CN112762954B - Path planning method and system - Google Patents
Path planning method and system Download PDFInfo
- Publication number
- CN112762954B CN112762954B CN202011561630.8A CN202011561630A CN112762954B CN 112762954 B CN112762954 B CN 112762954B CN 202011561630 A CN202011561630 A CN 202011561630A CN 112762954 B CN112762954 B CN 112762954B
- Authority
- CN
- China
- Prior art keywords
- road
- road section
- sampled
- section
- typical
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3461—Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
- Steering Control In Accordance With Driving Conditions (AREA)
Abstract
The invention provides a path planning method and a system, wherein the method comprises the following steps: firstly, determining n typical road sections; then h is 1; calculating the similarity between the h-th non-sampled road section and each typical road section based on a fuzzy reasoning method; calculating the speed weight corresponding to the h-th non-sampled road section according to the similarity of the h-th non-sampled road section and each typical road section; calculating the road impedance corresponding to the h-th non-sampled road section according to the speed weight corresponding to the h-th non-sampled road section; until h is equal to K, wherein K is the total number of the non-sampled road sections; and finally planning a driving path according to the road impedance corresponding to each non-sampled road section. The invention effectively avoids steep slopes and road sections with narrow visual fields based on the principle that the higher the road impedance is, the lower the safety is, and reduces the road passing chances with poor safety, thereby planning the driving path for safe trip of the driver on the mountain road.
Description
Technical Field
The present invention relates to the field of path planning technologies, and in particular, to a path planning method and system.
Background
In recent years, with the increasing of automobile holding capacity, road traffic accidents caused by motor vehicles in road traffic are increasing, and the road traffic safety situation is becoming more severe. The mountainous area road has complex environment, short protection, rare people and narrow road, and is a main place for traffic accidents. At present, how to plan a safe driving path in a complex environment in a mountainous area becomes a hot point of research in the traffic field.
Disclosure of Invention
The invention aims to provide a path planning method and a system to plan a safe driving path.
In order to achieve the above object, the present invention provides a path planning method, including:
s1: determining n typical road sections;
s2: let h equal to 1;
s3: calculating the similarity between the h-th non-sampled road section and each typical road section based on a fuzzy reasoning method;
s4: calculating the speed weight corresponding to the h-th non-sampled road section according to the similarity of the h-th non-sampled road section and each typical road section;
s5: calculating the road impedance corresponding to the h-th non-sampled road section according to the speed weight corresponding to the h-th non-sampled road section;
s6: judging whether h is smaller than K; if h is less than K, let h be h +1, and return to "S3"; if h is greater than or equal to K, "S7" is performed; k is the total number of the non-sampled road sections;
s7: and planning a driving path according to the road impedance corresponding to each non-sampled road section.
Optionally, the determining n typical road segments specifically includes:
s11: collecting a plurality of sample road sections; the sample road segment comprises m road attribute values and speed weights;
s12: classifying a plurality of sample road sections by using a random forest method and training a prediction model;
s13: calculating first speed weight of each non-sampling road section according to the prediction model;
s14: determining n representative road segments according to the first speed weight of each non-sampled road segment and the speed weights of a plurality of sample road segments.
Optionally, the similarity between the h-th non-sampled road segment and each typical road segment is calculated based on a fuzzy inference method, and a specific formula is as follows:
wherein, Pj,hJ-th road attribute value, S, representing h-th unsampled road segmenti,hRepresenting the similarity, k, of the h-th unsampled road segment to the i-th typical road segment1And w1Denotes a correction parameter, Pjt,hAnd j represents the j-th road attribute value of each typical road section corresponding to the h-th unexplored road section, and m is the number of the road attribute values.
Optionally, the speed weight corresponding to the h-th non-sampled road section is calculated according to the similarity between the h-th non-sampled road section and each typical road section, and the specific formula is as follows:
wherein, WhRepresents the speed weight corresponding to the h-th non-sampled road section, Si,hRepresents the similarity of the h-th non-sampled road section and the i-th typical road section, Wi,hAnd the speed weight of the ith typical road section corresponding to the h-th unexplored road section is shown.
Optionally, the road impedance corresponding to the h-th non-sampled road section is calculated according to the speed weight corresponding to the h-th non-sampled road section, and the specific formula is as follows:
wherein, length* hRepresents the road impedance, length, corresponding to the h-th unsampled road sectionhRepresents the road length, w, corresponding to the h-th unsampled road section* hRepresents the speed limit value W corresponding to the h-th non-sampled road sectionhAnd representing the speed weight corresponding to the h-th non-sampled road section.
Optionally, the planning of the driving path according to the road impedance corresponding to each non-sampled road segment specifically includes:
and selecting the road section with the minimum road impedance corresponding to each non-sampled road section as the driving path.
The invention also provides a path planning system, which comprises:
the typical road section determining module is used for determining n typical road sections;
the assignment module is used for enabling h to be 1;
the similarity determining module is used for calculating the similarity between the h-th non-sampled road section and each typical road section based on a fuzzy reasoning method;
the speed weight determining module is used for calculating the speed weight corresponding to the h-th non-sampling road section according to the similarity of the h-th non-sampling road section and each typical road section;
the road impedance determination module is used for calculating the road impedance corresponding to the h-th non-sampled road section according to the speed weight corresponding to the h-th non-sampled road section;
the judging module is used for judging whether h is smaller than K or not; if h is smaller than K, making h equal to h +1, and returning to the similarity determination module; if h is greater than or equal to K, executing a planning module; k is the total number of the non-sampled road sections;
and the planning module is used for planning the driving path according to the road impedance corresponding to each non-sampled road section.
Optionally, the typical road segment determining module further includes:
the acquisition unit is used for acquiring a plurality of sample road sections; the sample road segment comprises m road attribute values and speed weights;
the training unit is used for classifying the plurality of sample road sections by using a random forest method and training a prediction model;
the first speed weight calculation unit is used for calculating the first speed weight of each non-sampling road section according to the prediction model;
and the comparison unit is used for determining n typical road sections according to the first speed weight of each non-sampling road section and the speed weights of the plurality of sample road sections.
Optionally, the similarity between the h-th non-sampled road segment and each typical road segment is calculated based on a fuzzy inference method, and a specific formula is as follows:
wherein, Pj,hJ-th road attribute value, S, representing h-th unsampled road segmenti,hRepresenting the similarity, k, of the h-th unsampled road segment to the i-th typical road segment1And w1Denotes a correction parameter, Pjt,hAnd j represents the j-th road attribute value of each typical road section corresponding to the h-th unexplored road section, and m is the number of the road attribute values.
Optionally, the speed weight corresponding to the h-th non-sampled road section is calculated according to the similarity between the h-th non-sampled road section and each typical road section, and the specific formula is as follows:
wherein, WhRepresents the speed weight corresponding to the h-th non-sampled road section, Si,hRepresents the similarity of the h-th non-sampled road section and the i-th typical road section, Wi,hAnd the speed weight of the ith typical road section corresponding to the h-th unexplored road section is shown.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a path planning method and a system, wherein the method comprises the following steps: firstly, determining n typical road sections; then h is 1; calculating the similarity between the h-th non-sampled road section and each typical road section based on a fuzzy reasoning method; calculating the speed weight corresponding to the h-th non-sampled road section according to the similarity of the h-th non-sampled road section and each typical road section; calculating the road impedance corresponding to the h-th non-sampled road section according to the speed weight corresponding to the h-th non-sampled road section; until h is equal to K, wherein K is the total number of the non-sampled road sections; and finally planning a driving path according to the road impedance corresponding to each non-sampled road section. The invention effectively avoids steep slopes and road sections with narrow visual fields based on the principle that the higher the road impedance is, the lower the safety is, and reduces the road passing chances with poor safety, thereby planning the driving path for safe trip of the driver on the mountain road.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of a path planning method according to embodiment 1 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a path planning method and a system to plan a safe driving path.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example 1
Fig. 1 is a flowchart of a path planning method according to embodiment 1 of the present invention, and as shown in fig. 1, the path planning method includes:
s1: n representative road segments are determined.
S2: let h equal to 1.
S3: and calculating the similarity between the h-th non-sampled road section and each typical road section based on a fuzzy inference method.
S4: and calculating the speed weight corresponding to the h-th non-sampled road section according to the similarity of the h-th non-sampled road section and each typical road section.
S5: and calculating the road impedance corresponding to the h-th non-sampled road section according to the speed weight corresponding to the h-th non-sampled road section.
S6: judging whether h is smaller than K; if h is less than K, let h be h +1, and return to "S3"; if h is greater than or equal to K, "S7" is performed; wherein K is the total number of the non-sampled road sections.
S7: and planning a driving path according to the road impedance corresponding to each non-sampled road section.
In the embodiment of the present invention, the determining n typical road segments specifically includes:
s11: collecting a plurality of sample road sections; the sample road segment includes m road attribute values and speed weights. In the embodiment of the present invention, a total of 242 sample road segments are collected, where each sample road segment includes 4 road attribute values, and the road attribute values include: road grade factor, visual field factor, road grade factor and road waviness factor. The samples were taken as in table 1.
TABLE 1
S12: and classifying the plurality of sample road sections by using a random forest method and training a prediction model. Wherein the road attribute value is an independent variable and the speed is a dependent variable. A partial sample link data example is shown in table 2.
Table 2 example of partial sample link data selection
S13: and calculating the first speed weight of each non-sampling road section according to the prediction model.
S14: determining n typical road sections according to the first speed weight of each non-sampling road section and the speed weights of a plurality of sample road sections; specifically, the speed weight of each sample road section is compared with the first speed weight of each non-sampled road section, and each sample road section within a set range is selected as a typical road section.
In the embodiment of the present invention, the similarity between the h-th non-sampled road segment and each typical road segment is calculated based on a fuzzy inference method, and a specific formula is as follows:
wherein, Pj,hJ-th road attribute value, S, representing h-th unsampled road segmenti,hRepresenting the similarity, k, of the h-th unsampled road segment to the i-th typical road segment1And w1Denotes a correction parameter, Pjt,hAnd j represents the j-th road attribute value of each typical road section corresponding to the h-th unexplored road section, and m is the number of the road attribute values.
In the embodiment of the present invention, the speed weight corresponding to the h-th non-sampled road section is calculated according to the similarity between the h-th non-sampled road section and each typical road section, and a specific formula is as follows:
wherein, WhRepresents the speed weight corresponding to the h-th non-sampled road section, Si,hRepresents the similarity of the h-th non-sampled road section and the i-th typical road section, Wi,hAnd the speed weight of the ith typical road section corresponding to the h-th unexplored road section is shown.
In the embodiment of the present invention, the road impedance corresponding to the h-th non-sampled road section is calculated according to the speed weight corresponding to the h-th non-sampled road section, and the specific formula is as follows:
wherein, length* hRepresents the road impedance, length, corresponding to the h-th unsampled road sectionhRepresents the road length, w, corresponding to the h-th unsampled road section* hRepresents the speed limit value W corresponding to the h-th non-sampled road sectionhAnd representing the speed weight corresponding to the h-th non-sampled road section.
In the embodiment of the present invention, the planning of the driving path according to the road impedance corresponding to each non-sampled road segment specifically includes:
and selecting the road section with the minimum road impedance corresponding to each non-sampled road section as the driving path.
Example 2
The invention also provides a path planning system, which comprises:
and the typical road section determining module is used for determining n typical road sections.
And the assignment module is used for enabling h to be 1.
And the similarity determining module is used for calculating the similarity between the h-th non-sampled road section and each typical road section based on a fuzzy reasoning method.
And the speed weight determining module is used for calculating the speed weight corresponding to the h-th non-sampled road section according to the similarity of the h-th non-sampled road section and each typical road section.
And the road impedance determination module is used for calculating the road impedance corresponding to the h-th non-sampled road section according to the speed weight corresponding to the h-th non-sampled road section.
The judging module is used for judging whether h is smaller than K or not; if h is smaller than K, making h equal to h +1, and returning to the similarity determination module; if h is greater than or equal to K, executing a planning module; wherein K is the total number of the non-sampled road sections.
And the planning module is used for planning the driving path according to the road impedance corresponding to each non-sampled road section.
In an embodiment of the present invention, the typical road segment determining module further includes:
the acquisition unit is used for acquiring a plurality of sample road sections; the sample road segment comprises m road attribute values and speed weights;
the training unit is used for classifying the plurality of sample road sections by using a random forest method and training a prediction model;
the first speed weight calculation unit is used for calculating the first speed weight of each non-sampling road section according to the prediction model;
the comparison unit is used for determining n typical road sections according to the first speed weight of each non-sampling road section and the speed weights of a plurality of sample road sections; specifically, the speed weight of each sample road section is compared with the first speed weight of each non-sampled road section, and each sample road section within a set range is selected as a typical road section.
In the embodiment of the present invention, the similarity between the h-th non-sampled road segment and each typical road segment is calculated based on a fuzzy inference method, and a specific formula is as follows:
wherein, Pj,hJ-th road attribute value, S, representing h-th unsampled road segmenti,hRepresenting the similarity, k, of the h-th unsampled road segment to the i-th typical road segment1And w1Denotes a correction parameter, Pjt,hAnd j represents the j-th road attribute value of each typical road section corresponding to the h-th unexplored road section, and m is the number of the road attribute values.
In the embodiment of the present invention, the speed weight corresponding to the h-th non-sampled road section is calculated according to the similarity between the h-th non-sampled road section and each typical road section, and a specific formula is as follows:
wherein, WhRepresents the speed weight corresponding to the h-th non-sampled road section, Si,hRepresents the similarity of the h-th non-sampled road section and the i-th typical road section, Wi,hAnd the speed weight of the ith typical road section corresponding to the h-th unexplored road section is shown.
The invention mainly analyzes influence factors (road attribute values) influencing the driving safety of the mountainous area road, provides a weight assignment method considering the driving safety based on the principle that the lower the safety is, the higher the road impedance is, and realizes the planning of the mountainous area path according to the road impedance. According to the method, the influence factor data and the safe driving speed data of a typical road section are sampled, the classification mapping relation between the safe driving speed and the influence factors is constructed based on the thought of random forest classification, the problem that a weight assignment function is constructed directly through the influence factors is solved, the fuzzy membership degree of a fuzzy inference method is applied to weight assignment of a non-sampled road section sample, and the result is more accurate and reasonable than that of a direct sampling random forest classification method. Example verification results show that the path recommended by the evaluation method considering the safety weight, provided by the invention, can effectively avoid steep slopes and narrow-view road sections, and reduce the road passing chance with poor safety, so that scientific reference is provided for safe travel of the driver on the mountain road. The invention judges the safety of the driving road section and obtains a more reasonable driving path. The selection of the road attribute value is open, factors influencing the driving safety can be incorporated into the road attribute value according to the actual condition of the research area, and the clearer the analysis of the factors influencing the driving safety of the road is, the safer and more reasonable path can be obtained. And aiming at different types of mountain road characteristics, factors which influence safety more comprehensively are disclosed, and further intensive research is worth.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (5)
1. A path planning method is characterized by comprising the following steps:
s1: determining n typical road sections;
s2: let h equal to 1;
s3: calculating the similarity between the h-th non-sampled road section and each typical road section based on a fuzzy reasoning method; the concrete formula is as follows:
wherein, Pj,hJ-th road attribute value, S, representing h-th unsampled road segmenti,hRepresenting the similarity, k, of the h-th unsampled road segment to the i-th typical road segment1And w1Denotes a correction parameter, Pjt,hRepresenting the jth road attribute value of each typical road section corresponding to the h unexplored road section, wherein m is the number of the road attribute values;
s4: calculating the speed weight corresponding to the h-th non-sampled road section according to the similarity of the h-th non-sampled road section and each typical road section; the concrete formula is as follows:
wherein, WhRepresents the speed weight corresponding to the h-th non-sampled road section, Si,hRepresents the similarity of the h-th non-sampled road section and the i-th typical road section, Wi,hRepresenting the speed weight of the ith typical road section corresponding to the h-th unexplored road section;
s5: calculating the road impedance corresponding to the h-th non-sampled road section according to the speed weight corresponding to the h-th non-sampled road section; the concrete formula is as follows:
wherein, length* hRepresents the road impedance, length, corresponding to the h-th unsampled road sectionhRepresents the road length, w, corresponding to the h-th unsampled road section* hRepresents the speed limit value W corresponding to the h-th non-sampled road sectionhRepresenting the speed weight corresponding to the h-th non-sampled road section;
s6: judging whether h is smaller than K; if h is less than K, let h be h +1, and return to "S3"; if h is greater than or equal to K, "S7" is performed; k is the total number of the non-sampled road sections;
s7: and planning a driving path according to the road impedance corresponding to each non-sampled road section.
2. The path planning method according to claim 1, wherein the determining n representative road segments specifically includes:
s11: collecting a plurality of sample road sections; the sample road segment comprises m road attribute values and speed weights;
s12: classifying a plurality of sample road sections by using a random forest method and training a prediction model;
s13: calculating first speed weight of each non-sampling road section according to the prediction model;
s14: determining n representative road segments according to the first speed weight of each non-sampled road segment and the speed weights of a plurality of sample road segments.
3. The path planning method according to claim 1, wherein the planning of the driving path according to the road impedance corresponding to each non-sampled section comprises:
and selecting the road section with the minimum road impedance corresponding to each non-sampled road section as the driving path.
4. A path planning system, characterized in that the path planning system comprises:
the typical road section determining module is used for determining n typical road sections;
the assignment module is used for enabling h to be 1;
the similarity determining module is used for calculating the similarity between the h-th non-sampled road section and each typical road section based on a fuzzy reasoning method; the concrete formula is as follows:
wherein, Pj,hJ-th road attribute value, S, representing h-th unsampled road segmenti,hRepresenting the similarity, k, of the h-th unsampled road segment to the i-th typical road segment1And w1Denotes a correction parameter, Pjt,hRepresenting the jth road attribute value of each typical road section corresponding to the h unexplored road section, wherein m is the number of the road attribute values;
the speed weight determining module is used for calculating the speed weight corresponding to the h-th non-sampling road section according to the similarity of the h-th non-sampling road section and each typical road section; the concrete formula is as follows:
wherein, WhRepresents the speed weight corresponding to the h-th non-sampled road section, Si,hRepresents the similarity of the h-th non-sampled road section and the i-th typical road section, Wi,hIndicates the h-th non-mining roadThe speed weight of the ith typical road section corresponding to the section;
the road impedance determination module is used for calculating the road impedance corresponding to the h-th non-sampled road section according to the speed weight corresponding to the h-th non-sampled road section; the concrete formula is as follows:
wherein, length* hRepresents the road impedance, length, corresponding to the h-th unsampled road sectionhRepresents the road length, w, corresponding to the h-th unsampled road section* hRepresents the speed limit value W corresponding to the h-th non-sampled road sectionhRepresenting the speed weight corresponding to the h-th non-sampled road section;
the judging module is used for judging whether h is smaller than K or not; if h is smaller than K, making h equal to h +1, and returning to the similarity determination module; if h is greater than or equal to K, executing a planning module; k is the total number of the non-sampled road sections;
and the planning module is used for planning the driving path according to the road impedance corresponding to each non-sampled road section.
5. The path planning system according to claim 4, wherein the representative segment determining module further comprises:
the acquisition unit is used for acquiring a plurality of sample road sections; the sample road segment comprises m road attribute values and speed weights;
the training unit is used for classifying the plurality of sample road sections by using a random forest method and training a prediction model;
the first speed weight calculation unit is used for calculating the first speed weight of each non-sampling road section according to the prediction model;
and the comparison unit is used for determining n typical road sections according to the first speed weight of each non-sampling road section and the speed weights of the plurality of sample road sections.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011561630.8A CN112762954B (en) | 2020-12-25 | 2020-12-25 | Path planning method and system |
| ZA2021/09285A ZA202109285B (en) | 2020-12-25 | 2021-11-19 | Mountain road safety path planning method and system based on fuzzy random forest algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011561630.8A CN112762954B (en) | 2020-12-25 | 2020-12-25 | Path planning method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112762954A CN112762954A (en) | 2021-05-07 |
| CN112762954B true CN112762954B (en) | 2021-11-02 |
Family
ID=75694402
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011561630.8A Active CN112762954B (en) | 2020-12-25 | 2020-12-25 | Path planning method and system |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN112762954B (en) |
| ZA (1) | ZA202109285B (en) |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE10314119A1 (en) * | 2003-03-28 | 2004-10-21 | Dieter Dr. Bastian | Process for determining an integral risk potential for a road user and device for carrying out the process |
| US8478499B2 (en) * | 2010-06-07 | 2013-07-02 | Ford Global Technologies, Llc | System and method for vehicle speed monitoring using historical speed data |
| CN104567898B (en) * | 2013-10-17 | 2017-09-12 | 中国移动通信集团公司 | A kind of traffic route planing method, system and device |
| CN105808554B (en) * | 2014-12-30 | 2019-09-24 | 高德软件有限公司 | A kind of track data matching process and device |
| WO2016132395A1 (en) * | 2015-02-18 | 2016-08-25 | Hitachi, Ltd. | Weight estimation system |
| CN106932839B (en) * | 2015-12-31 | 2019-01-18 | 中国石油天然气股份有限公司 | A kind of method and device of pair of rock density and rate curve adjustment |
| US10262530B2 (en) * | 2016-12-20 | 2019-04-16 | Cambridge Mobile Telematics | Determining customized safe speeds for vehicles |
| CN107230389A (en) * | 2017-07-26 | 2017-10-03 | 山西省交通科学研究院 | A kind of mountain area winding road safety pre-warning system and method |
| US10474916B2 (en) * | 2017-11-20 | 2019-11-12 | Ashok Krishnan | Training of vehicles to improve autonomous capabilities |
| CN108519095A (en) * | 2018-03-08 | 2018-09-11 | 杭州后博科技有限公司 | A kind of the guidance path danger coefficient computing system and method for combination geographical feature |
| CN111798039B (en) * | 2020-06-12 | 2022-08-16 | 河海大学 | Student pick-up auxiliary system |
-
2020
- 2020-12-25 CN CN202011561630.8A patent/CN112762954B/en active Active
-
2021
- 2021-11-19 ZA ZA2021/09285A patent/ZA202109285B/en unknown
Also Published As
| Publication number | Publication date |
|---|---|
| CN112762954A (en) | 2021-05-07 |
| ZA202109285B (en) | 2022-03-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108430069B (en) | V2X network performance test and comprehensive evaluation analysis method | |
| CN110866677B (en) | Driver relative risk evaluation method based on benchmark analysis | |
| CN104809878B (en) | Method for detecting abnormal condition of urban road traffic by utilizing GPS (Global Positioning System) data of public buses | |
| CN111428960B (en) | Intelligent vehicle driving automatic evaluation method fusing multi-source vehicle-mounted sensor information | |
| EP0789341A1 (en) | Vehicle on board detection of traffic jam | |
| CN107953888B (en) | Road type identification method and system | |
| CN108001453B (en) | Method and system for identifying high-energy-consumption driving behaviors | |
| CN107038860B (en) | A kind of user's driving behavior methods of marking based on ADAS technology and regression model | |
| CN103198711B (en) | Vehicle regulating and controlling method of lowering probability of traffic accidents of different severity | |
| CN105644563A (en) | Evaluation method and system for accelerator transient condition driveability | |
| CN111695241A (en) | Method for determining length of accelerating lane of left-turn ramp confluence area based on VISSIM simulation | |
| CN109002622B (en) | Method for estimating overall load response of large-span bridge under random traffic flow effect | |
| CN113753059B (en) | Method for predicting takeover capacity of driver under automatic driving system | |
| CN111563555A (en) | Driver driving behavior analysis method and system | |
| CN112991685A (en) | Traffic system risk assessment and early warning method considering fatigue state influence of driver | |
| CN113870564A (en) | Traffic jam classification method and system for closed road section, electronic device and storage medium | |
| CN114550445B (en) | Urban area traffic safety state assessment method and device | |
| CN112762954B (en) | Path planning method and system | |
| CN114970943A (en) | Expressway accident type and severity prediction method | |
| CN109649399A (en) | A kind of driver distraction's method of discrimination based on lateral running speed | |
| CN113173175A (en) | Vehicle weight determination method and device | |
| CN116307699A (en) | Road hidden trouble point segment grading method, device and storage medium based on multi-source data | |
| CN109214582B (en) | Wind direction space correlation driven railway line wind speed prediction method | |
| CN113870560B (en) | Highway portrayal evaluation and safety risk research and judgment system based on multi-source data | |
| CN115497306A (en) | Speed interval weight calculation method based on GIS data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |