A tunnel traffic environment under the patrol inspection equipment anti-collision early warning and safety guidance system

Abstract

The application relates to the technical field of traffic control systems and discloses a tunnel traffic environment-based patrol equipment anti-collision early warning and safety guiding system, which comprises the following: a coordinate mapping module which converts social vehicle image coordinates into tunnel mileage positions by using a mapping matrix; a parameter self-calibration module which corrects mapping matrix parameters online by using patrol equipment self-positioning data, so that parameter drift caused by tunnel environment interference is eliminated; a collision risk calculation module which calculates residual collision time; a risk avoidance decision module which generates a forced track-changing instruction when the residual collision time is less than a safety threshold; and a guiding control module which drives the equipment to move transversely to a risk avoidance area. The application establishes a closed-loop mechanism for reverse calibration of a monitoring end by using a controlled object, solves the problem of sensing system precision attenuation caused by tunnel environment vibration, actively constructs a downstream operation gap by using upstream speed limiting, and realizes safe cooperation between patrol operation and traffic passing.

Description

Technical Field

[0001] This invention relates to a collision avoidance warning and safety guidance system for inspection equipment in a tunnel traffic environment, belonging to the field of traffic control technology. Background Technology

[0002] Current highway tunnel operation and maintenance systems often utilize mobile devices equipped with vision or lidar sensors to replace manual inspections. These devices operate at low speeds in driving lanes or maintenance areas, performing tasks such as pavement defect identification, environmental parameter monitoring, and early fire warning. Existing mainstream solutions mostly follow the autonomous obstacle avoidance logic of robots in unrestricted environments. They rely on the device's own sensors to detect the surrounding environment. When an obstacle or other vehicles approach, the control unit calculates a local path and performs braking or detour to avoid it. This places a passive obstacle avoidance mechanism based on individual perception into the closed, narrow, and high-speed traffic environment of tunnels. In actual engineering applications, the contradiction between safety and efficiency has been exposed. The unique characteristics of tunnels, such as sudden changes in lighting at the entrance, large curvature curves, and long downhill sections, limit the driver's visual adaptation ability and the vehicle's physical braking distance.

[0003] Existing improvement approaches are limited to hardware architecture repairs of inspection equipment, neglecting the systemic deficiencies in control logic. For example, Chinese patent CN307904156S discloses a collision warning device that integrates a tall communication antenna and a large-size audio-visual warning module. While enhancing the physical presence of the device increases its detection probability, this approach of strengthening individual warning features remains essentially passive defense. The device lacks deep interaction capabilities with the tunnel environment and does not have the authority to actively allocate right-of-way to vehicles approaching from upstream. When faced with high-speed approaching vehicles, isolated hardware cannot overcome physical blind spots, and warning triggering lags behind the driver's reaction time, leading to a risk of collision upon sight. When vehicles enter the device's limited visibility area at high speed, existing individual perception modes face multiple bottlenecks: the physical detection range of the device's sensors is limited, especially at high speeds. Approaching vehicles, the time window from threat perception to action is shorter than the braking time required for the vehicle to complete physical deceleration, leading to the risk of collision upon perception. The common "stop upon encountering a vehicle" logic turns the equipment into a stationary obstacle in the driving lane at the moment of danger, resulting in passive static defense that may induce rear-end collisions or chain collisions. Treating inspection equipment as an independent traffic participant lacks deep integration with roadside monitoring facilities and traffic flow control systems, making it impossible to use the existing beyond-line-of-sight perception network in tunnels for predictive right-of-way allocation. Simply piling up high-precision vehicle-mounted sensors or increasing edge computing power is limited by physical line-of-sight obstruction and tunnel geometry, failing to address safety hazards in blind spots, leading to a significant increase in hardware costs. The existing technology system lacks a solution that integrates inspection equipment into the macro-level traffic control logic and utilizes external spatiotemporal information to achieve deterministic isolation and operational continuity assurance.

[0004] Therefore, the technical problem to be solved by this invention is how to establish a roadside collaborative deterministic collision avoidance warning and guidance mechanism that breaks through the limitations of single-unit perception and ensures the physical isolation and continuity of inspection operations while ensuring the safe passage of high-speed traffic. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A collision avoidance warning and safety guidance system for inspection equipment in a tunnel traffic environment, comprising:

[0006] The coordinate mapping module is used to acquire the coordinates of social vehicle images collected by roadside cameras and the self-positioning mileage data uploaded by inspection equipment, and to map the social vehicle image coordinates to the tunnel mileage location using a pixel-geographic coordinate mapping matrix.

[0007] The parameter self-calibration module is used to simultaneously collect the self-positioning mileage data uploaded by the inspection equipment and the coordinates of the center point of the equipment image captured by the roadside camera when the inspection equipment enters the field of view of the roadside camera. It constructs a verification set containing multiple sets of mileage data and image coordinates, calculates the positioning deviation generated by the current mapping matrix, and uses the verification set to correct the mapping matrix parameters of the roadside camera online when the positioning deviation exceeds a preset threshold, until the positioning deviation converges.

[0008] The collision risk calculation module is used to calculate the remaining collision time for social vehicles to reach the current mileage position of the inspection equipment based on the corrected mapping matrix.

[0009] The risk avoidance decision module is used to compare the remaining collision time with a preset safety threshold, and generate a forced track-cutting command when the remaining collision time is less than the safety threshold.

[0010] The guidance control module is used to respond to the forced track cutting command, drive the inspection equipment to move laterally from the working position on the roadway to the danger zone on the tunnel sidewall and stay there until a conflict resolution signal is generated.

[0011] Preferably, the convergence condition for the online correction performed by the parameter self-calibration module follows the minimization of the reprojection error criterion; the reprojection error criterion is used to calculate the positioning residual. ,in ,in The self-positioning mileage data fed back by the inspection equipment serves as a benchmark. Let M be the image coordinates captured by the roadside camera, f be the mapping matrix to be corrected, and f be the mapping function based on the camera calibration model. The parameter self-calibration module iteratively adjusts M until... Less than the preset threshold.

[0012] Preferably, the guidance control module further includes a traffic flow gap control unit, which is used to acquire the traffic flow density and average flow speed in a preset area upstream of the inspection equipment, calculate the headway increment required to generate the target moving gap based on the traffic flow density, and generate an alternating speed limit sequence containing a first speed limit value and a second speed limit value that alternately change at a preset period; the traffic flow gap control unit is also used to drive the traffic guidance screen located upstream of the inspection equipment to execute the alternating speed limit sequence in order to build a low-density vehicle flow gap moving downstream in the traffic flow.

[0013] Preferably, the traffic flow gap control unit is also used to monitor the movement position of the low-density traffic flow gap relative to the inspection equipment in real time and generate a speed synchronization command; the speed synchronization command is used to adjust the operating speed of the inspection equipment so that the real-time position of the inspection equipment is kept within the spatiotemporal range of the low-density traffic flow gap, so as to realize the synchronous operation of the inspection equipment with the low-density traffic flow gap.

[0014] Preferably, the coordinate mapping module has a pre-set discrete track model for the tunnel; the discrete track model divides the physical space of the tunnel into working tracks corresponding to the center line of the driving lane and obstacle avoidance tracks corresponding to the tunnel sidewall area; the lateral movement of the inspection equipment driven by the guidance control module is a track state switching action based on the discrete track model, rather than an obstacle avoidance action based on continuous path planning.

[0015] Preferably, the risk avoidance decision module is also used to generate graded instructions based on the different time intervals in which the remaining collision time is located: when the remaining collision time is in the preset warning interval, a pre-switching instruction is generated to prompt the inspection equipment to interrupt the current detection task; when the remaining collision time is in the preset execution interval, a forced track-cutting instruction is generated to take over the motion control of the inspection equipment.

[0016] Preferably, the system also includes a communication status monitoring module, which is used to monitor the data link status between the inspection equipment and the coordinate mapping module in real time. When the data link is interrupted, the communication status monitoring module triggers the fault protection logic on the device side. The fault protection logic is used to forcibly lock the inspection equipment to operate in the danger avoidance area and prohibit it from entering the driving lane operation position.

[0017] Preferably, the coordinate mapping module is also used to access tunnel environment sensor data and dynamically adjust the preset safety threshold according to visibility data; when the visibility is lower than the preset standard, the preset safety threshold is increased to trigger the forced track cutting command in advance.

[0018] Preferably, the guidance control module is also used to send a warning trigger signal to the roadside information board located at a preset distance upstream of the inspection equipment while generating the forced track cutting command. The warning trigger signal is used to drive the roadside information board to display the operation prompt information ahead.

[0019] Preferably, the inspection equipment includes a positioning communication unit and a chassis control unit; the positioning communication unit is used to transmit self-positioning mileage data based on ultra-wideband positioning technology back to the coordinate mapping module in real time; the chassis control unit is used to receive and execute forced track cutting commands from the guidance control module, and the execution priority of the forced track cutting commands is higher than the task planning commands inside the inspection equipment.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. In the collision avoidance warning of inspection equipment, it does not rely on the inspection equipment's own sensors for probabilistic obstacle avoidance path planning at close range. Instead, it maps the physical cross-section of the tunnel into a discrete logical track containing both operational and avoidance states through the roadside system. Based on the remaining collision time of social vehicles arriving at the equipment, a right-of-way deprivation command is generated before the physical conflict occurs, driving the inspection equipment to switch to the avoidance track and remain stationary. Based on the discrete state switching logic of time threshold, it avoids the risk of equipment oscillation caused by the divergence or local minima of artificial potential field calculation in narrow and confined spaces. It ensures that there is a deterministic spatial isolation boundary between the inspection equipment and social vehicles when there are blind spots on curves or when the individual perception fails due to strong light interference.

[0022] 2. To address the inaccuracy of pixel-mileage mapping caused by minor changes in the physical pose of roadside cameras due to vibration and wind pressure during long-term tunnel operation, a reverse closed-loop correction mechanism is established. When the inspection equipment passes through the roadside perception field of view, the high-precision positioning true value fed back by the equipment and the coordinates of the image captured by the camera are collected simultaneously. The mapping residual between the two is calculated, and the external parameter matrix of the camera is updated in reverse iteration. The role of the inspection equipment is reused from the monitored object to the system's mobile calibration source. This does not interrupt traffic or increase manual calibration work. Existing communication and positioning data are used to continuously correct perception errors, ensuring the reliability of the remaining collision time calculation benchmark throughout the system's entire life cycle.

[0023] 3. For highly continuous inspection tasks, the traditional control strategy of stopping immediately upon encountering a vehicle or closing the entire road is changed. The compressibility of traffic flow is utilized to actively shape the flow. The required time window is calculated in reverse based on the equipment's operating position. The upstream variable information signs are driven to send alternating speed pulse sequences. The speed fluctuations propagate in the traffic flow, causing the time distance between the train head and the vehicle head to be stretched. This creates a low-density traffic gap that moves synchronously with the equipment when it arrives at the corresponding time and space section. This allows the inspection equipment to operate continuously within a dynamically maintained, interference-free window, avoiding task fragmentation caused by frequent track switching and eliminating the risk of congestion shock waves caused by forcibly cutting off upstream traffic flow. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the control architecture and data interaction of the anti-collision early warning system for inspection equipment of the present invention.

[0025] Figure 2This is a spatiotemporal evolution diagram of low-density gap modulation of traffic flow and synchronous operation of equipment according to the present invention.

[0026] Figure 3 This is a schematic diagram of the tunnel cross-section monitoring layout and the forced track-cutting avoidance action of the equipment in this invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to specific embodiments. It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of this application can be combined with each other. The numerical values, parameters and structures described in the following embodiments are only typical ways of explaining the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0028] This embodiment discloses a collision avoidance warning and safety guidance system for inspection equipment in a tunnel traffic environment. It includes a coordinate mapping module, a parameter self-calibration module, a collision risk calculation module, a risk avoidance decision module, and a guidance control module. These modules work collaboratively to achieve equipment safety protection based on asymmetric spatiotemporal right-of-way dynamic allocation within the confined space of a tunnel. The coordinate mapping module constructs a digital spatiotemporal reference for the tunnel space. This module acquires video stream data collected by roadside cameras and self-positioning data uploaded by the inspection equipment. It has a pre-set pixel-geographic coordinate mapping matrix to convert two-dimensional social vehicle image coordinates into one-dimensional tunnel mileage positions. To eliminate physical parameter drift caused by tunnel environmental vibrations, the system uses the parameter self-calibration module to perform closed-loop feedback correction. When the inspection equipment enters the field of view of the roadside camera, the parameter self-calibration module simultaneously collects two sets of heterogeneous data at the same time t: one set is the true self-positioning mileage value fed back by the inspection equipment through ultra-wideband (UWB) or a wheeled odometer. The other set consists of the coordinates of the center point of the equipment image captured by the roadside camera. A nonlinear parameter optimization procedure based on the Levenberg-Marquardt algorithm is constructed, and the system extracts N sets of synchronous heterogeneous feature point pairs in real time to form a verification set. The objective function is set as minimizing the sum of squared reprojection residuals. By calculating the Jacobian matrix J, the matrix parameter M is driven to iterate in the negative gradient direction, and a confidence weight factor is introduced. Suppressing environmental noise, among which Based on the high-frequency vibration gradient experiment of the tunnel, the value is taken as 0.15 meters. The change in residual between two adjacent iterations... The calculation terminates when the number of iterations exceeds 50, indicating that M has completed online correction. The corrected extrinsic parameter matrix is ​​then written into the coordinate mapping module, which constructs a matrix containing N sets of data pairs. The verification set is defined, where N is a statistically positive integer. Using the current mapping matrix M and the camera calibration model function f, the reprojection positioning residual is calculated. Their computational relationship satisfies If the calculated root mean square residual exceeds a preset drift threshold, such as 0.5 meters, this module employs a nonlinear optimization algorithm to minimize the drift. The objective function is to iteratively adjust the parameters of the mapping matrix M until the residual converges to the drift threshold range.

[0029] The collision risk calculation module performs time-window-based collision detection. Based on a modified mapping matrix, this module tracks other vehicles in real time and calculates their current mileage position. and longitudinal driving speed At the same time, obtain the current mileage location of the inspection equipment. and running speed This module is based on the formula The remaining collision time (TTC) is calculated periodically, where, and The unit is meters. and The unit is meters per second; the risk avoidance decision module is used to implement tiered right-of-way arbitration, and the system has preset safety thresholds, such as setting an execution threshold. Every 10 seconds, the module compares the calculated TTC with the safety threshold in real time. When the TTC is less than or equal to... At this time, the module generates the highest priority forced track-cutting command; the guidance control module is used to execute deterministic spatial isolation actions. In response to the forced track-cutting command, this module directly drives the chassis controller of the inspection equipment to perform state machine switching, controlling the equipment to move from the current working track to the emergency track on the tunnel sidewall with maximum lateral acceleration. The working track corresponds to the centerline area of ​​the driving lane, and the emergency track corresponds to the preset distance area on the inner sidewall of the tunnel or the maintenance lane area. After entering the emergency track, the equipment remains stationary until a conflict resolution signal is received. In addition, this module also integrates a traffic flow gap control unit, which is used to actively construct a safe working window in continuous operation scenarios. This unit obtains the traffic flow density k and average flow velocity v in the preset area upstream of the inspection equipment, and executes the traffic flow phase reconstruction procedure based on the Lighthill-Whitham-Richards shock wave theory. The system collects the upstream cross-sectional flow q and density k in real time and calculates the evacuation wave propagation speed. Set the target locomotive headway increment based on the equipment operation time requirements. Utilizing upstream induced screen pulse rate limiting sequence ,in The value is set based on the operating speed gradient difference of 20 km / h as specified in the highway tunnel design code, and the slope of the generated spatiotemporal coordinate system is [value missing]. In low-density mobile areas, the inspection equipment control unit receives synchronization commands and calculates real-time speed offset. The chassis speed is adjusted using a proportional-integral-derivative algorithm to control the real-time mileage of the equipment. Always positioned within a ±5-meter buffer zone at the center of the low-density zone, this unit achieves dynamic collaborative operation based on the LWR traffic flow model. It calculates the headway increment required to generate the target moving gap and generates a speed pulse guidance sequence containing alternating first and second speed limits. This sequence is sent to the traffic guidance screen upstream of the inspection equipment, constructing a low-density traffic flow gap that propagates downstream in the traffic flow. The unit monitors the propagation position of this low-density traffic flow gap in real time and adjusts the operating speed of the inspection equipment to keep it within the spatiotemporal range of the low-density traffic flow gap.

[0030] Example 1: In a 3000-meter-long, four-lane, two-way highway tunnel with a design speed of 80 km / h and during peak traffic hours, the roadside perception system not only faces high-frequency mechanical vibration of the mounting base caused by the continuous passage of heavy trucks, but also environmental interference from drastic changes in light intensity at the entrance section. When the inspection equipment equipped with LiDAR performs road surface smoothness detection along the center line of the driving lane, the external parameters of the Nth camera group on the roadside experience physical drift due to long-term vibration of the base. This causes a longitudinal positioning error of 0.8 meters when the camera maps the pixel coordinates of social vehicles to the tunnel coordinate system. This error exceeds the system's allowable safety tolerance of 0.5 meters. Under these conditions, the parameter self-calibration module uses the inspection equipment as a mobile high-precision calibration source. When the equipment enters the camera's field of view and the confidence level meets the preset conditions, it synchronously captures the coordinates of the equipment's image center point at the same time t. The absolute mileage truth value uploaded by the device through the ultra-wideband positioning system This module calculates the reprojection residual based on the current mapping matrix M. Upon detecting an abnormal increase in the root mean square value of the residuals, nonlinear optimization iterative calculations are initiated, updating the parameters of the mapping matrix M within 200 milliseconds, and re-converging the system's positioning accuracy for subsequent high-speed moving targets to within 0.2 meters.

[0031] Immediately after calibration, a speeding vehicle approached the inspection equipment from behind at 110 km / h. Due to the curvature of the tunnel, the driver had not taken braking action before visually detecting the equipment. The collision risk calculation module used the corrected mapping matrix M to calculate the vehicle's real-time mileage. and longitudinal speed And combined with the current mileage of the inspection equipment The calculated remaining collision time (TTC) is 8.5 seconds. Since this TTC value is below the system's preset forced execution threshold, In 10 seconds, the risk avoidance decision-making module, without relying on cloud scheduling or complex path planning, directly triggers the underlying safety logic on the edge side, generating a highest-priority forced track-cutting command. The guidance control module responds to this command, driving the inspection equipment chassis controller to perform a state machine switch, rapidly moving from the working track in the driving lane to the risk avoidance track adjacent to the tunnel sidewall with a lateral acceleration of 0.5g, and locking the braking mechanism. This completes deterministic isolation of the physical space 1.5 seconds before other vehicles arrive. After confirming the conflict risk has been resolved, to address the technical challenge of the equipment being unable to re-enter the driving lane for operation under high-density traffic flow... Instead of passively waiting for the traffic flow to naturally thin out, the traffic flow gap modulation unit calculates the headway stretch required for a 25-second working clearance based on the traffic flow density k and average velocity v detected 3 kilometers upstream, using LWR shock wave theory. The system then sends a 20-second pulsed speed guidance sequence to the variable speed limit sign at the upstream entrance, alternately displaying speed limits of 60 km / h and 80 km / h. This artificially creates a low-density evacuation wave propagating downstream within the continuous traffic flow. The system monitors the arrival position of the wave crest in real time and adjusts the longitudinal speed of the inspection equipment accordingly. Make the propagation speed of the evacuation wave Maintaining synchronization guides the equipment to precisely enter the moving low-density gap window, resuming continuous scanning of the lane without interference from other vehicles.

[0032] Example 2: To verify the effectiveness and stability of the field-of-view mapping parameter self-calibration and traffic flow gap modulation mechanism proposed in this invention in a real complex environment, this experiment selected an in-service highway tunnel located in a mountainous and hilly area, with a total length of 4200 meters and a single-direction two-lane road as the test platform. The tunnel has an average daily traffic volume of more than 30,000 vehicles, with heavy-duty trucks accounting for as much as 45%. Moreover, the entrance section has strong alternating interference from front light and back light due to its unique geographical orientation. The test environment deployment includes 10 sets of roadside sensing units spaced 200 meters apart. Each unit integrates a high-resolution industrial camera (resolution 1920×1080, frame rate 30fps) and dual-base station ultra-wideband positioning anchor points. The inspection equipment selected is a wheeled robot with a weight of 200kg, equipped with solid-state lidar and a high-precision wheeled odometer, and its maximum running speed is set to 5 meters per second.

[0033] The experiment focused on verifying the parameter self-calibration module's ability to suppress environmental disturbances. To simulate a real long-term operating environment, random mechanical vibrations with a frequency of 20-50Hz and an amplitude of 2mm were artificially applied to the mounting bracket of camera group 3 to simulate the micro-vibration of the base caused by heavy-load vehicles passing by. Between 8:00 and 9:00 AM, high-intensity floodlights were used to simulate drastic fluctuations in the light intensity at the entrance section (from 500 lux to 50 lux). Under these conditions, the system ran continuously for 24 hours, periodically recording data when the self-calibration function was not enabled. The vehicle positioning error when the self-calibration function is enabled (comparison group) and (invention sample group) is compared. During the test, the actual vehicle position measured by a high-precision total station was used as the true reference. Under vibration and light interference, the mean absolute error (MAE) of the comparison group in mapping image coordinates to tunnel mileage coordinates showed a divergent trend over time, with a maximum error reaching 1.8 meters and a positioning fluctuation standard deviation exceeding 0.6 meters, which could not meet the lane-level collision avoidance requirements. In contrast, the invention sample group automatically triggered a self-calibration function based on heterogeneous data each time the inspection equipment entered the field of view. The closed-loop correction process runs in the background and does not interfere with normal video surveillance operations.

[0034] Table 1: Example Data Table Comparing Positioning Accuracy under Different Working Conditions

[0035]

[0036] Referring to Table 1, the data shows that under combined interference conditions, the sample group of this invention stably controlled the positioning error within 0.35 meters, which is better than the 1.78 meters of the control group. This result confirms that by introducing the controlled object (inspection equipment) as a dynamic floating calibration source and using a nonlinear optimization algorithm to minimize the reprojection residual, the positioning error can be effectively minimized. It can effectively compensate for the drift of external parameters caused by changes in the physical environment, ensuring that the system's perception accuracy of spatial conflicts is always within the safe threshold. A verification test was conducted on the effectiveness of the traffic flow gap modulation unit. The test was carried out during the peak traffic flow period from 16:00 to 17:00 in the afternoon. At this time, the average traffic flow density k of the upstream section of the tunnel is about 45 vehicles / km, which is a typical subsaturated high-density flow. When the gap modulation function was not turned on (comparison group), the inspection equipment frequently detected the conflict risk of TTC≤10s and performed 12 avoidance actions, resulting in its effective working time accounting for only 35% and the average working speed being less than 1.5 m / s.

[0037] After activating the gap modulation function (sample of this invention), the system monitors the upstream traffic flow status in real time and, based on the LWR model calculation results, issues a pulse speed limit command with a period of 25 seconds (alternating between 60km / h and 80km / h) to the variable speed limit sign at the entrance. Field observation data shows that this control strategy induces a series of low-density gap waves with a duration of about 15-20 seconds in the downstream traffic flow. The system guides the inspection equipment to adjust its speed to synchronously enter these gap waves. In this mode, the number of forced avoidances by the equipment in the same period is reduced to 2, the effective working time ratio is increased to 88%, and the average working speed reaches 4.2 meters per second.

[0038] Table 2: Example Data Table Comparing Work Efficiency and Safety

[0039]

[0040] As shown in Table 2, the data clearly demonstrates that by actively constructing and utilizing gaps in moving low-density traffic flows, the present invention solves the problem of inspection equipment being unable to move freely in high-density traffic environments without affecting overall traffic flow.

[0041] Example 3: This example combines Figures 1 to 3 This document describes a collision avoidance warning and safety guidance system for inspection equipment in a tunnel traffic environment. Figure 1 As shown, the data flow of this system architecture begins with roadside cameras acquiring images of vehicles and the environment and outputting the coordinates of the vehicles. Simultaneously, the inspection equipment, acting as both the controlled object and the mobile calibration source, uploads self-positioning mileage data in real time. These two sets of data converge into the coordinate mapping module, which performs pixel-to-geographic coordinate mapping matrix transformation to output the tunnel mileage position of the vehicles. The parameter self-calibration module receives the device's image coordinates and the true positioning value, and corrects the mapping matrix parameters online to eliminate parameter drift caused by environmental vibrations. The collision risk calculation module calculates the remaining time to collision (TTC) based on the mileage position. The data is transmitted to the risk avoidance decision module, which compares the TTC with the safety threshold. When the TTC is less than the safety threshold, a forced track-cutting command is generated. Finally, the guidance control module receives the command and outputs a drive control signal to drive the equipment to perform lateral movement and remain stationary until the conflict is resolved, thereby enabling the equipment to enter the tunnel sidewall avoidance zone to achieve physical isolation and safe stationary.

[0042] like Figure 2As shown, the horizontal axis represents the tunnel location, the left vertical axis represents the location coordinates in meters, and the right vertical axis represents the traffic flow density. The figure contains three main curves: the solid line represents the low-density gap peak position, the dotted line represents the inspection equipment position, and the dashed line represents the traffic flow density. Observing the trend in the figure, it can be seen that as the low-density gap peak position moves downstream along the tunnel, the traffic flow density curve shows a decreasing trend in the corresponding spatiotemporal section, while the inspection equipment position curve always closely follows the change of the low-density gap peak position. This indicates that the system guides the inspection equipment to operate synchronously in the spatiotemporal range of the low-density area by constructing low-density traffic flow gaps upstream. Figure 3 As shown in the diagram, this schematic depicts a cross-sectional scene within the clear width of the tunnel. UWB positioning anchors and roadside cameras are symmetrically arranged on both sides of the tunnel wall. The field of view of the roadside cameras (shown by dashed lines) covers the driving lane area. The diagram clearly identifies the inspection equipment located on the working track on the center line of the driving lane (shown by dashed boxes for the working position) and social vehicles traveling in the driving lane, as well as the inspection equipment located on the maintenance track on the inner side of the tunnel wall (shown by solid boxes for the emergency position). When a forced track-cutting command is triggered, the inspection equipment performs a forced track-cutting action along the movement direction arrow marked in the diagram, moving laterally from the working track to the emergency area marked by diagonal lines. The legend clearly distinguishes the graphic symbols for the current position, original / alternate position, emergency area, camera field of view, and movement direction.

[0043] Example 4: This example constructs an adaptive iterative control procedure based on multi-source residual feedback to address the technical risk that nonlinear optimization algorithms may get trapped in local minima or diverge when the initial mapping error is too large or environmental interference causes instability in feature point extraction. The system defines a composite criterion for iteration termination, introducing a residual change rate index in addition to the conventional residual root mean square error threshold, such as 0.2 m. When three consecutive iterations If the value is less than a preset stagnation threshold, such as 0.01 meters per iteration, and the current residual still does not meet the target, the system determines that it has fallen into a local minimum. In this case, the system does not terminate directly, but triggers a perturbation restart mechanism, which superimposes a random perturbation vector following a normal distribution at the current solution space location. Its variance is dynamically determined by the current residual value, so as to drive the algorithm to escape local traps and search for the global optimal solution again.

[0044] Secondly, to address the issue of feature extraction jitter caused by drastic changes in lighting, the system implements a confidence-based weighted optimization strategy. When constructing the validation dataset, it not only collects location coordinates but also simultaneously extracts the confidence score for each feature point. (Values ​​range from 0 to 1), in the nonlinear optimization objective function In this process, a dynamic weighting factor is introduced. ,set up , where f is a monotonically increasing function. For feature points with a confidence level below 0.5, their weights are set to zero, i.e., they are removed from the calculation sequence; for high-confidence points, their residual contribution is amplified. This weighting mechanism ensures that parameter correction is mainly driven by high-quality data and suppresses the contamination of calibration results by environmental noise.

[0045] Example 5: Addressing the stability concerns in the original document regarding vehicle kinematic parameter acquisition under extreme boundary conditions such as sensor failure or communication interruption, this example constructs an emergency parameter reconstruction procedure based on multi-source heterogeneous data fusion. This aims to resolve the technical risk of collision avoidance logic failure due to the system's inability to acquire key motion state parameters when a single sensing source, such as a roadside camera or microwave vehicle detector, experiences data loss or decreased confidence. The system defines multi-level parameter acquisition priorities and a degradation reconstruction strategy. High-precision optical flow velocity extracted by the roadside camera is prioritized as the primary control parameter. When the system detects a video stream frame drop rate exceeding 20% ​​or light intensity below a preset threshold, it automatically downgrades to using Doppler velocity data from the microwave vehicle detector. If both sensors fail (e.g., power outage or link failure), the system triggers a historical trajectory extrapolation mechanism. Using the vehicle's effective trajectory point data within the last 3 seconds before entering the blind zone, a Kalman filter algorithm is used to establish a motion state equation, predicting its position and velocity within a short future window, such as 5 seconds. During the prediction process, the system introduces boundary correction factors based on vehicle dynamics constraints, such as maximum acceleration and deceleration limits. This is to prevent the prediction results from violating the laws of physics.

[0046] In addition, to address potential initial measurement errors from different batches of sensors, the system implements a baseline calibration procedure based on statistical laws. During the initial system deployment or after equipment replacement, at night when traffic volume is low, a set of calibration vehicles of known length, such as the inspection equipment itself, are selected and repeatedly pass through the monitoring sections of each sensor in constant speed cruise mode. The system collects and statistically analyzes the distribution of measurement values ​​from each sensor, calculating the systematic deviation between these values ​​and the true calibration values. With random error variance Based on the statistical results, the system generates a dedicated calibration lookup table (LUT) for each sensor. In subsequent real-time operation, the system uses this lookup table to preprocess and correct the raw measurement data, eliminate systematic biases, and ensure that the multi-source data have a unified accuracy benchmark before fusion.

[0047] Example 6: Addressing the adaptive black box problem of the collision risk calculation module in tunnels with unique geometric shapes such as large curvature curves or long downhill slopes, as described in the original document, this example constructs an offline calibration and online revision procedure for the trajectory prediction model based on road geometric constraints. This aims to solve the technical risk of decreased accuracy in Time-of-Collision (TTC) calculation when road curvature causes the straight-line prediction model to fail or slope changes cause vehicle acceleration / deceleration characteristics to deviate from preset values. The system implements static geometric parameter calibration based on high-precision maps. During system deployment, a mobile measurement vehicle equipped with a high-precision inertial navigation system collects centimeter-level coordinates of the centerline of the entire tunnel. The system fits the collected discrete coordinate points into piecewise cubic spline curves and calculates the radius of curvature R and longitudinal slope i at any mileage location along the entire tunnel. These geometric parameters are permanently stored in the local database of the roadside edge calculation nodes as static constraint benchmarks for subsequent trajectory prediction.

[0048] Secondly, to address the uncertainty of lateral motion under curve conditions, the system constructs an adaptive prediction model based on curvature constraints. During real-time operation, when the vehicle enters a curve with a curvature radius of less than 500 meters, the collision risk calculation module no longer uses a simple linear uniform motion model. Instead, it calls a curvilinear motion model in the Frenet coordinate system, using the tunnel centerline as a reference axis. The vehicle motion is decomposed into longitudinal motion along the road direction and lateral motion perpendicular to the road direction. The system utilizes the offline calibrated curvature parameter R, combined with the vehicle's current longitudinal speed... Calculate the lateral centripetal acceleration required for the vehicle to maintain lane keeping. If the measured lateral acceleration deviates from the theoretical value, the system will introduce a lateral offset correction factor. This dynamically adjusts the predicted envelope of the vehicle's future trajectory to ensure that the prediction results conform to the physical laws of vehicle driving on curves. Finally, to address the TTC calculation deviation caused by the vehicle's acceleration due to gravity on long downhill sections, the system introduces a slope influence factor. Based on the offline calibrated longitudinal slope i, and combined with the vehicle dynamics equations, the system calculates the theoretical contribution of the gravity component to the vehicle's longitudinal acceleration. In the TTC calculation formula, this gravitational acceleration component is superimposed on the vehicle's real-time measured acceleration, thereby correcting the prediction of future speed changes of the vehicle.

[0049] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A collision avoidance warning and safety guidance system for inspection equipment in a tunnel traffic environment, characterized in that, include: The coordinate mapping module is used to acquire the coordinates of social vehicle images collected by roadside cameras and the self-positioning mileage data uploaded by inspection equipment, and to map the social vehicle image coordinates to the tunnel mileage location using a pixel-geographic coordinate mapping matrix. The parameter self-calibration module is used to simultaneously collect the self-positioning mileage data uploaded by the inspection equipment and the coordinates of the center point of the equipment image captured by the roadside camera when the inspection equipment enters the field of view of the roadside camera. It constructs a verification set containing multiple sets of mileage data and image coordinates, calculates the positioning deviation generated by the current mapping matrix, and uses the verification set to correct the pixel-geographic coordinate mapping matrix parameters online when the positioning deviation exceeds a preset threshold, until the positioning deviation converges. The collision risk calculation module is used to calculate the remaining collision time for social vehicles to reach the current mileage position of the inspection equipment based on the corrected mapping matrix. The risk avoidance decision module is used to compare the remaining collision time with a preset safety threshold, and generate a forced track-cutting command when the remaining collision time is less than the safety threshold. The guidance and control module is used to respond to the forced track cutting command, drive the inspection equipment to move laterally from the working position on the roadway to the danger zone on the tunnel sidewall and stay there until a conflict resolution signal is generated; The convergence condition for the online correction performed by the parameter self-calibration module follows the criterion of minimizing the reprojection error; the system extracts N sets of synchronous heterogeneous feature point pairs in real time to form a verification set. The objective function is set as minimizing the sum of squared reprojection residuals. By calculating the Jacobian matrix J, the matrix parameter M is driven to iterate in the negative gradient direction, and a confidence weight factor is introduced. Suppressing environmental noise, among which Based on the experimental determination of high-frequency vibration gradient in tunnels, the residual change between two adjacent iterations is... The calculation terminates when the number of iterations exceeds 50, indicating that M has completed online correction. The corrected extrinsic parameter matrix is ​​then written into the coordinate mapping module, which constructs a matrix containing N sets of data pairs. The verification set is defined, where N is a statistically positive integer. Using the current mapping matrix M and the camera calibration model function f, the reprojection error criterion is used to calculate the residual. ,in ,in The self-positioning mileage data fed back by the inspection equipment serves as a benchmark. Let M be the image coordinates captured by the roadside camera, M be the mapping matrix to be corrected, and f be the mapping function based on the camera calibration model. The parameter self-calibration module iteratively adjusts M until... Less than the preset threshold; The guidance control module is used to perform deterministic spatial isolation actions. In response to a forced track-cutting command, this module directly drives the chassis controller of the inspection equipment to perform state machine switching, controlling the equipment to move from the current working track to the emergency track on the tunnel sidewall with maximum lateral acceleration. The guidance control module also includes a traffic flow gap control unit, which is used to obtain the traffic flow density and average flow velocity in a preset area upstream of the inspection equipment, calculate the headway increment required to generate the target moving gap based on the traffic flow density, and generate an alternating speed limit sequence containing a first speed limit value and a second speed limit value that alternate with a preset period. The traffic flow gap control unit is also used to drive the traffic guidance screen located upstream of the inspection equipment to execute the alternating speed limit sequence in order to build a low-density vehicle flow gap moving downstream in the traffic flow.

2. The anti-collision warning and safety guidance system for inspection equipment in a tunnel traffic environment according to claim 1, characterized in that, The traffic flow gap control unit is also used to monitor the movement position of the low-density traffic flow gap relative to the inspection equipment in real time and generate speed synchronization commands. The speed synchronization commands are used to adjust the operating speed of the inspection equipment so that the real-time position of the inspection equipment is kept within the spatiotemporal range of the low-density traffic flow gap, so as to realize the synchronous operation of the inspection equipment with the low-density traffic flow gap.

3. The anti-collision warning and safety guidance system for inspection equipment in a tunnel traffic environment according to claim 1, characterized in that, The coordinate mapping module contains a pre-set discrete track model for tunnels; the discrete track model divides the physical space of the tunnel into working tracks corresponding to the center line of the driving lane and avoidance tracks corresponding to the tunnel sidewall areas; The lateral movement of the inspection equipment driven by the guidance and control module is a track state switching action based on a discrete track model, rather than an obstacle avoidance action based on continuous path planning.

4. The anti-collision warning and safety guidance system for inspection equipment in a tunnel traffic environment according to claim 1, characterized in that, The risk avoidance decision module is also used to generate graded instructions based on the different time intervals of the remaining collision time: when the remaining collision time is within the preset warning interval, a pre-switching instruction is generated to prompt the inspection equipment to interrupt the current detection task; when the remaining collision time is within the preset execution interval, a forced track-cutting instruction is generated to take over the motion control of the inspection equipment.

5. The anti-collision warning and safety guidance system for inspection equipment in a tunnel traffic environment according to claim 1, characterized in that, The system also includes a communication status monitoring module, which is used to monitor the data link status between the inspection equipment and the coordinate mapping module in real time. When the data link is interrupted, the communication status monitoring module triggers the fault protection logic on the device side. The fault protection logic is used to forcibly lock the inspection equipment to operate in the danger avoidance area and prohibit it from entering the driving lane operation position.

6. The anti-collision warning and safety guidance system for inspection equipment in a tunnel traffic environment according to claim 1, characterized in that, The coordinate mapping module is also used to access tunnel environment sensor data and dynamically adjust the preset safety threshold based on visibility data; when the visibility is lower than the preset standard, the preset safety threshold is increased to trigger the forced track cutting command in advance.

7. The anti-collision warning and safety guidance system for inspection equipment in a tunnel traffic environment according to claim 1, characterized in that, The guidance control module is also used to send a warning trigger signal to the roadside information board located at a preset distance upstream of the inspection equipment while generating a forced track cutting command. The warning trigger signal is used to drive the roadside information board to display the operation prompt information ahead.

8. The anti-collision warning and safety guidance system for inspection equipment in a tunnel traffic environment according to claim 1, characterized in that, The inspection equipment includes a positioning and communication unit and a chassis control unit. The positioning and communication unit is used to transmit self-positioning mileage data based on ultra-wideband positioning technology back to the coordinate mapping module in real time. The chassis control unit is used to receive and execute forced track-cutting commands from the guidance and control module, and the execution priority of the forced track-cutting commands is higher than the task planning commands inside the inspection equipment.

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