Evaluation method for driving route in cabin

Abstract

The invention provides a method for evaluating a driving route in a cabin, and the method comprises the steps: simulating a three-dimensional environment in the cabin, combining with a driving simulator, recording the driving operation and subjective feeling of a tested person, and generating driving data; according to scores of objective factors (such as advancing distance, backing distance, average speed and the like) and subjective factors (such as sight shielding degree, environment narrowing degree and the like), a route comprehensive score is generated in combination with a preset weight model, and an optimal design route is selected; furthermore, based on an evaluation method, an unloading sequence is designed, indexes such as driving route convenience and total unloading time consumption are comprehensively considered, and an unloading scheme is optimized; through an objective and subjective combined evaluation mode, the cabin design and the vehicle arrangement scheme are optimized, and the unloading efficiency and the operation safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of in-cabin driving route evaluation, and in particular to an evaluation method for in-cabin driving route. Background Art

[0002] With the rapid development of the logistics and transportation industry, sea transportation, as an efficient and economical mode of transportation, has been widely used in the global supply chain. In ship types such as ro-ro ships and ro-ro passenger ships that can transport a large number of vehicles, the loading and unloading efficiency of vehicles in the cabin is one of the key factors affecting the overall performance of the ship. The process of boarding and disembarking vehicles involves the driver driving along a predetermined route in a certain cabin environment. The cabin space is usually narrow due to the large number of loaded vehicles. Especially for cabins with multi-layer structures or complex layouts, their shape and size design and vehicle layout are important factors affecting the convenience of route driving, which in turn affects the loading and unloading efficiency.

[0003] In the prior art, the design of ship cabins and the arrangement of vehicles usually rely on the experience of designers. This method cannot take into account the driver's feelings and operations of the route during loading and unloading operations, resulting in inappropriate space reserved for vehicle driving, increasing vehicle movement time and reducing the accuracy of the evaluation. In addition, most of the existing vehicle operation convenience evaluation methods only make a single evaluation based on the driver's subjective feelings or the objective data of the vehicle, without making an overall evaluation of the driver and the cabin environment.

[0004] Therefore, how to conduct an overall assessment of the driver and the driving environment in the complex and narrow environment of the cabin, improve the accuracy of the assessment, and optimize the cabin shape and size design and vehicle layout plan are technical problems that need to be solved urgently. Summary of the invention

[0005] In view of this, the embodiment of the present application provides an evaluation method for the driving route in the cabin, which combines subjective factors and objective factors, and scientifically evaluates the convenience of the route, which can provide a key basis for cabin design and vehicle layout optimization, and ultimately improve vehicle movement efficiency. The embodiment of the present application provides the following technical solutions: On the one hand, an embodiment of the present application provides a method for evaluating an in-cabin driving route, comprising the following steps: S1. Simulate the geometric structure of the cabin, obstacle distribution and vehicle layout to generate a three-dimensional environment inside the cabin; S2. The three-dimensional environment of the cabin is presented on a driving simulator, and the test participants act as drivers and perform driving simulation on all or part of the routes of the vehicle layout scheme on the driving simulator; S3. Recording the operation of the driving simulator and the operation of the test participants during the driving simulation process to generate driving data; S4. Analyze the driving data to generate an objective factor score; the test personnel fill out a questionnaire to generate a subjective factor score, where: The objective factors include parameters that can be objectively measured during the driving process of vehicles on this route; The subjective factors include the subjective feeling scores of the driver for this route; S5. The objective factors and the subjective factors are weighted and calculated through a preset weight model to generate a comprehensive route score. Select the route with the highest score and corresponding to the most difficult one from the comprehensive route scores, and use it as the design route for the cabin shape and size design or the vehicle layout plan.

[0006] Further, the score of the subjective factors is obtained by the driver filling out a standardized questionnaire, specifically including the degree of line of sight obstruction, the degree of environmental narrowness, and the degree of slope influence; the score of the objective factors is obtained by quantifying the driving data, specifically including the forward distance, the reverse distance, the average speed, the number of operation conversions, and the minimum distance between the vehicle and the obstacle.

[0007] Furthermore, the preset weight model is S = Ws * Fs + Wo * Fo, where S is the comprehensive score, Fs is the subjective factor score, Fo is the objective factor score, and Ws and Wo are the weights of the subjective factor and the objective factor respectively.

[0008] Further, the S2 also includes a preselected route mechanism. Based on the historical scores of the extreme position routes and combined with the vehicle layout plan, predict the scores of the non-extreme position routes, and pre-screen several routes with lower scores for key driving simulations.

[0009] Further, the S5 also includes a monitoring mechanism for abnormal single scores. Based on the historical data of the scores, establish a multiple linear regression model, detect the correlation between each score or the overall score, use the numerical relationship with high correlation as the monitoring standard, and calibrate the abnormal values of the scores in a single experiment.

[0010] On the other hand, the embodiment of the present application provides a method for designing the unloading sequence based on the driving route in the cabin. Based on any one of the above methods, it includes the following steps: Obtain the design route generated according to the evaluation method of the driving route in the cabin and the corresponding vehicle layout plan; Starting from the vehicle closest to the exit, based on the design route and the vehicle layout plan, plan multiple feasible unloading sequences; Generate the driving routes of each vehicle corresponding to the unloading sequence, and perform a convenience score on the driving routes. Select the driving route with the lowest convenience score as the convenience characteristic value of the driving route of this unloading sequence. Combined with the driving route convenience eigenvalue, total unloading time consumption, and other necessary indicators of this unloading sequence, calculate the total score of this unloading sequence; For the feasible unloading sequences, calculate their total scores in sequence using the above steps, and select the unloading sequence with the highest total score to guide the actual unloading operation.

[0011] Furthermore, the unloading sequence satisfies the following conditions: The unloading path of each vehicle does not conflict with the current position or unloading path of other vehicles; The unloading sequence conforms to the sequence constraint of the exit positions.

[0012] Furthermore, under the condition that the cabin shape and size design are determined, combined with the number of vehicles in the layout plan, driving route convenience, shortest total unloading time consumption, overall weight center position, and other necessary indicators, achieve the overall scoring of the vehicle layout plan.

[0013] Compared with the prior art, the beneficial effects that can be achieved by at least one of the above technical solutions adopted in the embodiments of the present application at least include: Starting from evaluating the driving convenience of the route, in addition to the cabin loading capacity and vehicle entry and exit efficiency, the present invention provides a new scoring method for the cabin shape and size design and the vehicle layout plan, comprehensively balancing subjective factors and objective factors, selecting the optimal design route based on the comprehensive score, guiding the optimization of the cabin shape and size design and the vehicle layout plan, and improving the utilization rate of the cabin space and the convenience of driving operations.

[0014] The present invention provides a specific method, which comprehensively incorporates subjective factors (such as the degree of line of sight occlusion, environmental narrowness, slope influence, etc.) and objective factors (such as forward distance, reverse distance, average speed, etc.) into the evaluation system, combines the subjective experience of the driver in a specific environment and the accurately simulated in-cabin driving simulation data, obtains the quantified subjective factors through a standardized questionnaire, and obtains the objective factors by measuring the data during the vehicle driving process, forming an evaluation framework that combines subjective and objective factors. This subjective-objective combination method is more in line with the actual driving experience and can more comprehensively and accurately reflect the feelings and behaviors of the driver in the complex in-cabin environment.

[0015] In the present invention, through the preselected route mechanism, combined with historical data to predict the scores of non-limit position routes, screen out the key simulation routes, reduce unnecessary simulation workload, and improve the evaluation efficiency; and introduce a single-score outlier monitoring mechanism, use a multiple linear regression model to detect the score correlation, and calibrate the outliers to ensure the stability and credibility of the evaluation results.

[0016] In the present invention, the unloading sequence design method is based on the driving route generated by the evaluation method. By combining indicators such as convenience score, total unloading time, and the position of the overall center of gravity, the unloading sequence is optimized to reduce path conflicts and improve unloading efficiency and safety. Under the condition that the shape and size of the cabin are designed, considering indicators such as the number of vehicles, convenience of the driving route, and shortest unloading time, the overall optimization of the vehicle arrangement plan is achieved, reducing the operation difficulty and time cost. The simulated route scoring method provides high-quality input data for the loading and unloading sequence design, and the loading and unloading sequence design method further utilizes the scoring results to optimize the operation. The two complement each other, significantly improving the efficiency and quality of the overall loading and unloading operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 is a flowchart of the evaluation method for the in-cabin driving route provided by an embodiment of the present invention; Figure 2 is a sample form of the subjective questionnaire on the driving convenience of the in-cabin driving route provided by an embodiment of the present invention; Figure 3 is a flowchart of the evaluation method for the in-cabin driving route provided by another embodiment of the present invention; Figure 4 is a flowchart of the unloading sequence design method based on the in-cabin driving route provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following will describe the embodiments of the present invention in detail with reference to the drawings.

[0020] The following specific examples illustrate the embodiments of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the description, claims and drawings of the present invention are used to distinguish similar objects and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion.

[0022] When designing a new ship type, the layout of the cabins and the arrangement of vehicles need to be coordinated with the planning of the driving route to improve driving convenience and overall loading and unloading efficiency; for ships that have been built and put into operation, it is necessary to optimize the vehicle arrangement and driving route design under the existing cabin environmental conditions to improve the loading and unloading efficiency as much as possible. The complexity and narrowness of the cabin environment pose many challenges to vehicle driving. For example, there may be various obstacles in the cabin (such as pillars, ventilation ducts, etc.), the number, position or orientation of the vehicle arrangement is unreasonable, and the space reserved for vehicle entry and exit or the arranged entry and exit sequence is unreasonable, which may lead to difficult driver operation, low vehicle movement efficiency, and even affect safety. Therefore, optimizing the design of the cabin shape and size and the vehicle arrangement plan is the key to improving the ship's loading and unloading efficiency.

[0023] Adopting an evaluation method for in-cabin driving routes provided by the embodiments of the present application can be applicable to scenarios where the driving routes and arrangement plans of vehicles are optimized in a complex and narrow in-cabin environment to improve vehicle movement efficiency and ship loading and unloading speed.

[0024] On the one hand, the embodiments of the present application provide an evaluation method for in-cabin driving routes, as Figure 1 shown in the flowchart of the evaluation method for in-cabin driving routes in the first embodiment, including the following steps: S1. Simulate the geometric structure of the cabin, the distribution of obstacles and the arrangement of vehicles to generate a three-dimensional in-cabin environment; when designing a new ship type, a three-dimensional model of the cabin can be constructed based on two-dimensional drawings, and if there are three-dimensional design drawings, they can be converted into a three-dimensional model available for this method through lightweighting and other work; for ships that have been built and put into operation, the internal environment data of the cabin, including channel width, obstacle position, slope and turning angle, etc., are pre-acquired through sensors, cameras and lidar, and the collected environmental data are digitally modeled to generate an electronic three-dimensional environment inside the cabin.

[0025] S2. Based on the three-dimensional in-cabin environment, present it on the driving simulator. The test personnel, as drivers, select the vehicles in the arrangement plan on the driving simulator and conduct driving simulations along the unloading or loading routes; Specifically, this step is based on an existing driving simulator to complete driving simulation. The software of the driving simulator constructs a virtual driving simulation environment and imports the three-dimensional scene inside the cabin and the vehicle model into it. The hardware of the driving simulator provides a driver's seat and operating devices, and also provides a display to show the three-dimensional scene. The driving simulator allows the driver to simulate controlling the vehicle through the steering wheel, accelerator, brake, etc., drive the vehicle along the route from the berthing position to disembark, or drive from the dock along the route to the berthing position.

[0026] S3. Record the operation of the driving simulator and the operations of the participants during the driving simulation to generate driving data; Specifically, the software of the driving simulator records data such as the vehicle's movement trajectory, speed, the number of times the operating devices are operated, and the positions of the protruding points on the vehicle body during the driving simulation.

[0027] S4. Generate an objective factor score by analyzing the recorded data; directly obtain a subjective factor score by having the participants fill out a questionnaire, where: The objective factors include at least one of the forward distance, reverse distance, average speed, number of driving operation conversions, and minimum distance between the vehicle body and the cabin environment. Specifically, the forward distance and reverse distance can be calculated based on the vehicle's movement trajectory and speed, the average speed can be calculated based on the real-time speed, the number of driving operation conversions can be calculated based on the number of times various operating devices are operated, and the minimum distance between the vehicle body and the cabin environment can be calculated based on the positions of the protruding points on the vehicle body and the positions of each point in the three-dimensional environment.

[0028] In some embodiments, the objective factors include the forward distance, reverse distance, average speed, number of operation conversions, and minimum distance between the vehicle and the obstacle. Among them, the forward distance refers to the forward distance that the vehicle travels along the route, the reverse distance refers to the reverse distance that the vehicle travels along the route, the average speed refers to the average speed that the vehicle travels along the route, the number of operation conversions refers to the number of times the driver makes driving operations such as stepping on the accelerator, brake, shifting gears, turning the steering wheel to the left, and turning the steering wheel to the right during the driving process, and the number of conversions between different operations, and the minimum distance refers to the minimum distance between the protruding part of the vehicle body (excluding the tires) and the cabin wall or other vehicles. The score of the objective factors is obtained through multi-dimensional quantization of the three-dimensional scene inside the cabin. The distance calculation is to calculate the forward distance and reverse distance based on the vehicle's movement trajectory, the speed calculation is to calculate the average speed based on the vehicle's movement trajectory and time, the statistics of the number of operation conversions is to count the number of operation conversions based on the change of the vehicle's movement direction, and the minimum distance calculation is to calculate the minimum distance between the vehicle and the obstacle based on the vehicle's movement trajectory and the position of the obstacle.

[0029] Specifically, the convenience of the vehicle cabin and the overall vehicle layout is based on the route with the least convenience among all routes, and this requires normalizing the objective factor indicators. In the following description, atan( ) is the arctangent function, and π = 3.14159265.

[0030] The forward distance is the total distance traveled by the vehicle in the forward gear during the process of the vehicle moving from the initial position to the target position. The smaller its directionality, the better. The normalization calculation formula for the forward distance is Y = 1 - atan(Sf)*2 / π, where Sf is the forward distance.

[0031] The reverse distance is the total distance traveled by the vehicle in the reverse gear during the process of the vehicle moving from the initial position to the target position. The smaller its directionality, the better. The normalization calculation formula for the reverse distance is Y = 1 - atan(Sr)*2 / π, where Sr is the reverse distance.

[0032] The average speed is the average speed of the vehicle during the process of moving from the initial position to the target position. The larger its directionality, the better. The normalization calculation formula for the average speed is Y = atan(V)*2 / π, where V is the average speed.

[0033] The number of driving operation conversions is the number of conversions between different driving operations such as the driver stepping on the accelerator, brake, shifting gears, turning the steering wheel to the left, and turning the steering wheel to the right during the process of the vehicle moving from the initial position to the target position. The smaller its directionality, the better. The normalization calculation formula for the number of driving operation conversions is Y = atan(C)*2 / π, where C is the number of conversions.

[0034] The minimum distance between the vehicle and the environment is the minimum distance between the protruding part of the vehicle body (excluding the tires) and the cabin wall or other vehicles. The larger its directionality, the better. The normalization calculation formula for the minimum distance is Y = atan(D)*2 / π, where D is the minimum distance.

[0035] Subjective factors include the driver's subjective feelings about the candidate route; usually involving the user's perception or preference, which can be achieved through one or more of the following methods: after the simulated driving ends, the driver fills in the scores for the line-of-sight occlusion degree, environmental narrowness degree, slope influence, etc. of the route; it is not excluded that other methods, such as physiological sensors (such as heart rate, skin conductance, brain waves, etc.), are used to obtain the driver's physiological data during the simulated driving to indirectly reflect subjective factors such as psychological pressure and fatigue degree.

[0036] In some embodiments, subjective factors include the degree of line of sight obstruction, the degree of environmental narrowness, and the degree of slope influence. The degree of line of sight obstruction is to evaluate the degree to which the driver's line of sight is blocked by obstacles during driving. The degree of environmental narrowness is to evaluate the degree of spatial narrowness felt by the driver during driving. The degree of slope influence is to evaluate the influence of the slope on driving operations. The scores of subjective factors are obtained by the driver filling out a standardized questionnaire. After the driver completes the driving simulation, they fill out the standardized questionnaire and score the above-mentioned indicators for each route (for example, take an integer between 1 and 5, where 1 point represents the worst and 5 points represent the best). It should be understood that the standardized questionnaire should include clear scoring criteria and instructions to ensure that the driver can accurately understand and evaluate each indicator. The scorers are trained to understand the scoring criteria and maintain consistency in scoring.

[0037] Furthermore, "route driving convenience" is regarded as an indicator for evaluating driving routes and driving environments. The driver drives a vehicle along a predetermined route. This process has differences from the driving process under ideal conditions due to the complexity of the route and the influence of surrounding obstacles. Here, "ideal conditions" refer to driving along a straight line on an open and flat land. The scoring value ranges from [1, 5], where 1 indicates extremely difficult and even impossible to drive, and 5 indicates completely convenient, that is, the driving process is exactly the same as the driving process under ideal conditions. For a route, the greater the driving convenience, the better. Specifically, as Figure 2 shown in the sample form of the subjective questionnaire on route driving convenience of the evaluation method for in-cabin driving routes provided in the first embodiment of the present invention, the driver can fill out the questionnaire in the form shown in the figure.

[0038] S5. The subjective factors and objective factors are weighted and calculated through a preset weight model to generate a comprehensive score for the route. The route with the highest score corresponding to the most difficult one is selected from the comprehensive route scores, and its convenience value is used as the route convenience for the cabin shape and size design or the vehicle layout plan.

[0039] For the calculation model combining subjective and objective factors, one or more of the following methods can be used for combination: different weights are assigned to subjective factors and objective factors respectively (such as determining the weight ratio through expert experience or big data analysis); based on a hierarchical structure, subjective factors and objective factors are decomposed into multiple sub-factors (such as forward distance, reverse distance, average speed, etc.), and pairwise comparisons are made for each sub-factor to determine the weight matrix, and the weights of each factor are calculated according to the matrix; a machine learning model (such as a support vector machine, neural network) is trained through historical driving data, and the subjective score and objective data are used as input features to generate a comprehensive score; in view of the ambiguity of subjective factors (such as "high" or "low" degree of line of sight obstruction), a fuzzy rule base is constructed, and a comprehensive evaluation of subjective and objective factors is realized based on a fuzzy inference system.

[0040] Further, subjective factors and objective factors comprehensively score each candidate route using a preset weighting algorithm or rule. The preset weight model is S = Ws* Fs + Wo *Fo, where S is the comprehensive score, Fs is the subjective factor score, Fo is the objective factor score, and Ws and Wo are the weights of subjective factors and objective factors respectively. According to the preset weights, the scores of subjective factors and objective factors are weighted and summed to obtain the comprehensive score of each route.

[0041] Further, as Figure 3 shown in the flowchart of the method for evaluating in-cabin driving routes provided by another embodiment of the present invention, compared with the previous embodiment, S2 further includes predicting the scores of routes related to intermediate parking spaces based on the in-cabin vehicle layout plan and the scoring results of routes related to extreme positions, and pre-screening several routes with poor scores for key driving simulations. The method for selecting routes related to extreme positions includes those for the initial departure and the final departure, the closest and the farthest from the exit, the closest and the farthest from the cabin wall or obstacles, etc., for simulation and scoring. Based on the scoring results of these routes related to extreme positions, the scores of routes related to intermediate parking spaces are predicted in advance. If the convenience scores of these routes are better, they are screened out, and the routes with poor scores are selected for key driving simulations. This strategy of scoring based on routes related to extreme positions, predicting scores of routes related to intermediate positions, and route screening enables some routes with significantly better scores not to participate in the simulation, thereby improving the efficiency of the experiment.

[0042] The present application provides a third embodiment of the method for evaluating in-cabin driving routes. Compared with the first embodiment, S5 in the third embodiment further includes a monitoring mechanism for single scoring, detecting the correlation between various indicators based on a large amount of historical scoring data, and calibrating the outliers in single scoring.

[0043] The outlier is defined as follows: the correlation deviation between two indicators exceeds a certain threshold (for example, 2 times the standard deviation), the objective score or subjective score of the same rater for similar routes varies too much, or the scores of different raters for the same route vary too much. Outlier detection is based on statistical methods: using the 3σ principle, box plot or Z-score method to identify outliers in subjective scores, or based on machine learning methods: using isolation forest, local outlier factor or one-class support vector machine to identify outliers. The calibration of outliers can adjust the subjective score appropriately according to the deviation degree of the outlier from the objective indicator; if the number of outliers is very small and it is certain that they are unreliable, they can be directly deleted.

[0044] Furthermore, the verification mechanism generates correlations based on the analysis of historical scoring results. Based on a large amount of historical scoring data, a multiple linear regression model is established to detect possible outliers in a single scoring. The Pearson correlation coefficient (or Spearman rank correlation coefficient, Kendall rank correlation coefficient) is calculated among individual subjective scores, objective scores, overall subjective scores, and overall objective scores. Based on the statistics of a large amount of historical scoring data, the numerical relationship between the indexes with high correlation coefficients is extracted as the monitoring standard. In a newly conducted single experiment, based on this standard, the outliers in the scoring are calibrated. This mechanism can effectively avoid the deviation caused by abnormal single driving operations or subjective scoring, making the evaluation results more scientific and credible.

[0045] Through the above simulation route scoring method, an optimized simulation route is obtained, which can be further used for the design of the cabin loading and unloading sequence to improve the loading and unloading efficiency. As Figure 4 shown, the embodiment of the present application provides a method for designing the unloading sequence based on the driving route in the cabin. The loading and unloading sequence design method of this embodiment takes the previous simulation route as the input or basis, which is an extension or application of the same inventive concept. Based on the method of any of the above, the following steps are included: Obtain the design route generated according to the evaluation method of the driving route in the cabin and the corresponding vehicle arrangement plan; by taking the preferred simulation route as the basis and combining the loading and unloading path characteristics and optimization rules to generate the loading and unloading sequence plan, it is possible to reduce path intersections and reduce the loading and unloading time, thereby significantly improving the efficiency of the loading and unloading operation in the cabin.

[0046] Starting from the vehicle closest to the exit, based on the design route and the vehicle arrangement plan, plan multiple feasible unloading sequences; Generate the driving routes of each vehicle corresponding to the unloading sequence, and conduct a convenience score on the driving routes. Select the driving route with the lowest convenience score as the convenience characteristic value of the driving route for this unloading sequence; Combined with the convenience characteristic value of the driving route of this unloading sequence, the total unloading time, and other necessary indicators, calculate the total score of this unloading sequence; among them, according to the data of the simulated loading and sailing process, a series of objective indicators are calculated: loading efficiency indicators, including loading time, vehicle cabin space utilization rate, average vehicle loading time, etc.; safety indicators, including vehicle spacing, vehicle-to-bulkhead spacing, changes in ship stability parameters, etc.; operability indicators, including vehicle movement path length, number of turns, number of ramp passages, etc. Through questionnaires, subjective scores of experienced crew members and drivers on different arrangement plans are collected, including the convenience of loading operations, such as the convenience of vehicle entry and exit from the cabin and parking; the sense of safety, including the safety of vehicle movement in the cabin and the stability of parking; overall satisfaction with the plan: the overall evaluation of the arrangement plan.

[0047] For a feasible unloading sequence, use the above steps to calculate its total score in sequence, and select the unloading sequence with the highest total score to guide the actual unloading operation.

[0048] Furthermore, the unloading sequence satisfies the following conditions: The unloading path of each vehicle does not conflict with the current position or unloading path of other vehicles; The unloading sequence conforms to the order constraint of the exit positions. By analyzing the safety characteristics of the simulated route (such as avoiding path conflicts and reducing the risk of equipment collisions), the generated loading and unloading sequence plan can reduce potential safety hazards during the operation and improve the safety of the operation.

[0049] Furthermore, under the condition that the shape and size design of the cabin is determined, combined with the number of vehicles in the layout plan, the convenience of the driving route, the shortest total unloading time, the position of the overall weight center, and other necessary indicators, the overall score of the vehicle layout plan is realized.

[0050] In addition, the embodiment of the present application also provides an evaluation system for the driving route in the cabin, including: A model construction module, configured to simulate the geometric structure of the cabin, the distribution of obstacles, and the vehicle layout plan to generate a three-dimensional environment in the cabin; A driving simulation module, connected to the model construction module, configured to present the three-dimensional environment in the cabin on a driving simulator, and a test participant acts as a driver to perform driving simulation on all or part of the routes in the vehicle layout plan on the driving simulator; A data generation module, connected to the model construction module and the driving simulation module, records the operation of the driving simulator and the operations of the test participant during the driving simulation to generate driving data; A data analysis module, connected to the data generation module, analyzes the driving data to generate an objective factor score; the test participant fills out a questionnaire to generate a subjective factor score, where: The objective factors include parameters that can be objectively measured during the vehicle driving process on this route; The subjective factors include the subjective feeling score of the driver for this route; A data evaluation module, connected to the data analysis module, performs weighted calculation on the objective factors and the subjective factors through a preset weight model to generate a comprehensive route score, selects the route with the highest score corresponding to the most difficult one from the comprehensive route score, and uses it as the design route for the cabin shape and size design or the vehicle layout plan.

[0051] In this specification, for the same or similar parts among various embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments described later, the description is relatively simple, and for the relevant parts, reference can be made to the partial descriptions of the foregoing embodiments.

[0052] As described above, the foregoing is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for evaluating an in-cabin driving route, characterized in that: The following steps are involved: S1. Simulate the geometric structure of the cabin, obstacle distribution and vehicle layout to generate a three-dimensional environment inside the cabin; S2. The three-dimensional environment of the cabin is presented on a driving simulator, and the test participants act as drivers and perform driving simulation on all or part of the routes of the vehicle layout scheme on the driving simulator; S3. Recording the operation of the driving simulator and the operation of the test participants during the driving simulation process to generate driving data; S4. Analyze the driving data and generate an objective factor score; The participants fill in the questionnaire to generate subjective factor scores, where: The objective factors include parameters that can be objectively measured during the vehicle's travel on the route; The subjective factors include the driver's subjective feeling score on the route; S5. The objective factors and the subjective factors are weighted and calculated using a preset weight model to generate a comprehensive route score. The route with the highest score corresponding to the most difficult route is selected from the comprehensive route scores and used as the design route for cabin shape and size design or vehicle layout solution.

2. The method for evaluating an in-cabin driving route according to claim 1, characterized in that: The scores of the subjective factors are obtained by the driver filling out a standardized questionnaire, which specifically includes the degree of obstruction of vision, the narrowness of the environment, and the degree of influence of the slope; the scores of the objective factors are obtained by quantifying the driving data, which specifically includes the forward distance, the reverse distance, the average speed, the number of operation conversions, and the minimum distance between the vehicle and the obstacle.

3. The method for evaluating the in-cabin driving route according to claim 2, characterized in that: The preset weight model is S = Ws* Fs + Wo *Fo, where S is the comprehensive score, Fs is the subjective factor score, Fo is the objective factor score, Ws and Wo are the weights of the subjective and objective factors respectively.

4. The method for evaluating an in-cabin driving route according to claim 1, characterized in that: The S2 also includes a pre-selected route mechanism, which predicts the scores of non-extreme position routes based on the historical scores of extreme position routes in combination with the vehicle deployment plan, and pre-screens several routes with lower scores for key driving simulation.

5. The method for evaluating an in-cabin driving route according to claim 1, characterized in that: The S5 also includes a monitoring mechanism for single score outliers. Based on the historical score data, a multivariate linear regression model is established to detect the correlation between individual scores or the overall scores. The numerical relationship with high correlation is used as the monitoring standard, and the outliers of the scores are calibrated in a single experiment.

6. A method for designing an unloading sequence based on an in-cabin driving route, characterized in that: The method according to any one of claims 1 to 5 comprises the following steps: Obtaining a design route generated according to an evaluation method of an in-cabin driving route and a corresponding vehicle deployment plan; Starting from the vehicle closest to the exit, planning multiple feasible unloading sequences based on the designed route and the vehicle arrangement scheme; Generating driving routes for each vehicle according to the unloading sequence, scoring the driving routes for convenience, and selecting the driving route with the lowest convenience score as the driving route convenience feature value of the unloading sequence; The total score of the unloading sequence is calculated by combining the driving route convenience characteristic value, the total unloading time and other necessary indicators of the unloading sequence; For the feasible uninstallation sequences, the total scores are calculated in sequence using the above steps, and the uninstallation sequence with the highest total score is selected to guide the actual uninstallation operation.

7. The unloading sequence design method based on the in-cabin driving route according to claim 6 is characterized in that: The uninstallation sequence meets the following conditions: The unloading path of each vehicle does not conflict with the current position or unloading path of other vehicles; The unloading sequence complies with the order constraints of the exit locations.

8. The unloading sequence design method based on the in-cabin driving route according to claim 6 is characterized in that: Under the condition that the cabin shape and size are designed, the overall score of the vehicle layout plan is achieved by combining the number of vehicles in the layout plan, the convenience of the driving route, the shortest total unloading time, the overall weight center position and other necessary indicators.

Images