Vehicle operation route planning method, device and equipment and storage medium

Abstract

The invention discloses a vehicle operation route planning method, device and equipment and a storage medium, and the method comprises the steps: collecting the index data related to the operation risk of an automatic driving vehicle on each road, and the index data comprises the road configuration data, the road surface condition data, the surrounding environment data and the traffic operation condition data; calculating a risk index score of each road section on each road by using the index data, and calculating a comprehensive risk index score of each road through the risk index scores; and according to the comprehensive risk index score, planning an optimal operation route of the autonomous vehicle. The method can identify the major hidden risk of the road, reduces the operation risk of the automatic driving vehicle, and effectively avoids the harm to the public safety.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving safety technology, and in particular to a vehicle operation route planning method, device, equipment and storage medium. Background Art

[0002] The technology behind autonomous vehicles has made significant progress. Many automakers and technology companies are actively developing autonomous driving technology and have already launched a number of autonomous vehicles at varying levels.

[0003] While meeting the testing and operational needs of autonomous vehicles and promoting the rapid development of autonomous driving technology, assessing operational risks and ensuring the safety of autonomous vehicle operations are key issues. Currently, road conditions and roadside facilities primarily serve Level 0-2 vehicles. Common road surface conditions and surrounding environments may present certain deficiencies, such as blurred or missing road markings and insufficient lighting. While these may not significantly impact human drivers, they present significant challenges for autonomous vehicles. Currently, autonomous vehicles must fully consider various factors, including road conditions and vehicle conditions, when selecting routes. They should avoid crowded areas such as schools, hospitals, and large supermarkets to minimize operational risks and avoid endangering public safety. Furthermore, the widespread adoption and application of autonomous driving technology will impact road traffic structure, public safety, and infrastructure, all of which require careful consideration when planning routes.

[0004] Therefore, how to plan the operating routes to reduce the operational risks of autonomous vehicles and effectively avoid endangering public safety. Summary of the Invention

[0005] The main purpose of the present invention is to provide a vehicle operation route planning method, device, equipment and storage medium, which can identify major road risk hazards, reduce the operation risks of autonomous driving vehicles, and effectively avoid endangering public safety.

[0006] In a first aspect, the present application provides a vehicle operation route planning method, the method comprising the steps of:

[0007] Collecting indicator data related to the operational risks of autonomous vehicles on each road, including: road configuration data, road surface condition data, surrounding environment data, and traffic operation status data;

[0008] Calculating a risk index score for each road section on each road using the indicator data, and calculating a comprehensive risk index score for each road using the risk index scores;

[0009] Based on the comprehensive risk index score, plan the optimal operating route for the autonomous driving vehicle.

[0010] In conjunction with the first aspect above, as an optional implementation method, the starting point and the end point of the autonomous driving vehicle are sent to a central processing unit, so that the central processing unit generates multiple preset routes;

[0011] Processing the road configuration data, road condition data, surrounding environment data, and road congestion data collected on each road section of the preset route to determine each evaluation indicator corresponding to the indicator data;

[0012] Determine the risk index level, weight, and influencing factor weight corresponding to each evaluation indicator by looking up the table;

[0013] The risk index score of the indicator data is calculated using the risk index level, weight, and influencing factor weights, and the risk index score of each road section on each road is calculated using the risk index score of the indicator data.

[0014] In combination with the first aspect above, as an optional implementation, according to the formula: Calculate the traffic operation risk index score, is the traffic operation status evaluation index, is the weight of traffic operation status evaluation index, q a is the weight of factors affecting traffic operation status;

[0015] According to the formula: Calculate the road configuration risk index score, is the road configuration evaluation index, is the road configuration evaluation index weight, q b is the weight of factors affecting road configuration;

[0016] According to the formula: Calculate the road condition risk index score, is the road condition evaluation index, is the weight of the road condition evaluation index, q c is the weight of factors affecting road conditions;

[0017] According to the formula: Calculate the surrounding environment risk index score, is the surrounding environment evaluation index, is the weight of the surrounding environment evaluation index, q d is the weight of the surrounding environment influencing factors;

[0018] According to the formula: S j =S a +S b +S c +S d, calculate the risk index score for each road section on each road.

[0019] In conjunction with the first aspect above, as an optional implementation method, a central processing unit is used to analyze the collected abnormal data of autonomous driving vehicles of different brands and models to analyze the evaluation indicators, number of accidents, and danger levels of accidents or abnormalities of autonomous driving vehicles of different brands and models;

[0020] Accident-related indicators of autonomous driving vehicles of different brands and models are graded, and indicators in the original risk assessment indicators that are not related to the accidents that have occurred are downgraded, and indicators related to the accidents are upgraded to revise the original risk assessment indicators and weights of different brands and models.

[0021] In combination with the first aspect above, as an optional implementation method, it is determined whether the comprehensive risk index score corresponding to each road is greater than a first threshold;

[0022] If it is greater than the first threshold, it is judged as a first-level risk road and marked;

[0023] If it is less than the first threshold, determine whether the comprehensive risk index score corresponding to each road is greater than the second threshold, and whether the road length exceeds the set length;

[0024] If the comprehensive risk index score corresponding to each road is greater than the second threshold and the road length exceeds the set length, it is determined to be a second-level risk road and marked, where the first level is greater than the second level;

[0025] If the comprehensive risk index score corresponding to each road is determined to be less than the second threshold and the road length does not exceed the set length, the remaining unmarked routes will be sorted from low to high according to the comprehensive risk index score, and the route with the smallest comprehensive risk score will be selected as the best operating route.

[0026] In combination with the first aspect above, as an optional implementation method, according to the formula: S = ∑ (S j ×l j ) / L, calculate the comprehensive risk index score of each road, where S j is the risk index score of the j-th road section, l j is the length of the jth section, and L is the total mileage of the operating route.

[0027] In conjunction with the first aspect above, as an optional implementation, road configuration data, road surface condition data, and surrounding environment data on each road are extracted based on video data acquired in real time by a camera installed on the outside of the autonomous vehicle and the acquired autonomous vehicle positioning data;

[0028] Using cameras installed at the entrances of each road, real-time information about vehicles on the target road is collected and recorded. This information is then combined with the video data and positioning data of the autonomous vehicle to determine the traffic conditions on each road.

[0029] The road configuration data includes: the distribution of dangerous roads such as signalized intersections, non-signalized intersections, intersections without pedestrian crossings, long downhill slopes, tunnels, public station distribution, and sharp turn points;

[0030] The road condition data includes: road section, road surface defect degree, lane width, marking clarity, traffic sign integrity rate, isolation facility defect, construction road width and roadside parking strip length;

[0031] The surrounding environment data includes: the number of points with poor visibility, the distribution of units along the route with dense pedestrian and vehicle traffic, the length of the bridge deck facing the water, road lighting strips, and the distribution of natural disasters;

[0032] The traffic operation conditions include: single lane flow, road section saturation, proportion of large vehicles, road design speed and road congestion.

[0033] In a second aspect, the present application provides a vehicle operation route planning device, which includes:

[0034] A collection module for collecting indicator data related to the operational risks of autonomous vehicles on various roads, wherein the indicator data includes: road configuration data, road surface condition data, surrounding environment data, and traffic operation status data;

[0035] a calculation module, which is used to calculate the risk index score of each road section on each road using the indicator data, and calculate the comprehensive risk index score of each road based on the risk index score;

[0036] A central processing unit is used to plan the optimal operating route of the autonomous driving vehicle based on the comprehensive risk index score.

[0037] In a third aspect, the present application further provides an electronic device comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method described in any one of the first aspects is implemented.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium storing computer program instructions, which, when executed by a computer, enables the computer to execute any one of the methods described in the first aspect.

[0039] This application provides a vehicle operation route planning method, apparatus, device, and storage medium, wherein the method comprises the following steps: collecting indicator data related to the operational risk of autonomous vehicles on various roads, wherein the indicator data includes: road configuration data, road surface condition data, surrounding environment data, and traffic operation status data; using the indicator data to calculate a risk index score for each road section, and using the risk index scores to calculate a comprehensive risk index score for each road; and planning the optimal operation route for the autonomous vehicle based on the comprehensive risk index score. This application can identify major road risk hazards, reduce the operational risk of autonomous vehicles, and effectively avoid endangering public safety.

[0040] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0042] Figure 1 A flow chart of a vehicle operation route planning method provided in an embodiment of the present application;

[0043] Figure 2 A schematic diagram of a vehicle operation route planning device provided in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of vehicle operation route planning provided in an embodiment of the present application;

[0045] Figure 4 A schematic diagram of an electronic device provided in an embodiment of the present application;

[0046] Figure 5 A schematic diagram of a computer-readable program medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0048] Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Some of the blocks shown in the drawings are functional entities that do not necessarily correspond to physically or logically separate entities.

[0049] The embodiments of the present application are further described in detail below with reference to the accompanying drawings.

[0050] Reference Figure 1 , Figure 1 The figure shows a flow chart of a vehicle operation route planning method provided by the present invention, as shown in FIG. Figure 1 As shown, the method includes the steps of:

[0051] Step S101: Collect indicator data related to the operational risks of autonomous driving vehicles on each road, wherein the indicator data includes: road configuration data, road surface condition data, surrounding environment data and traffic operation status data.

[0052] Specifically, based on the video data obtained in real time by the camera installed on the outside of the autonomous vehicle and the obtained autonomous vehicle positioning data, the road configuration data, road surface condition data and surrounding environment data on each road are extracted;

[0053] Using cameras installed at the entrances of each road, real-time information about vehicles on the target road is collected and recorded. This information is then combined with the video data and positioning data of the autonomous vehicle to determine the traffic conditions on each road.

[0054] The road configuration data includes: the distribution of dangerous roads such as signalized intersections, non-signalized intersections, intersections without pedestrian crossings, long downhill slopes, tunnels, public station distribution, and sharp turn points;

[0055] The road condition data includes: road section, road surface defect degree, lane width, marking clarity, traffic sign integrity rate, isolation facility defect, construction road width and roadside parking strip length;

[0056] The surrounding environment data includes: the number of points with poor visibility, the distribution of units along the route with dense pedestrian and vehicle traffic, the length of the bridge deck facing the water, road lighting strips, and the distribution of natural disasters;

[0057] The traffic operation conditions include: single lane flow, road section saturation, proportion of large vehicles, road design speed and road congestion.

[0058] For ease of understanding, a camera is installed at the entrance of each road, facing the direction of vehicle travel. It is used to collect and record vehicle information (vehicle type (large or small) and number) on the target road in real time. This vehicle information is combined with video data and positioning data from autonomous vehicles to determine the traffic status of each road. Specifically, road entrance security cameras and data processors are used to extract road traffic status parameters and combine them with camera data and positioning data uploaded by autonomous vehicles to determine the extent of road congestion. The central processing unit determines the data processing period: including the data processing start time and the duration of each period (e.g., starting at 00:00:00 daily, with each period lasting 15 minutes). This data processing period information is transmitted to the data processor via the communication module, and the data processor processes the traffic data for each road. (Optionally, a collection period can be selected. The cameras at the road entrance collect data in real time and upload it to the data processor, with 12-minute videos as a group. Traffic status data is processed and analyzed to determine the road vehicle flow and the number of large vehicles. The data values ​​collected in each period serve as the basis for selecting the road section after the 15-minute period.)

[0059] The central processing unit collects camera data and positioning data uploaded by autonomous driving vehicles in real time to determine whether the road is congested and the extent of the congestion during each data processing period.

[0060] The collection of road configuration data, road surface condition data, and surrounding environment data can be understood as having the autonomous vehicle travel all routes in advance to collect this data, which is dynamically updated regularly. It should be explained that each road can be understood as a route from point A to point B, and there are many possible paths.

[0061] The dynamic updates include: road configuration information collection: manual pre-collection of key urban road configuration information to form an original database, which is uploaded to the central processing unit for storage. At the same time, the central processing unit regularly processes the external camera data and positioning data uploaded by the vehicle to extract road configuration information (such as once a day), and updates it based on the pre-collected basic data.

[0062] Road Condition Information Collection: Road condition information on key urban roads is manually pre-collected to create a raw database, which is then uploaded to the central processing unit for storage. Simultaneously, the central processing unit regularly processes external camera data and positioning data uploaded by the vehicle to extract road condition information (e.g., once a day), and updates the pre-collected data. (It should be noted that road width is measured manually and must be re-measured if road markings are redrawn.)

[0063] Surrounding environment: The surrounding environment information of key urban roads is manually pre-collected to form a raw database, which is uploaded to the central processing unit for storage. At the same time, the central processing unit regularly processes the external camera data and positioning data uploaded by the vehicle to extract surrounding environment information (for example, once a day) and updates the pre-collected basic data. (Note: Natural disaster distribution includes floods (where lane lines cannot be clearly seen), landslides, blizzards, etc.)

[0064] In one embodiment, a camera is installed at the entrance of each road, facing the direction of vehicle travel, for real-time collection and recording of vehicle information on the target road.

[0065] The data processor, equipped with a communication module 1, is connected to the camera and is used to process the video data collected and uploaded by the camera, calculate the road traffic operation status (such as single lane flow, road section saturation, and the proportion of large vehicles), and upload the calculation results to the central processing unit through the communication module 2.

[0066] Autonomous driving operating vehicles: upload the vehicle's external camera data (including at least the front camera) and vehicle positioning data to the central processor through the on-board communication module 3, and upload the vehicle accident or driving abnormality data (positioning data, accident / abnormality type) to the central processor through the on-board communication module 3.

[0067] The central processing unit is equipped with a central communication module 2, which is used to receive the traffic operation status data calculated by the data processor transmitted by the roadside communication module; it is used to receive the vehicle positioning data and the video data of the external camera transmitted by the on-board communication module 3, and extract the configuration data, road condition data, surrounding environment data, and road congestion information of each road; it is used to summarize and dynamically update the operation risk indicator data of each road; it is used to receive the vehicle accident or driving abnormality data transmitted by the on-board communication module 3, extract the road section characteristics of each brand and model with accidents / abnormalities, and update the risk assessment indicators and weights.

[0068] Step S102: Calculate the risk index score of each road section on each road using the indicator data, and calculate the comprehensive risk index score of each road using the risk index score.

[0069] Specifically, the starting point and the end point of the autonomous vehicle are sent to the central processing unit so as to generate a plurality of preset routes through the central processing unit;

[0070] Processing the road configuration data, road condition data, surrounding environment data, and road congestion data collected on each road section of the preset route to determine each evaluation indicator corresponding to the indicator data;

[0071] Determine the risk index level, weight, and influencing factor weight corresponding to each evaluation indicator by looking up the table;

[0072] The risk index score of the indicator data is calculated using the risk index level, weight, and influencing factor weights, and the risk index score of each road section on each road is calculated using the risk index score of the indicator data.

[0073] For ease of understanding, let's take an example. The vehicle sends the starting point and destination to the central processor. The central processor combines the map to produce a preset feasible route, and performs risk assessments on each section (road) of the preset feasible route. The risk assessment of each road includes: collecting road configuration data, road condition data, surrounding environment data and road congestion data on each section, and processing them to determine each evaluation indicator corresponding to the indicator data. It needs to be explained that a route includes many roads, such as Qihang Road, Guangmao Road, Hong'an Road, etc. It also needs to be explained that Qihang Road, Guangmao Road and Hong'an Road can be understood as sections, that is, there are at least one or more sections on a route. After collecting the data on these road sections, namely road configuration data, road surface condition data, surrounding environment data and traffic operation status data, these data correspond to evaluation indicators. For example, traffic operation status indicators include: single-lane flow, road section saturation, proportion of large vehicles, road design speed and road congestion. The evaluation indicators corresponding to these data have corresponding risk index levels, weights and influencing factor weights. For example, one of the evaluation indicators in traffic operation status is the proportion of large vehicles, which has a first-level risk index, a second-level risk index, a third-level risk index, a fourth-level risk index, an indicator level, an indicator weight and an influencing factor weight (see Table 4).

[0074] The risk index score of the indicator data is calculated by the risk index level, weight and influencing factor weight, and the risk index score of each road section is calculated by the risk index score of the indicator data. Specifically, according to the formula: Calculate the traffic operation risk index score, is the traffic operation status evaluation index, is the weight of traffic operation status evaluation index, q a is the weight of factors affecting traffic operation status;

[0075] According to the formula: Calculate the road configuration risk index score, is the road configuration evaluation index, is the road configuration evaluation index weight, q b is the weight of factors affecting road configuration;

[0076] According to the formula: Calculate the road condition risk index score, is the road condition evaluation index, is the weight of the road condition evaluation index, q c is the weight of factors affecting road conditions;

[0077] According to the formula: Calculate the surrounding environment risk index score, is the surrounding environment evaluation index, is the weight of the surrounding environment evaluation index, q d is the weight of the surrounding environment influencing factors;

[0078] According to the formula: S j =S a +S b +S c +S d , calculate the risk index score of each road section on each road, where S j is the risk index score of the j-th road section, S a is the traffic operation risk index score, S b is the road configuration risk index score, S c is the road condition risk index score, S d It is the surrounding environment risk index score.

[0079] According to the formula: S=∑(S j ×l j ) / L, calculate the comprehensive risk index score of each road, where S j is the risk index score of the j-th road section, l j is the length of the jth road segment, and L is the total mileage of the operating route. It should be noted that an operating route often consists of one or more road segments. The safety risk of an operating route is quantified by weighting the risk scores of each road segment. The formula for calculating the comprehensive risk index score for an operating route is as follows. See Table 4 for the assessment criteria and grading method for determining the risk level of an operating route.

[0080] In one embodiment, there are multiple paths from point A to point B, for example, path A1 includes Qihang Road, Guangmao Road, and Hong'an Road. Road configuration data, road surface condition data, surrounding environment data, and road congestion data of Qihang Road, Guangmao Road, and Hong'an Road are collected and processed to determine each evaluation index corresponding to the index data, that is, to determine the evaluation index corresponding to the road configuration data, road surface condition data, surrounding environment data, and road congestion data of Qihang Road, Guangmao Road, and Hong'an Road. By looking up the table (Table 4), the risk index level, weight, and influencing factor weight corresponding to each evaluation index are determined;

[0081] The risk index score of the indicator data is calculated using the risk index level, weight, and influencing factor weights, and the risk index score of each road section on each road is calculated using the risk index score of the indicator data.

[0082] In one embodiment, a central processing unit is used to analyze the collected abnormal data of autonomous driving vehicles of different brands and models to analyze the evaluation indicators, number of accidents and danger levels of accidents or abnormalities of autonomous driving vehicles of different brands and models;

[0083] Accident-related indicators of autonomous driving vehicles of different brands and models are graded, and indicators in the original risk assessment indicators that are not related to the accidents that have occurred are downgraded, and indicators related to the accidents are upgraded to revise the original risk assessment indicators and weights of different brands and models.

[0084] It is understandable that in the initial stage of operation, different autonomous driving vehicles use the same original risk assessment indicators and weights (as shown in Table 4). During operation, road risk analysis is continuously conducted based on the actual operation of autonomous driving vehicles (such as operating accident / abnormal analysis and weight update once a year) to determine the operating risk indicators and weights of different vehicles, and update the weights in a timely manner.

[0085] (1) The central processing unit will classify and analyze the collected abnormal data of autonomous driving vehicles of different brands and models, analyze the characteristics of the road sections where accidents / abnormalities occurred for autonomous driving vehicles of each brand and model (evaluation indicators), and analyze the importance of each indicator (number of accidents, danger level).

[0086] (2) Accident-related indicators for autonomous vehicles of various brands and models will be graded, and the original risk assessment indicators and weights for each brand and model will be revised. Indicators not related to the accidents that have occurred will be downgraded, and indicators related to the accidents will be upgraded.

[0087] For example: Brand A vehicle had 2 accidents on section B and 4 accidents on section C.

[0088] ① Analysis of accidents / abnormal road characteristics of Brand A’s autonomous driving vehicles, as shown in Table 1, which is a table of accidents / abnormal road characteristics of Brand A’s autonomous driving vehicles.

[0089] Table 1

[0090]

[0091] ② Risk assessment indicator importance analysis and weight update:

[0092] Indicators are categorized and counted, and accident-related risk index scores are calculated based on the number of accidents and severity associated with each indicator (risk assessment index score = number of accidents * accident severity). Indicators are also graded (e.g., categorized into two levels). Indicators not related to existing accidents in the original risk assessment indicators (see Table 4) are downgraded to Level 1, while accident-related indicators are adjusted to "1 + accident indicator level." The accident-related indicator grade determination for Brand A's autonomous vehicle is shown in Table 2.

[0093] Table 2

[0094]

[0095] The adjusted evaluation index levels and weights of Brand A’s autonomous driving vehicles are shown in Table 3.

[0096] Table 3

[0097]

[0098]

[0099] Step S103: Planning the optimal operating route for the autonomous driving vehicle based on the comprehensive risk index score.

[0100] Specifically, determining whether the comprehensive risk index score corresponding to each road is greater than a first threshold;

[0101] If it is greater than the first threshold, it is judged as a first-level risk road and marked;

[0102] If it is less than the first threshold, determine whether the comprehensive risk index score corresponding to each road is greater than the second threshold, and whether the road length exceeds the set length;

[0103] If the comprehensive risk index score corresponding to each road is greater than the second threshold and the road length exceeds the set length, it is determined to be a second-level risk road and marked, where the first level is greater than the second level;

[0104] If the comprehensive risk index score corresponding to each road is determined to be less than the second threshold and the road length does not exceed the set length, the remaining unmarked routes will be sorted from low to high according to the comprehensive risk index score, and the route with the smallest comprehensive risk score will be selected as the best operating route.

[0105] In one embodiment, for example, the proportion of large vehicles in the collected traffic conditions of Guangmao Road is the second risk index, and the medium risk index is evaluated as 50 to 75 points. The indicator weight is 0.25, and the influencing factor weight is 0.25. The corresponding weights are shown in Table 4 (Road Safety Risk Assessment Indicator Weights). The risk index scores of all indicator data of Guangmao Road are calculated, that is, each evaluation indicator corresponding to all indicator data of Guangmao Road is calculated.

[0106] Table 4

[0107]

[0108]

[0109] The evaluation standards for each indicator are divided into four levels, with 25 points in each level. The higher the risk index, the higher the road risk affected by the indicator, among which:

[0110] Evaluation standard level 1, high risk, risk index is 75 points to 100 points (inclusive);

[0111] Evaluation standard level 2, medium risk, risk index is 50 points to 75 points (inclusive);

[0112] Evaluation standard level three, general risk, risk index is 25 points to 50 points (inclusive);

[0113] The evaluation standard level is four, low risk, and the risk index is 0 to 25 points (inclusive).

[0114] In summary, this application realizes the collection of key operational risk indicator data for autonomous driving roads, road operation risk analysis, risk analysis of each road, and the issuance of operation routes. It also enables real-time dynamic monitoring of operation routes and operating vehicle conditions to ensure vehicle operation safety.

[0115] Reference Figure 2 , Figure 2 FIG. 1 is a schematic diagram of a vehicle operation route planning device provided by the present invention, as shown in FIG. Figure 2 As shown, the device includes:

[0116] Collection module 201: It is used to collect indicator data related to the operational risks of autonomous driving vehicles on various roads, wherein the indicator data includes: road configuration data, road surface condition data, surrounding environment data and traffic operation status data.

[0117] Calculation module 202: It is used to calculate the risk index score of each road section on each road using the indicator data, and calculate the comprehensive risk index score of each road based on the risk index score.

[0118] Central processing unit 203: It is used to plan the optimal operation route of the autonomous driving vehicle based on the comprehensive risk index score.

[0119] Furthermore, in a possible implementation, the computing module is further configured to send the starting point and the end point of the autonomous driving vehicle to the central processing unit, so as to generate a plurality of preset routes via the central processing unit;

[0120] Processing the road configuration data, road condition data, surrounding environment data, and road congestion data collected on each road section of the preset route to determine each evaluation indicator corresponding to the indicator data;

[0121] Determine the risk index level, weight, and influencing factor weight corresponding to each evaluation indicator by looking up the table;

[0122] The risk index score of the indicator data is calculated using the risk index level, weight, and influencing factor weights, and the risk index score of each road section on each road is calculated using the risk index score of the indicator data.

[0123] Furthermore, in a possible implementation manner, the calculation module is further configured to calculate the value according to the formula: Calculate the traffic operation risk index score, is the traffic operation status evaluation index, is the weight of traffic operation status evaluation index, q a is the weight of factors affecting traffic operation status;

[0124] According to the formula: Calculate the road configuration risk index score, is the road configuration evaluation index, is the road configuration evaluation index weight, q b is the weight of factors affecting road configuration;

[0125] According to the formula: Calculate the road condition risk index score, is the road condition evaluation index, is the weight of the road condition evaluation index, q c is the weight of factors affecting road conditions;

[0126] According to the formula: Calculate the surrounding environment risk index score, is the surrounding environment evaluation index, is the weight of the surrounding environment evaluation index, q d is the weight of the surrounding environment influencing factors;

[0127] According to the formula: S j =Sa +S b +S c +S d , calculate the risk index score for each road section on each road.

[0128] Furthermore, in one possible implementation, the central processing unit is further configured to analyze, using the central processing unit, the collected abnormality data of autonomous driving vehicles of different brands and models to analyze the evaluation indicators, number of accidents, and danger levels of accidents or abnormalities of autonomous driving vehicles of different brands and models;

[0129] Accident-related indicators of autonomous driving vehicles of different brands and models are graded, and indicators in the original risk assessment indicators that are not related to the accidents that have occurred are downgraded, and indicators related to the accidents are upgraded to revise the original risk assessment indicators and weights of different brands and models.

[0130] Furthermore, in a possible implementation, the central processor is further configured to determine whether the comprehensive risk index score corresponding to each road is greater than a first threshold;

[0131] If it is greater than the first threshold, it is judged as a first-level risk road and marked;

[0132] If it is less than the first threshold, determine whether the comprehensive risk index score corresponding to each road is greater than the second threshold, and whether the road length exceeds the set length;

[0133] If the comprehensive risk index score corresponding to each road is greater than the second threshold and the road length exceeds the set length, it is determined to be a second-level risk road and marked, where the first level is greater than the second level;

[0134] If the comprehensive risk index score corresponding to each road is determined to be less than the second threshold and the road length does not exceed the set length, the remaining unmarked routes will be sorted from low to high according to the comprehensive risk index score, and the route with the smallest comprehensive risk score will be selected as the best operating route.

[0135] Furthermore, in a possible implementation manner, the calculation module is further configured to calculate the value of the product according to the formula: S = ∑(S j ×l j ) / L, calculate the comprehensive risk index score of each road, where S j is the risk index score of the j-th road section, l j is the length of the jth section, and L is the total mileage of the operating route.

[0136] Furthermore, in one possible implementation, the acquisition module is further configured to extract road configuration data, road surface condition data, and surrounding environment data on each road based on video data acquired in real time by a camera installed outside the autonomous vehicle and acquired autonomous vehicle positioning data;

[0137] Using cameras installed at the entrances of each road, real-time information about vehicles on the target road is collected and recorded. This information is then combined with the video data and positioning data of the autonomous vehicle to determine the traffic conditions on each road.

[0138] The road configuration data includes: the distribution of dangerous roads such as signalized intersections, non-signalized intersections, intersections without pedestrian crossings, long downhill slopes, tunnels, public station distribution, and sharp turn points;

[0139] The road condition data includes: road section, road surface defect degree, lane width, marking clarity, traffic sign integrity rate, isolation facility defect, construction road width and roadside parking strip length;

[0140] The surrounding environment data includes: the number of points with poor visibility, the distribution of units along the route with dense pedestrian and vehicle traffic, the length of the bridge deck facing the water, road lighting strips, and the distribution of natural disasters;

[0141] The traffic operation conditions include: single lane flow, road section saturation, proportion of large vehicles, road design speed and road congestion.

[0142] Reference Figure 3 , Figure 3 The diagram of the vehicle operation route planning provided by the present invention is shown as follows: Figure 3 As shown:

[0143] The vehicle sends its starting point and destination to the central processor, which combines the map to produce a preset feasible route, conducts risk assessments on each section (road) of the preset feasible route, comprehensively determines the risk level of each route, and forms a recommended operating route.

[0144] Determine whether there are high-risk sections in the preset route. If so, mark the route as unavailable for driving. If not, determine whether there are medium-risk sections in the preset route, and if the length exceeds 20% of the total factory route, mark the route as unavailable for driving. If so, sort the comprehensive risk values ​​of the drivable routes and determine the recommended order.

[0145] If there are high-risk sections in the preset operating route, or if there are more than 20% medium-risk sections, the preset route will not be accessible.

[0146] Refer to the following Figure 4 An electronic device 400 according to this embodiment of the present invention will be described. Figure 4 The electronic device 400 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0147] like Figure 4 As shown, electronic device 400 is implemented as a general-purpose computing device. Components of electronic device 400 may include, but are not limited to, at least one processing unit 410, at least one storage unit 420, and a bus 430 connecting various system components (including storage unit 420 and processing unit 410).

[0148] The storage unit stores program codes, which can be executed by the processing unit 410, so that the processing unit 410 performs the steps according to various exemplary embodiments of the present invention described in the above “Example Method” section of this specification.

[0149] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 421 and / or a cache memory unit 422 , and may further include a read-only memory unit (ROM) 423 .

[0150] The storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0151] Bus 430 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0152] The electronic device 400 may also communicate with one or more external devices (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 400, and / or any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication may occur via an input / output (I / O) interface 450. Furthermore, the electronic device 400 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 via a bus 430. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 400, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0153] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0154] According to the solution of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above-mentioned method of this specification is stored. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product, which includes program code. When the program product is executed on a terminal device, the program code is used to cause the terminal device to perform the steps according to various exemplary embodiments of the present invention described in the "Exemplary Methods" section of this specification.

[0155] refer to Figure 5 As shown, a program product 500 for implementing the above method according to an embodiment of the present invention is described. The program product 500 may be a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0156] The program product may be implemented in any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0157] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0158] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0159] The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0160] Furthermore, the above-described figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to be limiting. It is readily understood that the processes illustrated in the above-described figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0161] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily 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 is not limited to the embodiments shown herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

[0162] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

Claims

1. A vehicle operation route planning method, characterized in that: include: Collecting indicator data related to the operational risks of autonomous vehicles on each road, including: road configuration data, road surface condition data, surrounding environment data, and traffic operation status data; Calculating a risk index score for each road section on each road using the indicator data, and calculating a comprehensive risk index score for each road using the risk index scores; Based on the comprehensive risk index score, plan the optimal operating route for the autonomous driving vehicle.

2. The method according to claim 1, characterized in that The method of calculating the risk index score of each road section on each road using the indicator data includes: Sending the starting point and the ending point of the autonomous vehicle to the central processing unit to generate multiple preset routes through the central processing unit; Processing the road configuration data, road condition data, surrounding environment data, and road congestion data collected on each road section of the preset route to determine each evaluation indicator corresponding to the indicator data; Determine the risk index level, weight, and influencing factor weight corresponding to each evaluation indicator by looking up the table; The risk index score of the indicator data is calculated using the risk index level, weight, and influencing factor weights, and the risk index score of each road section on each road is calculated using the risk index score of the indicator data.

3. The method according to claim 2, characterized in that include: According to the formula: Calculate the traffic operation risk index score, is the traffic operation status evaluation index, is the weight of traffic operation status evaluation index, q a is the weight of factors affecting traffic operation status; According to the formula: Calculate the road configuration risk index score, is the road configuration evaluation index, is the road configuration evaluation index weight, q b is the weight of factors affecting road configuration; According to the formula: Calculate the road condition risk index score, is the road condition evaluation index, is the weight of the road condition evaluation index, q c is the weight of factors affecting road conditions; According to the formula: Calculate the surrounding environment risk index score, is the surrounding environment evaluation index, is the weight of the surrounding environment evaluation index, q d is the weight of the surrounding environment influencing factors; According to the formula: S j =S a +S b +S c +S d , calculate the risk index score for each road section on each road.

4. The method according to claim 2, characterized in that Also includes: The central processing unit is used to analyze the collected abnormal data of autonomous vehicles of different brands and models to analyze the evaluation indicators, number of accidents and danger levels of accidents or abnormalities of autonomous vehicles of different brands and models; Accident-related indicators of autonomous driving vehicles of different brands and models are graded, and indicators in the original risk assessment indicators that are not related to the accidents that have occurred are downgraded, and indicators related to the accidents are upgraded to revise the original risk assessment indicators and weights of different brands and models.

5. The method according to claim 1, wherein Planning the optimal operating route of the autonomous driving vehicle based on the comprehensive risk index score includes: Determine whether the comprehensive risk index score corresponding to each road is greater than a first threshold; If it is greater than the first threshold, it is judged as a first-level risk road and marked; If it is less than the first threshold, determine whether the comprehensive risk index score corresponding to each road is greater than the second threshold, and whether the road length exceeds the set length; If the comprehensive risk index score corresponding to each road is greater than the second threshold and the road length exceeds the set length, it is determined to be a second-level risk road and marked, where the first level is greater than the second level; If the comprehensive risk index score corresponding to each road is determined to be less than the second threshold and the road length does not exceed the set length, the remaining unmarked routes will be sorted from low to high according to the comprehensive risk index score, and the route with the smallest comprehensive risk score will be selected as the best operating route.

6. The method according to claim 1, wherein The comprehensive risk index score of each road is calculated using the risk index score, including: According to the formula: S=∑(S j ×l j ) / L, calculate the comprehensive risk index score of each road, where S j is the risk index score of the j-th road section, l j is the length of the jth section, and L is the total mileage of the operating route.

7. The method according to claim 1, characterized in that The collected indicator data related to the operational risks of autonomous vehicles on each road includes: Extracting road configuration data, road surface condition data, and surrounding environment data on each road based on real-time video data acquired by cameras installed on the outside of the autonomous vehicle and the acquired autonomous vehicle positioning data; Using cameras installed at the entrances of each road, real-time vehicle information on the target road is collected and recorded, and the vehicle information is combined with the video data and positioning data of the autonomous vehicle to determine the traffic operation status of each road; The road configuration data includes: the distribution of dangerous roads such as signalized intersections, non-signalized intersections, intersections without pedestrian crossings, long downhill slopes, tunnels, public station distribution, and sharp turn points; The road condition data includes: road section, road surface defect degree, lane width, marking clarity, traffic sign integrity rate, isolation facility defect, construction road width and roadside parking strip length; The surrounding environment data includes: the number of points with poor visibility, the distribution of units along the route with dense pedestrian and vehicle traffic, the length of the bridge deck facing the water, road lighting strips, and the distribution of natural disasters; The traffic operation conditions include: single lane flow, road section saturation, proportion of large vehicles, road design speed and road congestion.

8. A vehicle operation route planning device, characterized in that: include: A collection module for collecting indicator data related to the operational risks of autonomous vehicles on various roads, wherein the indicator data includes: road configuration data, road surface condition data, surrounding environment data, and traffic operation status data; a calculation module, which is used to calculate the risk index score of each road section on each road using the indicator data, and calculate the comprehensive risk index score of each road based on the risk index score; A central processing unit is used to plan the optimal operating route of the autonomous driving vehicle based on the comprehensive risk index score.

9. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer program instructions are stored therein, and when the computer program instructions are executed by a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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