously outputs the road surface adhesion coefficient; finally, the body braking system is triggered through the hierarchical braking decision link.

[0036] Embodiment one

[0037] The application provides a mine vehicle anti-collision system based on a millimeter wave radar and a safety distance modeling method.

[0038] The millimeter wave radar sensor adopts a 77GHz millimeter wave radar, has an accuracy of 0.5°, is installed at the left and right corners of the rear body of the vehicle, is 65° to the advancing direction of the vehicle, has an elevation angle of 88° in the vertical direction, and has a distance from the ground height of 550mm; and a dynamic calibration module is activated to monitor the installation angle offset in real time, and when the offset exceeds ±3°, the detection error is compensated through the formula Δd=h*tan(θ offset ), wherein h=550mm. The millimeter wave radar sensor improves the signal noise ratio (SNR) of the system by 12dB through a multipath interference suppression algorithm, and significantly improves the accuracy of obstacle identification.

[0039] The output end of the digital signal processing circuit board is a TTL interface, has the advantages of high speed and low power consumption, and is internally integrated with a voltage-controlled oscillator, a mixer, an amplifier, a loop filter and an antenna. The digital signal processing circuit board, the voltage-controlled oscillator, the mixer, the amplifier, the loop filter and the antenna are all placed in the shell and are subjected to signal processing and control through the OBD control box.

[0040] The OBD control box is integrated with a UART interface, a CAN interface and an RS-485 redundancy channel. When the bit error rate BER CAN =1.2*10 -4 of the CAN bus exceeds the threshold 10 -4) time, the RS-485 protocol is switched to transmit data within 48 ms, wherein the UART interface is used for communication with the millimeter wave radar sensor, and the CAN interface is used for communication with the vehicle body braking system and the safety distance estimation module. The output end of the OBD control box is a UART interface, which has no strict timing requirements for both parties in communication, so that different devices can be easily combined, the communication signal is stable, and the communication speed is fast. The input end and the interactive end of the OBD control box are both CAN interfaces, which have strong anti-interference ability in processing complex information.

[0041] The safety distance estimation module includes a front vehicle braking deceleration preset value submodule, a self-learning submodule, and a road surface recognition submodule. The front vehicle braking deceleration preset value submodule presets the braking deceleration parameters of different vehicles according to the vehicle type and the road type. The self-learning submodule corrects and adjusts the parameters through the evaluation results of the braking performance of the vehicle, including adjusting the braking deceleration of the vehicle, the driver response time, and the braking system coordination time. The road surface recognition submodule identifies the current driving road surface through the road surface recognition sensor and adjusts the braking parameters to adapt to different road conditions.

[0042] During the driving of the vehicle, the safety distance estimation module calculates the minimum safety distance d after eliminating the relative speed of the vehicle and the front vehicle in real time, adjusts the braking deceleration of the vehicle through the adjustment coefficient k, and makes the measured value D close to the estimated value D h . At the same time, the driver response time T d preset value is adjusted through the evaluation results of the self-learning module on the driver response time. The braking system coordination time T z preset value is adjusted through the evaluation results of the self-learning module on the braking system coordination time.

[0043] During the reversing of the vehicle, the system adopts a dynamic obstacle trajectory prediction model, combines the relative vehicle speed and the real-time vehicle speed information collected by the millimeter wave radar in real time, forms a closed-loop estimation model, adjusts the relative safety distance through real-time adjustment of the braking deceleration parameter coefficient l of the vehicle, and makes the relative distance d after eliminating the relative speed more reasonable. The k is adjusted and corrected through the road surface recognition module, and the braking deceleration a b preset value of the vehicle and the front vehicle in the model is adjusted to match the current driving road surface.

[0044] The system realizes omnidirectional anti-collision monitoring of the driving and reversing processes of the vehicle through the combination of various sensors and algorithms, can accurately identify and locate moving targets, realizes multivariable early warning, and improves the safety performance of the underground explosion-proof trackless rubber-tyred vehicle.

[0045] Example Two

[0046] The application provides a millimeter wave radar-based mine vehicle anti-collision system and a safety distance modeling method.

[0047] The millimeter wave radar sensor adopts a 77GHz millimeter wave radar, has a precision of 0.5°, is installed at the left and right corners of the rear body of the vehicle, is 70° to the advancing direction of the vehicle, has an elevation angle of 89° in the vertical direction, and is 580mm from the ground height; and a dynamic calibration module is activated to monitor the installation angle offset in real time, and when the offset exceeds ±3°, the detection error is compensated through the formula Δd=h*tan(θ offset ), wherein h=580mm. The millimeter wave radar sensor improves the signal-to-noise ratio (SNR) of the system by 12dB through a multipath interference suppression algorithm, and significantly improves the accuracy of obstacle identification.

[0048] The output end of the digital signal processing circuit board is a TTL interface, has the advantages of high speed and low power consumption, and is internally integrated with a voltage-controlled oscillator, a mixer, an amplifier, a loop filter and an antenna. The digital signal processing circuit board, the voltage-controlled oscillator, the mixer, the amplifier, the loop filter and the antenna are all placed in the shell and are subjected to signal processing and control through the OBD control box.

[0049] The OBD control box has a UART interface and a CAN interface, wherein the UART interface is used for communication with the millimeter wave radar sensor, and the CAN interface is used for communication with the vehicle body braking system and the safety distance estimation module. The output end of the OBD control box is a UART interface, the interface has no strict timing requirements for both parties of communication, so different devices can be easily combined, the communication signal is stable, and the communication speed is fast. The input end and the interactive end of the OBD control box are both CAN interfaces, the interface has strong anti-interference performance in processing complex information.

[0050] The safety distance estimation module includes a front vehicle braking deceleration preset value submodule, a self-learning submodule and a road surface identification submodule. The front vehicle braking deceleration preset value submodule presets the braking deceleration parameters of different vehicles according to the vehicle type and the road type. The self-learning submodule corrects and adjusts the parameters through the braking performance evaluation results of the vehicle, including adjusting the vehicle braking deceleration, the driver response time and the braking system coordination time. The road surface identification submodule identifies the current driving road surface through the road surface identification sensor, and adjusts the braking parameters to adapt to different road conditions.

[0051] During the driving of the vehicle, the safety distance estimation module calculates the minimum safety distance d after eliminating the relative speed of the vehicle and the front vehicle in real time, adjusts the vehicle braking deceleration through the adjustment coefficient k, and makes the measured value D close to the estimated value D hMeanwhile, the self-learning module adjusts the driver response time T d preset value. The self-learning module adjusts the brake system coordination time T z preset value.

[0052] In the process of reversing the vehicle, the system adopts a dynamic obstacle trajectory prediction model, combines the relative speed collected by the millimeter wave radar in real time and the real-time vehicle speed information of the vehicle, forms a closed-loop prediction model, adjusts the relative safety distance by adjusting the vehicle braking deceleration parameter coefficient l in real time, so that the relative distance d after the relative speed is eliminated is more reasonable. Adjust k through the road identification module to correct the braking deceleration a b preset value to match the current driving road surface.

[0053] The system realizes omnidirectional anti-collision monitoring of vehicle driving and reversing process through the combination of various sensors and algorithms, can accurately identify and locate moving targets, realizes multivariable warning, and improves the safety performance of the underground explosion-proof trackless rubber-tyred vehicle.

[0054] Example three

[0055] 1. When the vehicle is driving in a straight line, the 77GHz millimeter wave radar installed on both sides of the rear body detects the front obstacles at a horizontal angle of 65 degrees;

[0056] 2. The digital signal processing circuit board mixes and filters the echo signal to extract the relative speed of the obstacle 2.5m / s and the distance 15m;

[0057] 3. The OBD control box obtains the vehicle speed 30km / h through the CAN bus, and the safety distance estimation module starts the driving mode calculation;

[0058] 4. The self-learning sub-module corrects the driver response time to 1.2 seconds according to the historical braking data;

[0059] 5. The road identification sub-module detects the wet road adhesion coefficient 0.4, and outputs the braking deceleration 3.2m / s 2 ;

[0060] 6. Calculate the minimum safety distance 8.3m according to the formula, the actual distance 15m is greater than the safety distance, and the system remains in the monitoring state.

[0061] Example four

[0062] 1. When the vehicle is reversing, the millimeter wave radar detects a pedestrian moving horizontally to the right rear, with a relative speed of 1.8m / s;

[0063] 2. The safety distance estimation module starts the dynamic obstacle trajectory prediction:

[0064] Establish the trajectory equation:

[0065] Among them, a bx = -0.3m / s 2 (lateral acceleration of this vehicle), a by =0.5m / s 2 (Longitudinal acceleration of this vehicle) is provided by the onboard inertial measurement unit (IMU);

[0066] Initial positions: x0 = 2.1m, y0 = 5.3m

[0067] velocity component v rx =0.6m / s, v ry =1.7m / s

[0068] 3. Based on the vehicle's reversing speed of 1.5 m / s, the calculated collision time threshold is 4.2 seconds;

[0069] 4. The road surface recognition submodule detects gravel roads, and the dynamic adjustment coefficient l is set to 0.9;

[0070] 5. The minimum safe distance calculated according to the reversing formula is 3.8m, but the actual distance is 5.3m, triggering a level one audible warning.

[0071] 6. When a pedestrian continues to approach to within 3.5m, the system initiates graded braking to reduce speed by 30%.

[0072] Example 5

[0073] 1. When the vehicle enters the underground bend, the millimeter-wave radar detects a distance of 3.2m from the tunnel wall;

[0074] 2. The safe distance estimation module simultaneously acquires the steering wheel angle information at 45 degrees;

[0075] 3. The self-learning submodule corrects the braking coordination time to 0.8 seconds based on historical cornering data;

[0076] 4. The road surface recognition submodule detects waterlogged roads and adjusts the braking deceleration to 2.8 m / s. 2 ;

[0077] 5. The dynamic adjustment coefficient k is adaptively adjusted to 1.1 based on the steering angle;

[0078] 6. Real-time calculation of the lateral safety distance is 2.9m, and the system triggers the steering assist torque limit;

[0079] 7. When the actual distance approaches 2.8m, the formula is used:

[0080] Calculate the required braking force (m = 1500 kg, v) r= 2.5 m / s), the vehicle body brake system is activated according to F b and the single-wheel brake is activated.

[0081] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the purpose and scope of the technical solutions, and they should all be covered in the scope of the claims of the present application.

Claims

1. A millimeter wave radar based collision avoidance system for mining vehicles, characterized in that: The application relates to a millimeter wave radar system for vehicle safety distance estimation. The millimeter wave radar sensor adopts 77GHz millimeter wave radar, is installed at the left and right corners of the rear body of the vehicle, has an angle of 60°-70° with the advancing direction of the vehicle, has an upward angle of 85°-90° in the vertical direction, and has a distance from the ground of 500-600mm; and is integrated with a dynamic calibration module to monitor the installation angle deviation in real time, the angle deviation threshold is ±3°, and the detection error is automatically corrected through a compensation formula Δd=h*tan(θ offset ), wherein h is the radar height, and θ offset is the angle deviation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. The safety distance estimation module is connected with the OBD control box through the vehicle CAN bus, and comprises a front vehicle braking deceleration preset value submodule, a self-learning submodule, a road surface recognition submodule, and an obstacle classification submodule; the submodules are based on millimeter wave radar point cloud density p motion variance 2 Differentiate target types, i.e. pedestrians, vehicles, and fixed objects; and dynamically adjust the safety coefficient: k x 1.5 for pedestrians, k x 0.8 for vehicles, and k x 1.0 for fixed objects, with a classification threshold set as p < 30 / m 2 and σ 2 > 0.5 to determine a pedestrian; The vehicle body brake system is connected with the OBD control box through the vehicle CAN bus, and integrates a brake pressure prediction model, and dynamically distributes brake force according to a safety distance d and a vehicle mass m: Where d0=0.5m is a brake buffer threshold, F max is the maximum brake force of the system.

2. The millimeter wave radar-based collision avoidance system for mining vehicles of claim 1, wherein: The application relates to a millimeter wave radar system for vehicle safety distance estimation.

3. The millimeter wave radar-based collision avoidance system for mining vehicles of claim 1, wherein: The application relates to a millimeter wave radar system for vehicle safety distance estimation.

4. The millimeter wave radar-based collision avoidance system for mining vehicles of claim 1, wherein: The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. The self-learning sub-module dynamically corrects the braking deceleration a of the vehicle according to the braking performance evaluation result of the vehicle b , driver response time T d and braking system coordination time T z ; The application relates to a millimeter wave radar system for vehicle safety distance estimation.

5. The millimeter wave radar-based collision avoidance system for mining vehicles of claim 1, wherein: The application relates to a millimeter wave radar system for vehicle safety distance estimation. Wherein, v r is the relative speed of the host vehicle and the preceding vehicle; T d is the driver response time; T z is the braking system coordination time; a b is the host vehicle braking deceleration; k is the braking deceleration adjustment coefficient, 0.8≤k≤1.

2.

6. The millimeter wave radar-based collision avoidance system for mining vehicles of claim 1, wherein: The application relates to a millimeter wave radar system for vehicle safety distance estimation. a) collecting in real time the relative speed v of the obstacle by means of a millimeter wave radar r and the vehicle speed v0 of the own vehicle; The application relates to a millimeter wave radar system for vehicle safety distance estimation. wherein (x0, y0) is the initial position, (v rx x, v ry y) is the relative velocity component; a bx x, a by y) is the real-time acceleration component of the host vehicle, provided by the vehicle-mounted inertial measurement unit (IMU); x t represents the lateral coordinate of the obstacle at time t, with the origin of the coordinate system being the center of the millimeter wave radar, and the x-axis being parallel to the width direction of the vehicle; y t represents the longitudinal coordinate of the obstacle at time t, with the y-axis pointing to the rear of the vehicle; v rx represents the lateral velocity component of the obstacle relative to the host vehicle, with a positive value indicating movement to the right; v ry represents the longitudinal velocity component of the obstacle relative to the host vehicle, with a positive value indicating movement away from the host vehicle; t represents the cumulative time since the obstacle was first detected. The application relates to a millimeter wave radar system for vehicle safety distance estimation. Wherein, l is a dynamic adjustment coefficient, 0.7≤l≤1.5; v0 represents the absolute value of the speed of the vehicle in reverse; a b represents the deceleration of the vehicle in reverse; The application relates to a millimeter wave radar system for vehicle safety distance estimation. Wherein, l is a dynamic adjustment coefficient, 0.7≤l≤1.5; v0 represents the absolute value of the speed of the vehicle in reverse; a b represents the deceleration of the vehicle in reverse.

7. The millimeter wave radar-based collision avoidance system for mining vehicles of claim 1, wherein: The application relates to a millimeter wave radar system for vehicle safety distance estimation.

8. A safety distance modeling method based on the system of any one of claims 1-7, characterized by: The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. wherein v r is the relative speed between the host vehicle and the preceding vehicle; T d is the driver response time; T z is the braking system coordination time; a b is the host vehicle braking deceleration; k is a braking deceleration adjustment coefficient, 0.8≤k≤1.2; The application relates to a millimeter wave radar system for vehicle safety distance estimation. a) collecting in real time the relative speed v of the obstacle by means of a millimeter wave radar r and the vehicle speed v0 of the own vehicle; The application relates to a millimeter wave radar system for vehicle safety distance estimation. wherein (x0, y0) is the initial position, (v rx ,v ry ) is the relative speed component; x t represents the lateral coordinate of the obstacle at time t, with the origin of the coordinate system being the center of the millimeter wave radar, and the x-axis being parallel to the width direction of the vehicle; y t represents the longitudinal coordinate of the obstacle at time t, with the y-axis pointing to the rear of the vehicle; v rx represents the lateral speed component of the obstacle relative to the host vehicle, with a positive value indicating rightward motion; v ry represents the longitudinal speed component of the obstacle relative to the host vehicle, with a positive value indicating movement away from the host vehicle; and t represents the cumulative time since the obstacle was first detected. The application relates to a millimeter wave radar system for vehicle safety distance estimation. Wherein, l is a dynamic adjustment coefficient, 0.7≤l≤1.5; v0 represents the absolute value of the speed of the vehicle in reverse; a b represents the deceleration of the vehicle in reverse; The application relates to a millimeter wave radar system for vehicle safety distance estimation.

9. The safety distance modeling method of claim 8, wherein: In S4, the self-learning sub-module dynamically corrects the preset value of T d and T z by analyzing historical braking data, and the correction formula is: T' d = T d · (1 + a · At) T' = T + α * Δt d T' = T + α * Δt where T' is the corrected response time; α is a learning rate factor, 0.01 ≤ α ≤ 0.05; and Δt is the deviation of the actual response time from the preset value.

10. The safety distance modeling method of claim 8, wherein: In S4, the road surface identification submodule adjusts the braking deceleration a according to the road surface adhesion coefficient μ b : a b = μ·g·β The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. The application relates to a millimeter wave radar system for vehicle safety distance estimation. 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