Automatic control system of electric tricycle with auxiliary digital controller

Abstract

The present application provides an electric tricycle automation control system with an auxiliary digital controller, which relates to the technical field of electric tricycles. The system includes: a sensor array module: a sensor array is configured and connected to a pre-built automation control platform; a driving path set generation module: a driving path set is generated according to the target start and end points; a target driving path generation module: a target driving path is obtained by screening the driving path set; an obstacle avoidance analysis node module: a target driving path is divided to obtain an obstacle avoidance analysis node sequence; an automatic control strategy module: data collection is performed, and the data collection results are analyzed to obtain K-1 local automatic control strategies; an execution module: the K-1 local automatic control strategies are sent to the digital controller to execute the auxiliary control of the target tricycle. The present application solves the technical problem that the prior art cannot provide an effective night driving control strategy, and improves the safety factor of night driving of electric tricycles.

Description

Technical Field

[0001] The present application relates to the technical field of electric tricycles, and in particular to an electric tricycle automation control system auxiliary to a digital controller. Background Art

[0002] As a convenient means of transportation, electric tricycles are widely used in urban and rural areas due to their low cost and high mobility. With the development of science and technology, the automatic control system of electric tricycles is also constantly improving. In the early days, electric tricycles usually used simple controllers to achieve basic motor control and vehicle speed control. With the rapid development of electronic technology, the control system of electric tricycles has begun to integrate more intelligent functions, such as battery management system, GPS positioning, anti-theft alarm, etc. Through advanced algorithms and electronic components, accurate control of the operating status of electric tricycles is achieved, which greatly improves the performance of the vehicle and the user's driving experience.

[0003] However, to date, the intelligence level of existing electric tricycle automatic control systems is still relatively low, and they usually only have basic speed control and gear adjustment functions. When faced with complex road conditions and driving needs, their control strategies are relatively simple, especially in night driving environments. They are unable to identify risk obstacles and provide effective lighting control and obstacle avoidance control strategies. Summary of the invention

[0004] The present application provides an electric tricycle automation control system with an auxiliary digital controller, which solves the technical problem that the existing electric tricycle automatic control system is unable to identify risk obstacles and provide effective lighting control and obstacle avoidance control strategies when faced with complex road conditions and driving requirements, especially in night driving environments. It achieves the technical effect of actively identifying collision risks in night driving environments, generating automatic control strategies for driving assistance control, and improving the safety factor of electric tricycles for night use.

[0005] In view of the above problems, the present application provides an electric tricycle automatic control system with an auxiliary digital controller, the system comprising:

[0006] Sensor array module: the sensor array module is used to configure a sensor array for the target tricycle, and connect the sensor array to a pre-built automation control platform, wherein the automation control platform includes a driving light analysis module and a driving obstacle avoidance analysis module, and the sensor array includes a photosensitive sensor and a plurality of directional laser radars; Driving path set generation module: the driving path set generation module is used to interactively obtain the target start and end points of the target tricycle, and generate a driving path set according to the target start and end points; Target driving path generation module: the target driving path generation module is used to interactively obtain the vehicle performance information of the target tricycle, and filter the target driving path from the driving path set according to the vehicle performance information; Obstacle avoidance analysis node module: The obstacle avoidance analysis node module is used to divide the target driving path according to the effective acquisition distance of the sensor array to obtain an obstacle avoidance analysis node sequence, wherein the obstacle avoidance analysis node sequence includes K obstacle avoidance analysis nodes; automatic control strategy module: the automatic control strategy module is used to schedule the sensor array to perform data acquisition according to the obstacle avoidance analysis node sequence, and perform data analysis on the data acquisition results based on the automation control platform to obtain K-1 local automatic control strategies; execution module: the execution module is used to send the K-1 local automatic control strategies to the digital controller through the automation control platform to execute auxiliary control of the target tricycle, wherein the automation control platform and the digital controller are connected using a CAN bus.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] This application obtains the target tricycle and the target starting and ending points and vehicle performance information through interaction, determines the target driving path of the target tricycle, configures a sensor array for the target tricycle, divides the target driving path into multiple driving local areas according to the effective acquisition distance of the sensor array, and dispatches the sensor to perform data acquisition when the target tricycle drives to the local starting point, analyzes the data acquisition results, obtains the corresponding local automatic control strategy, and performs auxiliary control of the target tricycle through a digital controller. It realizes the dynamic generation of corresponding automatic control strategies according to the real-time driving environment, assists the driving of the target tricycle, and improves the safety factor of electric tricycles at night.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1A schematic diagram of the structure of an electric tricycle automation control system with an auxiliary digital controller provided in an embodiment of the present application.

[0011] Figure 2 A schematic diagram of the process of obtaining a target driving path in an electric tricycle automation control system of an auxiliary digital controller provided in an embodiment of the present application.

[0012] Figure 3 A schematic diagram of a flow chart of obtaining a first local lighting control strategy in an electric tricycle automation control system of an auxiliary digital controller provided in an embodiment of the present application.

[0013] Figure 4 A schematic diagram of a flow chart for generating a first local driving control strategy in an electric tricycle automation control system of an auxiliary digital controller provided in an embodiment of the present application.

[0014] Explanation of reference numerals: sensor array module 10 , driving path set generation module 20 , target driving path generation module 30 , obstacle avoidance analysis node module 40 , automatic control strategy module 50 , execution module 60 . DETAILED DESCRIPTION

[0015] The embodiment of the present application provides an electric tricycle automation control system that assists a digital controller. In a nighttime driving environment, the electric tricycle automation control strategy is generated according to the surrounding driving environment to assist the driving of the electric tricycle and ensure the safety of the electric tricycle at night.

[0016] like Figure 1 As shown, the embodiment of the present application provides an electric tricycle automatic control system with an auxiliary digital controller, the system comprising:

[0017] Sensor array module 10: The sensor array module 10 is used to configure a sensor array for the target tricycle and connect the sensor array to a pre-built automation control platform, wherein the automation control platform includes a driving light analysis module and a driving obstacle avoidance analysis module, and the sensor array includes a photosensitive sensor and multiple directional laser radars.

[0018] Specifically, the target tricycle can be any electric tricycle, and a photosensitive sensor and multiple directional laser radars are installed on the outer contour of the target tricycle to configure a sensor array. The photosensitive sensor is installed in the middle of the front of the target tricycle to detect the brightness of the surrounding environment of the target tricycle. When selecting a photosensitive sensor, it is necessary to consider factors such as the working environment, response time, and sensitivity. It is best to choose a photosensitive sensor that is vibration-resistant, waterproof, has a short response time, and has high sensitivity, such as a phototransistor, a reflective photoelectric sensor, and a grayscale sensor. The directional laser radars are evenly distributed on the outer contour of the target tricycle to detect the three-dimensional contour of the surrounding environment of the target tricycle and help identify obstacles. When selecting a directional laser radar model, it is necessary to comprehensively consider factors such as detection distance, field of view, cost, and reliability. 40-line laser radars, 64-line laser radars, etc. can be used.

[0019] An automated control platform is pre-built to exchange information with the target tricycle and system, including a driving light analysis module and a driving obstacle avoidance analysis module. The automated control platform is connected to the sensor array, receives the surrounding environment data collected by the sensor array, and performs data analysis on the collected results. The driving light analysis module is used to analyze the surrounding light data collected by the photosensitive sensor, identify different light signals, and output the light control strategy. The driving obstacle avoidance analysis module is used to analyze the surrounding environment three-dimensional contour data collected by the directional laser radar, identify obstacles, and output the driving control strategy.

[0020] The sensor array module 10 of the embodiment of the present application collects driving environment data around the target tricycle by configuring a sensor array, thereby providing basic analysis data for the subsequent generation of an automatic control strategy.

[0021] Driving path set generation module 20: The driving path set generation module 20 is used to interactively obtain the target starting and ending points of the target tricycle, and generate a driving path set according to the target starting and ending points.

[0022] Specifically, the driving path set generation module 20 interacts with the user, obtains the starting and ending points of the target tricycle, generates multiple possible driving paths based on the starting and ending points of the target tricycle using map data and path planning algorithms, and aggregates these driving paths to generate a driving path set. The starting and ending points of the target tricycle can be the geographic coordinates or specific addresses of the starting and ending points, which are input by the user through the input interface; the map data includes road networks, traffic rules, roadblock information, traffic flow, etc.; the path planning algorithm includes A algorithm, D algorithm, or an algorithm based on machine learning, etc.; the user interface, map data, and path planning algorithm can be provided by a navigation system built into the automation control platform.

[0023] Target driving path generation module 30: The target driving path generation module 30 is used to interactively obtain vehicle performance information of the target tricycle, and obtain the target driving path from the driving path set based on the vehicle performance information.

[0024] Specifically, the target driving path generation module 30 obtains the vehicle performance information of the target tricycle through the built-in sensors of the target tricycle, user input, big data, etc. The vehicle performance information refers to various performance parameters that can reflect the driving ability of the target tricycle under different road conditions, including, for example, the maximum speed, driving force and climbing ability of the vehicle, torque, minimum turning radius, body roll angle, steering system reaction speed, gravity distribution, etc. The vehicle performance information of the target tricycle is used as a screening condition, and the optimal driving path is screened out from the driving path set and determined as the target driving path, which is the formal path planned to be adopted by the target tricycle.

[0025] The target driving path generation module 30 of the embodiment of the present application screens the driving path according to the vehicle performance information of the target tricycle, and determines the optimal driving path that meets the performance restrictions of the target tricycle.

[0026] Obstacle avoidance analysis node module 40: The obstacle avoidance analysis node module 40 is used to divide the target driving path according to the effective acquisition distance of the sensor array to obtain an obstacle avoidance analysis node sequence, wherein the obstacle avoidance analysis node sequence includes K obstacle avoidance analysis nodes.

[0027] Specifically, the effective acquisition distance of the sensor array is obtained according to the model parameters and performance parameters of the sensor array. Starting from the starting point of the target driving path, an obstacle avoidance analysis node is set at every effective acquisition distance to obtain an obstacle avoidance analysis node sequence consisting of K obstacle avoidance analysis nodes, where K is an integer greater than 1. The path between two adjacent obstacle avoidance analysis nodes is a local area, and the above K obstacle avoidance analysis nodes divide the target driving path into K-1 local areas. Except for the last local area, the straight line length of each local area is equal to the effective acquisition distance of the sensor array. Exemplarily, the straight line length of a certain target driving path is 820 meters, and the effective acquisition distance of the sensor array is 40 meters. The starting point of the target driving path is the first obstacle avoidance analysis node, and then an obstacle avoidance analysis node is set every 40 meters, and 22 obstacle avoidance analysis nodes can be obtained, which divides the target driving path into 21 local areas. Except for the last local area, the length of each local area is 40 meters.

[0028] The obstacle avoidance analysis node module 40 of the embodiment of the present application divides the target driving path into multiple small-scale local areas according to the effective collection distance of the sensor array for data collection and analysis, refines the target driving path, and ensures that accurate and reliable data can be collected in each local area, which facilitates the subsequent generation of corresponding automatic control strategies for each local area, thereby ensuring the driving safety of the entire target driving path.

[0029] Automatic control strategy module 50: The automatic control strategy module 50 is used to schedule the sensor array to perform data acquisition according to the obstacle avoidance analysis node sequence, and perform data analysis on the data acquisition results based on the automatic control platform to obtain K-1 local automatic control strategies.

[0030] Specifically, when the target tricycle drives to each obstacle avoidance analysis node position, the automatic control strategy module 50 mobilizes the sensor array to collect data about the surrounding environment in the corresponding local area, transmits the data collection results to the automatic control platform, and analyzes the data collection results through the driving light analysis module and the driving obstacle avoidance analysis module to obtain the lighting conditions and obstacle conditions of the driving environment in the local area, and generates a corresponding automatic control strategy, which is used to assist the target tricycle in driving in the above-mentioned local area. K-1 local areas of the target driving path can correspond to K-1 local area automatic control strategies.

[0031] The automatic control strategy module 50 of the embodiment of the present application generates K-1 local automatic control strategies through data analysis, provides accurate control parameters for the auxiliary control of the target tricycle, and improves the driving safety of the target tricycle in a night environment.

[0032] Execution module 60: The execution module 60 is used to send the K-1 local automatic control strategies to the digital controller through the automatic control platform to execute the auxiliary control of the target tricycle, wherein the automatic control platform and the digital controller are connected using a CAN bus.

[0033] Specifically, when the target tricycle travels to the Qth obstacle avoidance analysis node, the automatic control strategy module 50 generates the corresponding Qth local automatic control strategy, and the execution module 60 sends the Qth local automatic control strategy to the digital controller of the target tricycle through the automatic control platform. The digital controller is installed inside the target tricycle and connected to the automatic control platform using a CAQ bus to execute the Qth local automatic control strategy and assist in controlling the target tricycle to travel through the Qth local area. Q is an integer greater than or equal to 1 and less than K. When the target tricycle passes the Qth local area and arrives at the Q+1th obstacle avoidance analysis node, the automatic control strategy module 50 starts to generate the corresponding Q+1th local automatic control strategy.

[0034] The execution module 60 of the embodiment of the present application executes the automatic control strategy generated by the automatic control strategy module 50, and assists in controlling the target tricycle to adjust the lights and avoid obstacles, thereby ensuring the safe driving of the target tricycle in a night environment.

[0035] Furthermore, the automatic control strategy module 50 is also used to perform the following steps:

[0036] P51: Fit the first obstacle avoidance analysis node and the second obstacle avoidance analysis node to the target driving path to obtain a first local path vector and a first local obstacle avoidance source data, wherein the first obstacle avoidance analysis node and the second obstacle avoidance analysis node are adjacent obstacle avoidance analysis nodes among the K obstacle avoidance analysis nodes located at the starting position of the target driving path.

[0037] P52: According to the first local path vector, N local laser radars are dispatched among the multiple directional laser radars to collect entity data of the first local obstacle avoidance source data to obtain first local obstacle avoidance entity data.

[0038] P53: Using the first local path vector as a constraint, scheduling the photosensitive sensor to collect and obtain the first local environment brightness data.

[0039] P54: The first local environment brightness data and the first local obstacle avoidance entity data are respectively synchronized to the driving lighting analysis module and the driving obstacle avoidance analysis module of the automation control platform to perform driving control analysis and obtain a first local automatic control strategy, wherein the first local automatic control strategy includes a first local driving control strategy and a first local lighting control strategy.

[0040] P55: The automation control platform sends the first local automatic control strategy to the digital controller to execute auxiliary control of the target tricycle.

[0041] P56: Similarly, the K-1 local automatic control strategies are generated, and the K-1 local automatic control strategies are sent to the digital controller through the automatic control platform to execute auxiliary control of the target tricycle.

[0042] Specifically, two adjacent obstacle avoidance analysis nodes at the starting position of the target driving path are selected from the K obstacle avoidance analysis nodes as the first obstacle avoidance analysis node and the second obstacle avoidance analysis node. The first obstacle avoidance analysis node and the second obstacle avoidance analysis node are fitted to the target driving path to obtain the first local path vector and the first local obstacle avoidance source data. The first local vector describes the local path direction and distance from the first obstacle avoidance analysis node to the second obstacle avoidance analysis node. The first local obstacle avoidance source data refers to the original environmental information collected by the sensor array when the target tricycle is at the starting position, including the position, size, speed and movement direction of the obstacle.

[0043] According to the first local path vector, N local laser radars with the same driving direction as the target tricycle are dispatched from multiple directional laser radars in the sensor array to collect entity data of the first local obstacle avoidance source data, and obtain the first local obstacle avoidance entity data. The first local obstacle avoidance entity data refers to the more specific and detailed 3D point cloud data of potential obstacles obtained by the N local laser radars through accurate data collection in the first local area. These data include not only the basic attributes of the obstacle, but also the shape, surface features, material, etc. of the obstacle.

[0044] The first local area path vector is analyzed to determine the area that the target tricycle is about to enter, that is, the actual position of the first local area, and the photosensitive sensor is dispatched to perform data collection on the first local area to obtain the first local area environment brightness data.

[0045] The first local area environment brightness data is synchronized to the driving light analysis module of the automated control platform to perform driving light control analysis and generate a first local area light control strategy. The first local area light control strategy is used to adjust the driving light brightness of the target tricycle in the first local area. When the brightness of the first local area environment is too low, the first local area light control strategy is to increase the light brightness of the target tricycle. The first local area obstacle avoidance entity data is synchronized to the driving obstacle avoidance analysis module of the automated control platform to perform driving control analysis and obtain the first local area driving control strategy. The first local area driving control strategy is a set of instructions for adjusting the driving path and driving speed of the target tricycle in the first local area, including acceleration, deceleration, steering, speed on different curves, etc.

[0046] The automation control platform sends the first local automatic control strategy formed by combining the first local lighting control strategy and the first local driving control strategy to the digital controller of the target tricycle through the CAN bus to perform auxiliary control on the target tricycle.

[0047] Each time a local automatic control strategy is generated, the automatic control strategy module 50 will send the generated local automatic control strategy to the digital controller through the automation control platform to execute auxiliary control of the target tricycle, and so on, generating K-1 local automatic control strategies until the target tricycle passes through K obstacle avoidance analysis nodes and reaches the end of the target driving path.

[0048] The embodiment of the present application collects the first local obstacle avoidance entity data and the first local environment brightness data in the first local area, analyzes and generates the first local driving control strategy and the first local lighting control strategy, and provides specific control parameters for the safe driving of the target tricycle in the first local area. Optionally, when the directional laser radar detects an obstacle, the automatic control strategy module 50 can also issue an early warning through the automatic control platform and issue a prompt to the driver through an output device such as a loudspeaker.

[0049] Furthermore, the automatic control strategy module described in the embodiment of the present application is also used to perform the following steps:

[0050] P561: When the digital controller moves the target tricycle to the second obstacle avoidance analysis node based on the first local automatic control strategy, the third obstacle avoidance analysis node is fitted to the target driving path, and the second obstacle avoidance analysis node is combined with the target driving path to generate a second local path vector and a second local obstacle avoidance source data.

[0051] P561: Schedule the sensor array to perform data collection based on the second local path vector and the second local obstacle avoidance source data as constraints to obtain second local obstacle avoidance entity data and second local environment brightness data.

[0052] P563: Synchronize the second local obstacle avoidance entity data and the second local environment brightness data to the automation control platform to perform driving control analysis and obtain a second local automatic control strategy.

[0053] P564: The automation control platform sends the second local automatic control strategy to the digital controller for executing auxiliary control of the target tricycle's travel process from the second obstacle avoidance analysis node to the third obstacle avoidance analysis node.

[0054] P565: And so on, generate the K-1 local automatic control strategies.

[0055] Specifically, the digital controller executes the first local automatic control strategy to control the target tricycle to move from the first obstacle avoidance analysis node to the second obstacle avoidance analysis node. At this time, the automatic control strategy module 50 obtains the real-time position of the target tricycle according to the navigation system built into the automatic control platform, determines that the target tricycle has reached the second obstacle avoidance analysis node, fits the third obstacle avoidance analysis node to the target driving path, and generates a second local path vector describing the path direction and distance from the second obstacle avoidance node to the third obstacle avoidance node and a second local obstacle avoidance source data describing the surrounding environment information of the second obstacle avoidance node in combination with the second obstacle avoidance analysis node. With the second local path vector and the second local obstacle avoidance source data as constraints, multiple directional laser radars and photosensitive sensors with the same direction as the second local vector are dispatched to perform data collection on the second local area, obtain the second local obstacle avoidance entity data and the second local environment brightness data, and further analyze the second local automatic control strategy through the automatic control platform. The second local automatic control strategy includes the second local driving control strategy and the second local lighting control strategy. The digital controller executes the second local automatic control strategy to control the target unicycle to move from the second obstacle avoidance analysis node to the third obstacle avoidance analysis node. Afterwards, the automatic control strategy module 50 continues to fit the next obstacle avoidance analysis node, repeats the above steps to generate the next local automatic control strategy, and sends it to the digital controller through the automated control platform to execute the automatic control strategy, assisting in controlling the target tricycle to travel through the next local area, until the automatic control strategy module 50 determines that the target tricycle has reached the end of the target driving path and ends the execution.

[0056] The embodiment of the present application collects the latest data in the local area according to the position of the target tricycle, dynamically generates local automatic control strategies corresponding to multiple local areas, assists in controlling the driving of the target tricycle, and ensures the effectiveness and referenceability of the local automatic control strategies.

[0057] Further, such as Figure 2 As shown, the target driving path generation module 30 is also used to perform the following steps:

[0058] P31: The driving path set includes multiple road condition information of multiple alternative paths.

[0059] P32: Preset a road evaluation index, and split the multiple road condition information based on the road evaluation index to obtain multiple road evaluation parameter sets.

[0060] P33: The vehicle performance information includes driving bump constraints, driving curvature constraints and driving slope constraints.

[0061] P34: Using the vehicle performance information to traverse the multiple road evaluation parameter sets for data screening, a plurality of candidate evaluation parameter sets are obtained.

[0062] P35: Using the multiple candidate evaluation parameter sets as a reference for road driving matching, introducing a road matching degree evaluation function to perform road matching evaluation, and obtaining multiple road matching degrees.

[0063] P36 serializes the multiple road matching degrees and performs extreme value call to obtain the target driving path.

[0064] Furthermore, the road matching evaluation function described in the embodiment of the present application is as follows:

[0065] ;

[0066] in, is the road matching coefficient, is the road curvature weight, is the road slope weight, is the road bumpiness weight, is the mean road curvature, is the minimum curvature of the road, is the maximum curvature of the road, is the mean interval between adjacent curve sections, is the maximum distance between adjacent curve sections, is the mean road slope, is the minimum slope of the road, is the maximum slope of the road, is the mean interval between adjacent ramps, is the maximum spacing distance between adjacent ramps, is the average road bumpiness, is the minimum road bumpiness, is the maximum road bumpiness, is the mean interval between adjacent bumpy sections, It is the maximum distance between adjacent bumpy road sections.

[0067] Specifically, the driving path set generated by the driving path set generation module 20 includes multiple alternative paths and their corresponding multiple road condition information. The road condition information includes road type, traffic flow, estimated driving time, road surface condition, etc. Road evaluation indicators are pre-set, including three road evaluation indicators: road curvature, road slope, and road bumpiness. The road condition information of multiple alternative paths is split according to the road evaluation indicators, and each road evaluation indicator is correspondingly split into a road evaluation parameter set corresponding to each road, so as to obtain multiple road curvature evaluation parameter sets, multiple road slope evaluation parameter sets, and multiple road bumpiness evaluation parameter sets with a one-to-one correspondence.

[0068] The vehicle performance information includes driving bump constraint, driving curvature constraint and driving slope constraint. The driving bump constraint refers to the road bump threshold that the target tricycle can pass safely, the driving curvature constraint refers to the road curvature threshold that the target tricycle can pass, usually represented by the turning radius of the target tricycle, and the slope constraint refers to the road slope threshold that the target tricycle can pass safely, usually represented by the power parameters of the target tricycle.

[0069] According to the curvature constraint of the target tricycle, the road curvature evaluation parameter set is traversed to select multiple alternative curvature evaluation parameter sets that meet the curvature constraint; according to the slope constraint of the target tricycle, the road slope evaluation parameter set is traversed to select multiple alternative slope evaluation parameter sets that meet the slope constraint; according to the bumpy constraint of the target tricycle, the road bumpiness evaluation parameter set is traversed to select multiple alternative bumpiness evaluation parameter sets that meet the bumpy constraint. Exemplarily, for bumpy constraint, the system will select a path with higher road surface flatness; for curvature constraint, the system will select a path with larger curve radius and longer sight distance; for slope constraint, the system will select a path with smaller slope and shorter slope length.

[0070] A plurality of alternative evaluation parameter sets are used as references for road driving matching, and the selected plurality of alternative paths are evaluated for road matching by the above-mentioned road matching evaluation function to obtain a plurality of corresponding road matching degrees. The larger the calculation result of the road matching evaluation function, the more complex the road and the higher the difficulty of passing. The obtained plurality of road matching degrees are serialized in order from small to large to obtain a road matching degree sequence. The minimum value of the sequence is called to obtain the corresponding driving path, which is determined as the target driving path. Exemplarily, the driving path set consists of M alternative paths, M is an integer greater than 1, and the road condition information of the M alternative paths is split to obtain M road curvature evaluation parameter sets, M road slope evaluation parameter sets, and M road bumpiness evaluation parameter sets. According to the driving curvature constraint of the target tricycle, the M road curvature evaluation parameter sets are traversed to obtain A1 alternative curvature evaluation parameter sets, A1 is an integer greater than or equal to 1 and less than or equal to M, and the corresponding A1 alternative paths are obtained. According to the driving slope constraint of the target tricycle, A1 road slope evaluation parameter sets are traversed to obtain A2 candidate slope evaluation parameter sets, A2 is an integer greater than or equal to 1 and less than or equal to A1, and the corresponding A2 candidate paths are obtained; according to the driving bumpiness constraint of the target tricycle, the corresponding A2 road bumpiness evaluation parameter sets are traversed to obtain A3 candidate bumpiness evaluation parameter sets, A3 is an integer greater than or equal to 1 and less than or equal to A2, and the corresponding A3 candidate paths are obtained. The A3 candidate paths are evaluated for road matching through the road matching evaluation function to obtain A3 road matching degrees, and the A3 road matching degrees are sorted from small to large to determine the driving path corresponding to the minimum value, which is the target driving path.

[0071] The embodiment of the present application screens multiple driving paths by road evaluation indicators and vehicle performance, and introduces a road matching evaluation function to perform road matching evaluation, thereby determining a driving path that meets the performance limitations of the target tricycle and has the lowest road complexity, thereby ensuring that the target tricycle can arrive at the destination smoothly and safely.

[0072] Further, such as Figure 3 As shown, the automatic control strategy module 50 is also used to perform the following steps:

[0073] P541: Interactively obtain multiple sample brightness adjustment arrays, where each sample brightness adjustment array includes sample environment brightness and sample light control brightness.

[0074] P542: Use polynomial fitting to perform data relationship analysis on the multiple sample brightness adjustment arrays to obtain a driving light control function.

[0075] P543: Synchronize the driving light control function to the driving light analysis module.

[0076] P544: The first local environment brightness data includes multiple instantaneous local environment brightnesses. The multiple instantaneous local environment brightnesses are serialized and extreme value extraction is performed. The extraction results are used as inputs of the driving light analysis module. The driving light calculation is performed through the driving light control function to obtain the first local light control strategy.

[0077] Specifically, the automatic control strategy module 50 obtains multiple sample brightness adjustment arrays through information interaction, and each sample brightness adjustment array includes sample environment brightness and sample light control brightness. These sample brightness adjustment arrays are the brightness data of the tricycle in different environments, including the environment brightness and the corresponding light control brightness. The automatic control platform can collect the historical records of the target tricycle and obtain the debugging records of the target tricycle or the same model tricycle based on big data. The data relationship analysis of multiple sample brightness adjustment arrays is performed using polynomial fitting, and a mathematical model between the environment brightness and the light control brightness is established to obtain the driving light control function. The driving light control function can calculate the light control brightness under the environment brightness according to the input environment brightness value. The obtained driving light control function is applied to the driving light analysis module to analyze and adjust the light brightness in real time.

[0078] The first local environment brightness data obtained in step P53 includes multiple instantaneous local environment brightnesses. The multiple instantaneous local environment brightnesses are serialized in order from small to large, and the minimum value, that is, the darkest local environment brightness, is extracted. The minimum value is input into the driving light analysis module, and the driving light is calculated through the driving light control function to obtain the corresponding light control brightness, and the first local light control strategy is output.

[0079] The embodiment of the present application obtains the driving light control function through polynomial fitting, which can automatically adjust the light brightness of the target tricycle according to the brightness changes of the driving environment, ensure the target tricycle has the best lighting effect in the night environment, and improve the safety factor of the electric tricycle during driving.

[0080] Further, such as Figure 4 As shown, the automatic control strategy module 50 is also used to perform the following steps:

[0081] P545: In the driving obstacle avoidance analysis module, entity modeling is performed based on the first local obstacle avoidance entity data to obtain a first local obstacle model.

[0082] P546: Fitting the first local obstacle avoidance source data to the first local obstacle model to perform trajectory control judgment and obtain multiple obstacle avoidance nodes.

[0083] P547: Interactively obtain a target turning radius and a target body width of the target tricycle, and combine the target turning radius and the target body width to obtain an obstacle avoidance trajectory radius.

[0084] P548: Generate multiple obstacle avoidance driving curves according to the obstacle avoidance trajectory radius and the multiple obstacle avoidance nodes.

[0085] P549: Perform driving trajectory fitting based on the multiple obstacle avoidance driving curves and the first local obstacle avoidance source data to generate the first local driving control strategy.

[0086] Furthermore, the automatic control strategy module 50 is also used to perform the following steps:

[0087] P549-1: Smoothly connect the multiple obstacle avoidance driving curves to the first local obstacle avoidance source data to obtain a first local optimized trajectory.

[0088] P549-2: Pre-construct a curve-speed association table, and synchronize the multiple curves of the multiple optimized curves in the first local optimization trajectory to the curve-speed association table to perform curve driving speed matching, and obtain multiple optimized curve speed constraints.

[0089] P549-3: Interactively obtain the standard driving speed of the target tricycle, and smoothly fit the standard driving speed and the multiple optimized curvature speed constraints to the first local optimization trajectory to generate the first local driving control strategy.

[0090] Specifically, according to the first local obstacle avoidance entity data obtained in step P52, including information such as the shape, size, and position of the obstacle, a mathematical model, namely the first local obstacle model, is created in the driving obstacle avoidance analysis module. The first local obstacle model is used to represent obstacles in the first local surrounding environment. The first local obstacle avoidance source data is fitted to the first local obstacle model, the specific position and potential motion trajectory of the obstacle are determined, and multiple obstacle avoidance nodes are determined, which refer to the key position points of the target tricycle when avoiding obstacles.

[0091] The system obtains the target turning radius and target body width of the target tricycle through user input or interaction with the built-in sensors of the target tricycle, and calculates the obstacle avoidance trajectory radius, which ensures that the vehicle can smoothly bypass obstacles. Based on the obstacle avoidance trajectory radius and multiple obstacle avoidance nodes, the path planning algorithm of the built-in navigation system of the automated control platform generates multiple obstacle avoidance driving curves, which represent the paths that the target tricycle can take when avoiding obstacles.

[0092] After generating multiple obstacle avoidance driving curves, the multiple obstacle avoidance driving curves are smoothly connected through a curve smoothing algorithm to obtain the first local optimization trajectory to ensure that the vehicle can smoothly transition when avoiding obstacles and avoid sudden turns or speed changes. Curve smoothing algorithms include Bezier curves, B-spline curves, etc. Based on big data, the curvature of the path curves of the same model of electric tricycles during driving, that is, the corresponding driving speed, is collected, and a curvature-speed association table is pre-constructed to recommend a suitable driving speed based on the curvature of the curve. The curvature information of multiple optimized curves in the first local optimization trajectory is synchronized to the curvature-speed association table, and the appropriate driving speed is matched for each curve according to the recommended speed in the table, thereby obtaining multiple optimized curvature-speed constraints.

[0093] By interacting with the model and performance information of the target tricycle and the road speed limit, the standard driving speed of the target tricycle is obtained, and the standard driving speed and multiple optimized curvature speed constraints are smoothly fitted to the first local optimization trajectory through the speed planning algorithm, and the first local driving control strategy is generated to ensure that the vehicle can maintain a suitable speed in both straight and curved sections, while ensuring the smoothness of speed changes. The standard driving speed refers to the speed of the target tricycle when it is driving normally in a straight line.

[0094] The embodiment of the present application generates a first local driving control strategy by optimizing the first local driving path and the driving speed of the target tricycle, and guides the target tricycle to avoid obstacles safely and smoothly within the first local area through appropriate speed and motion estimation, thereby ensuring the safety of the target tricycle during driving.

[0095] In summary, the electric tricycle automatic control system with auxiliary digital controller provided in the embodiment of the present application has the following technical effects:

[0096] In the embodiment of the present application, a sensor array is configured for a target tricycle, a driving path set is generated by a driving path set generation module 20, a target driving path generation module 30 is used to screen multiple driving paths according to vehicle performance information and calculate the road matching degree, and determine the optimal driving path that meets the performance limitations of the target tricycle; the target driving path is divided into multiple local areas according to the effective collection distance of the sensor array by an obstacle avoidance analysis node module 40; dynamic data collection is performed on the divided local areas as units by an automatic control strategy module 50, the latest environmental brightness data and obstacle avoidance source data and obstacle avoidance entity data in each local area are collected, and data analysis is performed using a driving light analysis module and a driving obstacle avoidance analysis module of an automated control platform to generate corresponding local light control strategies and local driving control strategies; the local light control strategies and local driving control strategies are sent to the digital controller of the target tricycle by an execution module 60 to control the lights, driving direction and driving speed of the target tricycle, so as to help the target tricycle avoid obstacles smoothly and pass through the local path safely.

[0097] As described above, the electric tricycle automatic control system with auxiliary digital controller provided in the embodiment of the present application can plan the driving path for the electric tricycle, collect environmental information during driving, automatically generate corresponding control strategies, and assist in control through the digital controller to ensure that the electric tricycle has good lighting effects in the night driving environment, can avoid obstacles in a timely and safe manner, and improve the safety factor of the electric tricycle in the night driving environment.

[0098] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. Electric tricycle automation control system with auxiliary digital controller, It is characterized in that The system comprises: Sensor array module: the sensor array module is used to configure a sensor array for the target tricycle and connect the sensor array to a pre-built automation control platform, wherein the automation control platform includes a driving light analysis module and a driving obstacle avoidance analysis module, and the sensor array includes a photosensitive sensor and a plurality of directional laser radars; Driving path set generation module: The driving path set generation module is used to interactively obtain the target starting and ending points of the target tricycle, and generate a driving path set according to the target starting and ending points; Target driving path generation module: The target driving path generation module is used to interactively obtain vehicle performance information of the target tricycle, and filter and obtain the target driving path from the driving path set according to the vehicle performance information; Obstacle avoidance analysis node module: the obstacle avoidance analysis node module is used to divide the target driving path according to the effective acquisition distance of the sensor array to obtain an obstacle avoidance analysis node sequence, wherein the obstacle avoidance analysis node sequence includes K obstacle avoidance analysis nodes; Automatic control strategy module: The automatic control strategy module is used to schedule the sensor array to perform data acquisition according to the obstacle avoidance analysis node sequence, and perform data analysis on the data acquisition results based on the automatic control platform to obtain K-1 local automatic control strategies; Execution module: The execution module is used to send the K-1 local automatic control strategies to the digital controller through the automatic control platform to execute the auxiliary control of the target tricycle, wherein the automatic control platform and the digital controller are connected using a CAN bus.

2. The system according to claim 1, It is characterized in that The automatic control strategy module is also used to perform the following steps: P51: Fitting a first obstacle avoidance analysis node and a second obstacle avoidance analysis node to the target driving path to obtain a first local path vector and first local obstacle avoidance source data, wherein the first obstacle avoidance analysis node and the second obstacle avoidance analysis node are adjacent obstacle avoidance analysis nodes located at the starting position of the target driving path among the K obstacle avoidance analysis nodes; P52: According to the first local path vector, dispatching N local laser radars in the multiple directional laser radars to collect entity data of the first local obstacle avoidance source data to obtain first local obstacle avoidance entity data; P53: Using the first local path vector as a constraint to schedule the photosensitive sensor to collect and obtain first local environment brightness data; P54: Synchronizing the first local environment brightness data and the first local obstacle avoidance entity data to the driving light analysis module and the driving obstacle avoidance analysis module of the automation control platform respectively to perform driving control analysis, and obtain a first local automatic control strategy, wherein the first local automatic control strategy includes a first local driving control strategy and a first local light control strategy; P55: The automation control platform sends the first local automatic control strategy to the digital controller to execute auxiliary control of the target tricycle; P56: Similarly, the K-1 local automatic control strategies are generated, and the K-1 local automatic control strategies are sent to the digital controller through the automatic control platform to execute auxiliary control of the target tricycle; Among them, P56 specifically includes the following steps: P561: When the digital controller moves the target tricycle to the second obstacle avoidance analysis node based on the first local automatic control strategy, a third obstacle avoidance analysis node is fitted to the target driving path, and a second local path vector and a second local obstacle avoidance source data are generated in the target driving path in combination with the second obstacle avoidance analysis node; P562: Scheduling the sensor array to perform data collection based on the second local path vector and the second local obstacle avoidance source data as constraints to obtain second local obstacle avoidance entity data and second local environment brightness data; P563: Synchronizing the second local obstacle avoidance entity data and the second local environment brightness data to the automation control platform to perform driving control analysis and obtain a second local automatic control strategy; P564: The automation control platform sends the second local automatic control strategy to the digital controller for executing auxiliary control of the target tricycle during the driving process from the second obstacle avoidance analysis node to the third obstacle avoidance analysis node; P565: And so on, generate the K-1 local automatic control strategies.

3. The system according to claim 1, It is characterized in that The target driving path generation module is also used to perform the following steps: The driving path set includes a plurality of road condition information of a plurality of alternative paths; Preset a road evaluation index, and split the plurality of road condition information based on the road evaluation index to obtain a plurality of road evaluation parameter sets; The vehicle performance information includes driving bump constraint, driving curvature constraint and driving slope constraint; Using the vehicle performance information to traverse the multiple road evaluation parameter sets for data screening to obtain multiple candidate evaluation parameter sets; The plurality of candidate evaluation parameter sets are used as road driving matching references, a road matching degree evaluation function is introduced to perform road matching evaluation, and a plurality of road matching degrees are obtained; The multiple road matching degrees are serialized and extreme value calls are performed to obtain the target driving path.

4. The system according to claim 3, It is characterized in that The road matching evaluation function is as follows: ； in, is the road matching coefficient, is the road curvature weight, is the road slope weight, is the road bumpiness weight, is the mean road curvature, is the minimum curvature of the road, is the maximum curvature of the road, is the mean interval between adjacent curve sections, is the maximum distance between adjacent curve sections, is the mean road slope, is the minimum slope of the road, is the maximum slope of the road, is the mean interval between adjacent ramps, is the maximum spacing distance between adjacent ramps, is the average road bumpiness, is the minimum road bumpiness, is the maximum road bumpiness, is the mean interval between adjacent bumpy sections, It is the maximum distance between adjacent bumpy road sections.

5. The system according to claim 2, It is characterized in that The automatic control strategy module is also used to perform the following steps: Interactively obtain multiple sample brightness adjustment arrays, wherein each sample brightness adjustment array includes a sample environment brightness and a sample light control brightness; Performing data relationship analysis on the plurality of sample brightness adjustment arrays by using polynomial fitting to obtain a driving light control function; synchronizing the driving light control function to the driving light analysis module; The first local environment brightness data includes multiple instantaneous local environment brightnesses, the multiple instantaneous local environment brightnesses are serialized and extreme value extraction is performed, the extraction results are used as input of the driving light analysis module, the driving light calculation is performed through the driving light control function, and the first local light control strategy is obtained.

6. The system of claim 2, It is characterized in that The automatic control strategy module is also used to perform the following steps: In the driving obstacle avoidance analysis module, entity modeling is performed according to the first local obstacle avoidance entity data to obtain a first local obstacle model; Fitting the first local obstacle avoidance source data to the first local obstacle model to perform trajectory control judgment to obtain multiple obstacle avoidance nodes; Interactively obtaining a target turning radius and a target body width of the target tricycle, and combining the target turning radius and the target body width to obtain an obstacle avoidance trajectory radius; Generate a plurality of obstacle avoidance driving curves according to the obstacle avoidance trajectory radius and the plurality of obstacle avoidance nodes; Driving trajectory fitting is performed based on the multiple obstacle avoidance driving curves and the first local obstacle avoidance source data to generate the first local driving control strategy.

7. The system according to claim 6, It is characterized in that The automatic control strategy module is also used to perform the following steps: Smoothly connecting the multiple obstacle avoidance driving curves to the first local obstacle avoidance source data to obtain a first local optimized trajectory; Pre-constructing a curve-speed association table, and synchronizing multiple curves of multiple optimized curves in the first local optimization trajectory to the curve-speed association table to perform curve driving speed matching, and obtaining multiple optimized curve speed constraints; The standard driving speed of the target tricycle is interactively obtained, and the standard driving speed and the multiple optimized curvature speed constraints are smoothly fitted to the first local optimization trajectory to generate the first local driving control strategy.

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