CN115880927A - Mountain complex road overtaking prediction method and system based on Internet of vehicles - Google Patents
Mountain complex road overtaking prediction method and system based on Internet of vehicles Download PDFInfo
- Publication number
- CN115880927A CN115880927A CN202211519325.1A CN202211519325A CN115880927A CN 115880927 A CN115880927 A CN 115880927A CN 202211519325 A CN202211519325 A CN 202211519325A CN 115880927 A CN115880927 A CN 115880927A
- Authority
- CN
- China
- Prior art keywords
- overtaking
- vehicle
- information
- road
- vehicles
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 58
- 238000011156 evaluation Methods 0.000 claims abstract description 149
- 238000004364 calculation method Methods 0.000 claims abstract description 48
- 230000006399 behavior Effects 0.000 claims description 43
- 230000001133 acceleration Effects 0.000 claims description 22
- 230000004888 barrier function Effects 0.000 claims description 15
- 230000006996 mental state Effects 0.000 claims description 11
- 230000000007 visual effect Effects 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 10
- 230000035484 reaction time Effects 0.000 claims description 8
- 238000004088 simulation Methods 0.000 claims description 8
- 230000009194 climbing Effects 0.000 claims description 7
- 230000003993 interaction Effects 0.000 claims description 6
- 230000006855 networking Effects 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 4
- 238000012360 testing method Methods 0.000 claims description 3
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 claims description 2
- 230000008569 process Effects 0.000 description 10
- 230000008713 feedback mechanism Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 239000000284 extract Substances 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000036314 physical performance Effects 0.000 description 2
- 239000004575 stone Substances 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 1
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Landscapes
- Traffic Control Systems (AREA)
Abstract
The application discloses a mountain complex road overtaking prediction method and system based on the internet of vehicles, which can automatically perform the security assessment of the advance overtaking calculation and assist the driver to safely select the overtaking opportunity according to the calculation result. The method comprises the following steps: acquiring the overtaking safety evaluation grade of the overtaking vehicle based on the overtaking evaluation calculation model according to the overtaking evaluation factors; when the evaluation level reaches primary safety or secondary safety, judging whether the overtaking vehicle is allowed under the current state of the overtaking vehicle, if so, obtaining the maximum speed and the minimum speed of the overtaking vehicle through repeated iterative trial calculation, and if not, carrying out overtaking warning prompt; when the overtaking vehicle is allowed to overtake in the current state and the driver selects overtaking, the overtaking speed is determined according to the maximum speed value and the minimum speed value of the overtaking vehicle for safe overtaking, the overtaking vehicle is controlled to overtake based on the overtaking speed, and the overtaking dynamic information of the overtaking vehicle is fed back to other vehicles on the overtaking vehicle running section in real time.
Description
Technical Field
The application relates to the technical field of vehicles, in particular to a mountain complex road overtaking prediction method and system based on Internet of vehicles.
Background
Intelligent networked vehicles (ICV) refer to a new generation of vehicles that organically combines a Vehicle networking system with an Intelligent Vehicle, carries advanced Vehicle-mounted sensors, controllers, actuators, and other devices, and integrates modern communication and network technologies, so as to realize Intelligent information exchange and sharing between vehicles and people, roads, backgrounds, and the like, and further, the vehicles are safe, comfortable, energy-saving, and efficient to drive, and can finally replace people to operate. However, under the background of complex road conditions that the linear indexes of mountain roads are poor, the longitudinal gradient changes greatly and the speed per hour of passing vehicles is different, in the driving process of the vehicles on the mountain roads, even though the vehicles are intelligent networked, the overtaking condition cannot be judged due to the driving sight distance blind area caused by complex curves or weather conditions, the passing efficiency is poor, traffic jam is easy to form, and traffic accidents occur frequently.
Disclosure of Invention
The application provides a mountain complex road overtaking prediction method and a mountain complex road overtaking prediction system based on the Internet of vehicles.
The specific technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a method for predicting a passing of a vehicle on a complex mountain road based on an internet of vehicles, including:
acquiring overtaking evaluation factors of overtaking vehicles, wherein the overtaking evaluation factors comprise road traffic real-time information flow in a safety range of the overtaking vehicles, real-time road physical information of a running road section of the overtaking vehicles, vehicle power parameters of the overtaking vehicles and driver factors, the real-time road physical information comprises real-time traffic control information and mountain road indexes, the mountain road indexes comprise longitudinal slope values, curve radiuses, ultrahigh transverse slopes, roadbed widths and lane distribution of mountains, and the real-time traffic control information comprises highest speed limit, lowest speed limit and limited driving information;
according to the road traffic real-time information flow, the real-time road physical information, the vehicle power parameters and the evaluation values of the driver factors, calculating to obtain a comprehensive overtaking behavior evaluation value based on an overtaking evaluation calculation model, and determining an overtaking safety evaluation level of the overtaking vehicle according to the comprehensive overtaking behavior evaluation value, wherein the preset overtaking safety evaluation level is divided into primary safety, secondary safety, primary danger and secondary danger, and the evaluation value of the real-time road physical information is the product sum of the longitudinal slope value, the curve radius, the ultrahigh transverse slope, the roadbed width, the lane distribution, the overtaking influence value of the highest speed limit and the influence weight coefficient;
when the safety evaluation level of the overtaking pre-calculation reaches primary safety or secondary safety, judging whether the overtaking vehicle is allowed under the current state of the overtaking vehicle according to the road traffic real-time information flow, the real-time road physical information and the vehicle power parameter, if so, obtaining the maximum speed value and the minimum speed value of the overtaking vehicle through multiple iterative trial calculations, and if not, carrying out overtaking warning prompt;
when the overtaking vehicle is allowed to overtake under the current state and the driver selects overtaking, determining the overtaking speed according to the maximum speed and the minimum speed of the overtaking vehicle, controlling the overtaking vehicle to overtake based on the overtaking speed, and simultaneously feeding the overtaking dynamic information of the overtaking vehicle back to other vehicles on the overtaking vehicle running section in real time, wherein the overtaking dynamic information comprises overtaking track information, overtaking speed and predicted overtaking time.
Optionally, the acquiring the overtaking evaluation factor of the overtaking vehicle specifically includes:
acquiring a dynamic three-dimensional traffic information stream within the safety range of the overtaking vehicle through a central cloud computing platform, wherein the dynamic three-dimensional traffic information stream comprises a track of a dynamic target and a fixed obstacle mark for traffic passing;
acquiring initial road physical information of a driving road section of the overtaking vehicle through a network end and a map end, wherein the initial road physical information comprises initial traffic control information and mountain road indexes;
acquiring roadside basic information of a driving road section of the overtaking vehicle through real-time interaction with a roadside device unit, wherein the roadside basic information comprises road information of the driving road section of the overtaking vehicle and basic information of a dynamic target, the road information comprises dynamic traffic control information, and the dynamic target comprises pedestrians, vehicles and other obstacles;
updating the dynamic three-dimensional traffic information stream in real time according to the roadside basic information to obtain the road traffic real-time information stream, updating the initial traffic control information in real time according to the dynamic traffic control information to obtain the real-time traffic control information, and generating the real-time road physical information according to the real-time traffic control information and the mountain road indexes, wherein the road traffic real-time information stream comprises the speed, the acceleration, the position and the direction of a traffic flow;
and extracting vehicle power parameters of the overtaking vehicle and driver factor information of a vehicle end control center, wherein the vehicle power parameters comprise the current vehicle speed, the highest vehicle speed, the acceleration performance and the maximum climbing gradient, and the driver factor information comprises the reaction time, the mental state and the driving time of a driver.
Further optionally, the acquiring, by the central cloud computing platform, the dynamic three-dimensional traffic information stream within the safe range of the passing vehicle specifically includes:
the roadside device unit acquires road information of a passing vehicle driving road section and basic information of vehicles, pedestrians and other obstacles in the passing vehicle driving road section and generates roadside basic information, wherein the basic information of the vehicles comprises driving directions, spatial positions, speeds, vehicle types and vehicle body sizes of the vehicles, the basic information of the pedestrians comprises spatial positions and moving directions of the pedestrians, and the basic information of the other obstacles comprises spatial positions, moving directions, sizes, types and speeds of the other obstacles;
the roadside equipment unit sends the roadside basic information to the central cloud computing platform and interacts with the overtaking vehicle in real time;
the central cloud computing platform deconstructs and analyzes the roadside basic information to obtain road information of a driving road section of the overtaking vehicle and basic information of the dynamic target, calculates the acceleration of the vehicle according to the basic information of the vehicle, and generates multi-dimensional information of the dynamic target according to the basic information of the dynamic target and the acceleration of the vehicle;
the central cloud computing platform builds a road section model according to road information of the overtaking vehicle driving road section, dynamically simulates vehicles, pedestrians and other barriers of the overtaking vehicle driving road section according to the road section model and the multi-dimensional information of the dynamic targets, builds a road traffic flow model, obtains tracks of the dynamic targets and fixed barrier marks of traffic passing in the overtaking vehicle safety range, and sends the fixed barrier marks to the overtaking vehicle;
and the overtaking vehicle receives the track of the dynamic target and the traffic passing fixed obstacle mark sent by the central cloud computing platform and generates a dynamic three-dimensional traffic information stream within the safety range of the overtaking vehicle.
Further optionally, the rating of the overtaking safety assessment level is configured to:
when the comprehensive evaluation value of the overtaking behaviors is not less than 90, classifying the overtaking safety evaluation level to primary safety;
when the comprehensive evaluation value of the overtaking behaviors is smaller than 90 but not smaller than 80, classifying the overtaking safety evaluation level into secondary safety;
when the overtaking behavior comprehensive evaluation value is smaller than 80 but not smaller than 60, classifying the overtaking safety evaluation level to a first-level danger;
and when the comprehensive evaluation value of the overtaking behaviors is less than 60, classifying the overtaking safety evaluation level into a secondary danger.
Still further optionally, the formula of the overtaking evaluation calculation model is as follows:
wherein y is the comprehensive evaluation value of overtaking behavior, x i Evaluation value for overtaking evaluation factor (i =1,2,3,4), ω i Evaluation value x for evaluation factor of overtaking i Corresponding weight coefficient (0 ≦ ω) i Less than or equal to 1), overtaking evaluation factor x i The specific calculation formula of (2) is as follows:
x 1 ＝ω 11 z 1 +ω 12 z 2 +ω 13 z 3 +ω 14 z 4
wherein x is 1 Evaluation of real-time information flows for road traffic, z 1 、z 2 、z 3 、z 4 The overtaking influence values omega of the speed, acceleration, position and direction of the traffic flow 11 、ω 12 、ω 13 、ω 14 Are each z 1 、z 2 、z 3 、z 4 A corresponding impact weight coefficient;
x 2 ＝ω 21 p 1 +ω 22 p 2 +ω 23 p 3 +ω 24 p 4 +ω 25 p 5 +ω 26 p 6
wherein x is 2 Evaluation value for real-time road physical information, p 1 、p 2 、p 3 、p 4 、p 5 、p 6 Respectively are longitudinal slope value, curve radius, ultrahigh transverse slope, roadbed width, lane distribution and overtaking influence value of highest speed limit, omega 21 、ω 22 、ω 23 、ω 24 、ω 25 、ω 26 Are each p 1 、p 2 、p 3 、p 4 、p 5 、p 6 A corresponding impact weight coefficient;
x 3 ＝ω 31 d 1 +ω 32 d 2 +ω 33 d 3 +ω 34 d 4
wherein x is 3 As an estimate of a vehicle dynamic parameter, d 1 、d 2 、d 3 、d 4 The overtaking influence values omega of the current vehicle speed, the highest vehicle speed, the acceleration performance and the maximum climbing gradient 31 、ω 32 、ω 33 、ω 34 Are respectively d 1 、d 2 、d 3 、d 4 A corresponding impact weight coefficient;
x 4 ＝ω 41 j 1 +ω 42 j 2 +ω 43 j 3
wherein x is 4 As an evaluation value of the driver factor, j 1 、j 2 、j 3 The overtaking influence values omega of the reaction time, the mental state and the driving duration of the driver 41 、ω 42 、ω 43 Are respectively j 1 、j 2 、j 3 Corresponding impact weight coefficients.
Optionally, the determining, according to the road traffic real-time information stream, the real-time road physical information, and the vehicle power parameter, whether the passing vehicle is allowed to pass under the current state specifically includes:
and according to the road traffic real-time information flow, the real-time road physical information and the vehicle dynamic parameters, carrying out repeated tests on the overtaking vehicle overtaking simulation scene for multiple times based on a visual simulation program, and determining whether the overtaking vehicle is allowed to overtake or not in the current state according to an output result of the visual simulation program, wherein the visual simulation program is a simulink simulation program.
Still further optionally, the other vehicles include an intelligent internet vehicle and a non-intelligent internet vehicle, and the real-time feedback of the overtaking dynamic information of the overtaking vehicle to the other vehicles on the overtaking vehicle driving road section specifically includes:
the overtaking vehicle sends the overtaking dynamic information to a central cloud computing platform in real time;
the central cloud computing platform receives the overtaking dynamic information, performs data preprocessing on the overtaking dynamic information, and updates the road traffic flow model according to the processed overtaking dynamic information;
when the other vehicles are intelligent network vehicles, the central cloud computing platform sends the updated road traffic flow model to other vehicles on the overtaking vehicle driving road section and prompts overtaking behaviors nearby the other vehicles;
when the other vehicles are non-intelligent networking vehicles, the central cloud computing platform records the dynamic tracks of the other vehicles, predicts the track states of the other vehicles, simultaneously sends the predicted track states of the other vehicles to the overtaking vehicles, and prompts the overtaking vehicles to whistle and warn the other vehicles.
Still further optionally, after the central cloud computing platform sends the updated road traffic flow model to other vehicles on the overtaking vehicle travel road segment, the method further includes:
and the other vehicles recalculate and adjust the overtaking safety evaluation level of the other vehicles according to the updated road traffic flow model.
In a second aspect, an embodiment of the present application provides a mountain area complex road overtaking prediction system based on internet of vehicles, including:
the system comprises a driving factor acquisition module, a driving factor acquisition module and a driving factor acquisition module, wherein the driving factor acquisition module is used for acquiring overtaking evaluation factors of overtaking vehicles, the overtaking evaluation factors comprise road traffic real-time information flow based on the safety range of the overtaking vehicles, real-time road physical information of the driving road sections of the overtaking vehicles, vehicle power parameters of the overtaking vehicles and driver factors, the real-time road physical information comprises real-time traffic control information and mountain road indexes, the mountain road indexes comprise longitudinal slope values, curve radiuses, ultrahigh transverse slopes, roadbed widths and lane distribution of mountains, and the real-time traffic control information comprises highest speed limit, lowest speed limit and restricted traffic information;
the overtaking precalculation module is used for calculating to obtain an overtaking behavior comprehensive evaluation value according to the road traffic real-time information flow, real-time road physical information, vehicle power parameters and evaluation values of driver factors and an overtaking evaluation calculation model, and determining an overtaking safety evaluation grade of the overtaking vehicle according to the overtaking behavior comprehensive evaluation value, wherein the preset overtaking safety evaluation grade is divided into primary safety, secondary safety, primary danger and secondary danger, and the evaluation values of the real-time road physical information are the product sum of a longitudinal slope value, a curve radius, an ultrahigh transverse slope, roadbed width, lane distribution, an overtaking influence value of the highest speed limit and an influence weight coefficient;
the overtaking simulation module is used for judging whether the overtaking vehicle is allowed under the current state according to the road traffic real-time information flow, the real-time road physical information and the vehicle power parameter when the overtaking pre-calculation safety evaluation level reaches primary safety or secondary safety, obtaining the maximum speed value and the minimum speed value of the overtaking vehicle in safety overtaking through repeated iterative trial calculation if the overtaking vehicle is allowed, and giving an overtaking warning prompt if the overtaking vehicle is not allowed;
the overtaking auxiliary module is used for determining overtaking speed according to the maximum speed value and the minimum speed value of the overtaking vehicle safety overtaking when the overtaking is allowed under the current state of the overtaking vehicle and the driver selects overtaking, and controlling the overtaking vehicle to overtake based on the overtaking speed;
and the overtaking feedback module is used for feeding dynamic overtaking information of the overtaking vehicle back to other vehicles on the overtaking vehicle running road section in real time after the overtaking is selected by a driver of the overtaking vehicle, wherein the dynamic overtaking information comprises overtaking track information, overtaking speed and predicted overtaking time.
In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when executed by a processor, the computer program implements the method according to the first aspect.
In a fourth aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory;
the processor is coupled to the memory for storing a computer program, which, when executed by the processor, causes the electronic device to carry out the method according to the first aspect.
In a fifth aspect, the present application provides a computer program product containing instructions which, when executed on a computer or processor, cause the computer or processor to perform the method according to the first aspect.
The technical effects of the application are as follows:
on one hand, various barrier information in a section of range can be searched through the road side equipment unit, including pedestrians, vehicles, other barriers and the like, data are continuously sent to intelligent networked vehicles running in a road section, comprehensive and dynamic road traffic continuous flow information is formed under the assistance of high-precision map and edge cloud computing, high precision and real-time performance are achieved, and the problem that in the prior art, a visual blind area caused by a curve or weather cannot be computed is solved; on the other hand, the method automatically performs edge cloud calculation on the basis of track prediction analysis of obstacle information, combines various overtaking influence factors such as physical performance parameters of the vehicle and linear conditions of mountainous roads, and sends the factors to the overtaking vehicles to obtain overtaking condition judgment, accurately calculates the road distance and time of opposite lanes required by overtaking, guides overtaking driving, and feeds the road distance and time to drivers of the overtaking vehicles in real time to prompt overtaking opportunity, overtaking behaviors required to be taken or overtaking danger warning, greatly improves safety of driving on mountainous roads, reduces accident occurrence probability, and improves traffic passing efficiency of mountainous roads; in addition, a set of overtaking feedback mechanism is constructed, after the driver selects overtaking, dynamic information of the overtaking feedback mechanism is captured by road side equipment and fed back to nearby vehicles in real time to carry out new overtaking calculation adjustment, so that the safety of the overtaking of the mountain road is further improved, meanwhile, calculation feedback is continuously carried out after the overtaking is finished, and reasonable system redundancy is adjusted.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a schematic flow chart of a mountain complex road overtaking prediction method based on the internet of vehicles according to an embodiment of the present application;
fig. 2 is a device composition diagram in a mountain complex road overtaking prediction method based on the internet of vehicles according to the embodiment of the present application;
FIG. 3 is a display diagram of an overtaking warning provided by an embodiment of the present application;
fig. 4 is a block diagram of a system for predicting passing vehicles on a complex mountain road based on internet of vehicles according to an embodiment of the present disclosure;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
The technical solution in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the described embodiments are merely a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without inventive step, are within the scope of the present disclosure.
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict. The terms "comprising" and "having" and any variations thereof in the embodiments and drawings of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the application discloses a mountain complex road overtaking prediction method based on a vehicle networking, which dynamically monitors overtaking intention and forms logic judgment to judge whether overtaking is available or not based on the surrounding environment factors and self factors of overtaking vehicles. The following are detailed below.
Fig. 1 is a mountain complex road overtaking prediction method based on the internet of vehicles, as shown in fig. 1, the method includes the following steps:
s110: and acquiring the overtaking evaluation factors of the overtaking vehicle.
In the embodiment of the application, the overtaking evaluation system of the mountainous area complex road overtaking prediction method based on the internet of vehicles mainly considers four major factors, specifically, the overtaking evaluation factors mainly include four major factors, namely a road traffic real-time information flow based on the safety range of the overtaking vehicle, real-time road physical information of a driving section of the overtaking vehicle, vehicle power parameters of the overtaking vehicle and driver factors, wherein the road traffic real-time information flow is mainly used for measuring whether pedestrians, vehicles and other obstacles in the surrounding environment of the overtaking vehicle can influence the overtaking behavior of the vehicle, the real-time road physical information and the vehicle power parameters are mainly used for measuring the overtaking capability of the overtaking vehicle in the mountainous area road section and whether the power condition of the vehicle has the overtaking capability at the current time, and the driver factors are mainly used for measuring the influence of the current state of the driver on the overtaking behavior, and the whole traffic participants are fully considered. Through the information acquisition of four major factors, an overtaking evaluation system can be formed through a set of logical algorithm, the overtaking behavior comprehensive evaluation is carried out on the current state of the overtaking vehicle, the driver is assisted to safely select the overtaking opportunity according to the evaluation result, the mountain area congestion is avoided, and the mountain area accident probability is reduced. It should be noted and understood that the "overtaking vehicle" in the embodiment of the present application refers to an intelligent internet vehicle, and is capable of performing real-time communication interaction with a roadside device unit, receiving multidimensional information sent by a central cloud computing platform, and performing overtaking signal interaction with the central cloud computing platform.
In one embodiment, as shown in fig. 2, the implementation of the mountain complex road overtaking prediction method based on the internet of vehicles depends on a vehicle-road-cloud integrated system composed of a roadside device unit, a central cloud computing platform and an intelligent internet vehicle in the background of vehicle-road cooperative intelligent internet system.
The roadside equipment units can be structurally arranged along the mountain road in the specific implementation process of the application, namely, the roadside equipment is arranged on the mountain road according to a fixed spacing distance, the type, the size, the height, the spacing distance and the like of the arranged roadside equipment are the same, the roadside equipment comprises but is not limited to sensors, cameras and other various sensing equipment, and the mountain road has clear road condition information such as road sidelines, central lines, speed limit instructions, intersection guidance and the like. The roadside device unit has the function of sensing vehicles, pedestrians and other obstacles in a mountain road, can acquire basic information of the vehicles such as the driving direction, the spatial position and the speed of the vehicles, can acquire basic information of the pedestrians and other obstacles, and can continuously transmit the acquired information to a driving intelligent internet vehicle and a central cloud computing platform under a mountain road intelligent internet monitoring platform through a vehicle wireless communication technology (V2X).
Specifically, the roadside device unit collects road information of a passing vehicle driving road section and basic information of vehicles, pedestrians and other obstacles in the passing vehicle driving road section, performs data preprocessing on the collected road information of the passing vehicle driving road section and basic information of dynamic targets such as vehicles, pedestrians and other obstacles in the passing vehicle driving road section to perform screening and structural processing of data, forms a uniform data format, generates roadside basic information according to processed data packaging, sends the roadside basic information to the central cloud computing platform, and performs real-time interaction with passing vehicles. The road information includes dynamic traffic control information, the dynamic target in the present application includes pedestrians, vehicles and other obstacles in the road section of the mountain area, the basic information of the vehicle specifically includes, but is not limited to, the driving direction, the spatial position, the speed, the vehicle type and the vehicle body size of the vehicle, the basic information of the pedestrians specifically includes, but is not limited to, the spatial position and the moving direction of the pedestrians, the basic information of the other obstacles specifically includes, but is not limited to, the spatial position, the moving direction, the size, the type and the speed of the other obstacles, for example, the other obstacles are road blocking stones, stone piles, passing animals and the like, it is noted and understood that the spatial position and the moving direction of the pedestrians and the other obstacles can be directly identified and obtained through a roadside device unit, the types and the sizes of the pedestrians and the other obstacles can be obtained through a picture identification technology, and the speeds of the pedestrians and the other obstacles can be calculated through the magnitude of displacement in unit time.
The central cloud computing platform in the embodiment of the application refers to an edge cloud computing platform, and after receiving roadside basic information, the central cloud computing platform can perform deconstruction analysis and calculate multi-dimensional information such as acceleration, vehicle types and vehicle body sizes of running vehicles based on data packets of roadside equipment units so as to build a three-dimensional traffic model of mountainous roads on the central cloud computing platform, and the built model can be used for displaying dynamic tracks of people, vehicles and logistics and marking fixed barriers influencing traffic.
Specifically, the central cloud computing platform deconstructs and analyzes the road-side basic information to obtain road information of a driving road section of the overtaking vehicle and basic information of the dynamic target, calculates and obtains acceleration of the vehicle according to the basic information of the vehicle, namely calculates the acceleration of each vehicle according to the change of the speed of the vehicle in unit time collected by the road-side equipment unit, and generates multidimensional information of the dynamic target according to the basic information of the dynamic target and the acceleration of the vehicle. Then, the central cloud computing platform builds a road section model according to road information of a driving road section of the overtaking vehicle, dynamically simulates vehicles, pedestrians and other barriers of the driving road section of the overtaking vehicle according to the road section model and multi-dimensional information of the dynamic targets, builds a road traffic flow model with tracks of the pedestrians, the vehicles and other barriers in a mountain road, forms a three-dimensional traffic model, meanwhile, updates in real time according to data sent by a road side equipment unit to obtain real-time tracks of the dynamic targets and fixed barrier marks of traffic passing in a safety range of the overtaking vehicle, and sends the real-time tracks and the fixed barrier marks of the traffic passing to the overtaking vehicle, so that the overtaking vehicle obtains dynamic three-dimensional traffic information flow comprising the tracks of the dynamic targets and the fixed barrier marks of the traffic passing in the safety range of the overtaking vehicle through the central cloud computing platform.
The overtaking vehicle receives the track of the dynamic target and the fixed obstacle mark of traffic passing sent by the central cloud computing platform, generates a dynamic three-dimensional traffic information stream within the overtaking vehicle safety range, and updates the dynamic three-dimensional traffic information stream in real time according to roadside basic information interacted with a roadside device unit in real time to obtain a road traffic real-time information stream, wherein the road traffic real-time information stream comprises but is not limited to the speed, the acceleration, the position and the direction of a traffic flow.
Meanwhile, the overtaking vehicle acquires initial road physical information of a driving road section of the overtaking vehicle through a network end and a map end, wherein the initial road physical information comprises initial traffic control information and mountain road indexes, the mountain road indexes comprise but are not limited to a longitudinal slope value, a curve radius, an ultrahigh transverse slope, roadbed width and lane distribution of a mountain road, and the initial traffic control information comprises traffic control information processed according to fusion of the network end and the map end, for example, traffic restriction information, speed limit information and the like of the mountain road in the high-precision map. And then, updating the initial traffic control information in real time according to dynamic traffic control information in the roadside basic information obtained by real-time interaction of the overtaking vehicle and the roadside device unit to obtain real-time traffic control information, and generating real-time road physical information according to the real-time traffic control information and the mountain road index, wherein the real-time traffic control information comprises but is not limited to highest speed limit, lowest speed limit and restriction information. In the embodiment of the application, the dynamic traffic control information collected by the roadside device unit is the current traffic control information of the mountain road, the initial traffic control information is updated through the dynamic traffic control information, the problem that the road traffic control information is inaccurate due to the fact that the network end and the map end are not updated timely is solved, more accurate data are provided for the safe overtaking behavior of subsequent overtaking vehicles, and the accuracy of the overtaking estimation result and the driving safety of the mountain road are improved.
In addition, the overtaking vehicle extracts the vehicle power parameters of the vehicle directly from the self database and extracts the driver factor information from the vehicle-side control center, wherein the vehicle power parameters are mainly used for measuring the vehicle dynamic property, including but not limited to the current vehicle speed, the highest vehicle speed, the acceleration performance and the maximum climbing gradient, and can be obtained by the vehicle self or a vehicle speed sensor, the driver factor information includes but not limited to the reaction time, the mental state and the driving time length of the driver, in the specific implementation process, the driving time length can be obtained by a timing device of the vehicle, and the reaction time and the mental state of the driver can be obtained by a machine learning model, for example, by taking a picture of the driver, and identifying through a mental state identification model after a large amount of training data to obtain the mental state of the driver at the current moment, and the identified mental state results include but not limited to fatigue, concentration and the like.
Therefore, the intelligent networked vehicles acquire a section of complete traffic real-time information flow of the mountain road based on the front and rear safety ranges of the vehicles through the roadside equipment units and the central cloud computing platform, and then combine with the mountain road indexes such as longitudinal slope values, curve radiuses, super-high transverse slopes, roadbed widths, lane distribution and the like and real-time traffic control information such as speed limit and restriction and the like acquired based on the network end and the map end, and extract vehicle power parameters and driver factor information of the vehicles, so that data are provided for overtaking prediction in subsequent steps.
S120: and calculating to obtain a comprehensive evaluation value of the overtaking behavior based on the overtaking evaluation calculation model according to the real-time information flow of the road traffic, the real-time road physical information, the vehicle power parameter and the evaluation value of the driver factor, and determining the overtaking safety evaluation level of the overtaking vehicle according to the comprehensive evaluation value of the overtaking behavior.
In the embodiment of the present application, after the overtaking evaluation factors of the overtaking vehicle are obtained by the overtaking vehicle in the step S110, the overtaking behavior evaluation of the overtaking vehicle is performed according to four major factors mainly affecting the overtaking behavior, that is, the corresponding road traffic real-time information stream, the real-time road physical information, the vehicle dynamic parameter, and the evaluation value of the driver factor are obtained according to the current vehicle driving environment, and then the overtaking behavior comprehensive evaluation value is obtained by calculation based on the overtaking evaluation calculation model, so as to determine the overtaking safety evaluation level of the overtaking vehicle according to the overtaking behavior comprehensive evaluation value, and thus, whether the current time is the appropriate safe overtaking opportunity is determined according to the evaluation result of the overtaking safety evaluation level.
In one embodiment, the evaluation value of the road traffic real-time information stream is the product of the overtaking influence value and the influence weight coefficient of the speed, the acceleration, the position and the direction of the traffic flow, namely the following calculation formula:
x 1 ＝ω 11 z 1 +ω 12 z 2 +ω 13 z 3 +ω 14 z 4
wherein x is 1 Evaluation of real-time information flows for road traffic, z 1 、z 2 、z 3 、z 4 The overtaking influence values omega of the speed, acceleration, position and direction of the traffic flow 11 、ω 12 、ω 13 、ω 14 Are each z 1 、z 2 、z 3 、z 4 Corresponding impact weight coefficients.
The evaluation value of the real-time road physical information is the product sum of the longitudinal slope value, the curve radius, the ultrahigh transverse slope, the roadbed width, the lane distribution, the overtaking influence value of the highest speed limit and the influence weight coefficient of the mountain road, namely the following calculation formula:
x 2 ＝ω 21 p 1 +ω 22 p 2 +ω 23 p 3 +ω 24 p 4 +ω 25 p 5 +ω 26 p 6
wherein x is 2 Evaluation value for real-time road physical information, p 1 、p 2 、p 3 、p 4 、p 5 、p 6 Respectively the longitudinal slope value, curve radius, super-high transverse slope, roadbed width, lane distribution and overtaking influence value of highest speed limit, omega 21 、ω 22 、ω 23 、ω 24 、ω 25 、ω 26 Are each p 1 、p 2 、p 3 、p 4 、p 5 、p 6 Corresponding impact weight coefficients.
The evaluation value of the vehicle power parameter is the product sum of the overtaking influence value and the influence weight coefficient of the current vehicle speed, the highest vehicle speed, the acceleration performance and the maximum climbing gradient, namely the following calculation formula:
x 3 ＝ω 31 d 1 +ω 32 d 2 +ω 33 d 3 +ω 34 d 4
wherein x is 3 As an estimate of a vehicle dynamic parameter, d 1 、d 2 、d 3 、d 4 The overtaking influence values omega of the current vehicle speed, the highest vehicle speed, the acceleration performance and the maximum climbing gradient 31 、ω 32 、ω 33 、ω 34 Are respectively d 1 、d 2 、d 3 、d 4 Corresponding impact weight coefficients.
The evaluation value of the driver factor is the product sum of the overtaking influence value and the influence weight coefficient of the reaction time, the mental state and the driving time of the driver, namely the following calculation formula:
x 4 ＝ω 41 j 1 +ω 42 j 2 +ω 43 j 3
wherein x is 4 As an evaluation value of the driver factor, j 1 、j 2 、j 3 The overtaking influence values omega of the reaction time, the mental state and the driving time of the driver 41 、ω 42 、ω 43 Are respectively j 1 、j 2 、j 3 Corresponding impact weight coefficients.
In the embodiment of the application, the contribution amounts of the road traffic real-time information flow, the real-time road physical information, the vehicle dynamic parameters and the driver factors to the overtaking comprehensive evaluation level are mutually independent, a linear model can be adopted, and specifically, the calculation formula of the overtaking evaluation calculation model is as follows:
wherein y is the comprehensive evaluation value of the overtaking behavior, x i Evaluation value for overtaking evaluation factor (i =1,2,3,4), ω i Evaluation value x for overtaking evaluation factor i Corresponding weight coefficient (0 ≦ ω) i ≤1)。
It should be noted and understood that the overtaking influence value of each overtaking evaluation factor and the weight coefficient corresponding to the influence weight coefficient or the evaluation value are calculated according to multiple experiments, for example, when the curve radius of the mountain road is smaller, the weight coefficient corresponding to the evaluation value of the real-time road physical information may be higher, that is, the influence of the real-time road physical information on the current overtaking safety is larger.
In another embodiment, the preset overtaking safety evaluation level is divided into primary safety, secondary safety, primary danger and secondary danger, when the overtaking safety evaluation level reaches the primary safety or the secondary safety, the overtaking is considered to be possible to carry out safety overtaking currently, calculation and judgment can be further carried out, if the overtaking safety evaluation level is the primary danger or the secondary danger, the overtaking is considered to be dangerous currently, overtaking warning prompt is directly carried out, and the overtaking of the vehicle is controlled not to be overtaken. In a specific implementation process, the classification of the overtaking safety evaluation level is configured as follows: when the comprehensive evaluation value of the overtaking behaviors is not less than 90, classifying the overtaking safety evaluation level to primary safety; when the comprehensive evaluation value of the overtaking behaviors is smaller than 90 but not smaller than 80, classifying the overtaking safety evaluation level into secondary safety; when the comprehensive evaluation value of the overtaking behaviors is smaller than 80 but not smaller than 60, classifying the overtaking safety evaluation level to a first-level danger; and when the comprehensive evaluation value of the overtaking behaviors is less than 60, classifying the overtaking safety evaluation level into a secondary danger.
S130: and when the passing pre-calculation safety evaluation level reaches primary safety or secondary safety, judging whether the passing vehicle is allowed to pass under the current state according to the road traffic real-time information flow, the real-time road physical information and the vehicle power parameter.
In the embodiment of the application, the passing pre-calculation safety evaluation level reaches primary safety or secondary safety, namely, no other obstacles such as vehicles and the like exist in front and back lanes and opposite lanes of a passing vehicle in the current state in the range of the mountain road, the passing preliminary permission is reached, then the current passing condition is calculated and whether passing can be performed is further specifically judged, namely whether the passing action can be completed within a specified time or not based on the power condition of the passing vehicle or whether the obstacles can appear in the safety range in the passing process or not is further judged, and specifically, a passing condition judgment result in the current state is calculated through a visual simulation program.
In one embodiment, according to road traffic real-time information flow, real-time road physical information and vehicle dynamic parameters, a visual simulation program is used for repeatedly testing a overtaking simulation scene of the overtaking vehicle for many times, whether overtaking is allowed in the current state of the overtaking vehicle is determined according to an output result of the visual simulation program, namely whether accidents such as collision, rollover and the like happen in the current state of the overtaking vehicle is judged through simulation, if the accidents do not happen, the overtaking condition judgment result is true, namely, overtaking is allowed, and if the overtaking condition judgment result is false, namely, overtaking is not allowed. Wherein, the visual simulation program can be a simulink simulation program.
If the overtaking vehicle is allowed in the current state, the step S130 is executed to the step S140; if the passing vehicle is not allowed to pass in the current state, the process proceeds from step S130 to step S160.
S140: and obtaining the maximum speed and the minimum speed of the overtaking vehicle through repeated iterative trial calculation.
And when the passing pre-calculation safety evaluation level reaches primary safety or secondary safety and the passing is allowed under the current state through simulation judgment, calculating to obtain a reasonable speed distribution interval for safe passing. Specifically, the maximum speed value and the minimum speed value of the overtaking vehicle safety overtaking are obtained through repeated iterative trial calculation, namely, the maximum speed value and the minimum speed value meeting the safety overtaking on the premise that the evaluation grade is safe are obtained step by using an iterative trial calculation method, so that the reasonable speed distribution interval of the safety overtaking is determined.
S150: when the overtaking vehicle is allowed to overtake in the current state and the driver selects overtaking, the overtaking speed is determined according to the maximum speed and the minimum speed of the overtaking vehicle, the overtaking vehicle is controlled to overtake based on the overtaking speed, and the overtaking dynamic information of the overtaking vehicle is fed back to other vehicles on the overtaking vehicle running section in real time.
In the embodiment of the application, when the overtaking vehicle is allowed in the current state and the driver selects overtaking, an overtaking speed is determined according to the maximum speed value and the minimum speed value of the overtaking vehicle for safe overtaking calculated in the step S140, so that the overtaking vehicle is controlled to safely overtake according to the overtaking speed.
Meanwhile, a set of overtaking feedback mechanism is further constructed in the embodiment of the application, after the driver selects overtaking, the dynamic information of the overtaking vehicle is captured by the road side equipment, and the overtaking dynamic information of the overtaking vehicle is fed back to other vehicles on the overtaking vehicle driving road section in real time, wherein the overtaking dynamic information comprises overtaking track information, overtaking speed and predicted overtaking time.
In one embodiment, the other vehicles comprise an intelligent networked vehicle and a non-intelligent networked vehicle, when the driver selects overtaking, the overtaking vehicle sends overtaking dynamic information to the central cloud computing platform in real time, and after the central cloud computing platform receives the overtaking dynamic information, data preprocessing is carried out on the overtaking dynamic information, so that a road traffic flow model is updated according to the processed overtaking dynamic information, and overtaking feedback is achieved accordingly. Specifically, when other vehicles in the mountain road are intelligent network-linked vehicles, the central cloud computing platform can send the updated road traffic flow model to other vehicles on the overtaking vehicle driving road section, prompt that overtaking behaviors exist near the other vehicles, and directly issue an early warning notification to prompt that overtaking behaviors exist near the vehicle, pay attention to driving avoidance, and further improve the safety of overtaking on the mountain road, so that dynamic information of the overtaking vehicles is fed back to the other vehicles in real time, the other vehicles perform new calculation and adjustment on the overtaking behaviors of the other vehicles, that is, once one vehicle in the mountain road preferentially sends an overtaking signal, the other vehicles immediately perform new overtaking calculation and adjustment, and simultaneously perform calculation and feedback after overtaking is completed, and the other vehicles recalculate and adjust the overtaking safety evaluation level of the other vehicles according to the updated road traffic flow model, and adjust reasonable system redundancy. And when other vehicles are non-intelligent networking vehicles, the central cloud computing platform records the dynamic tracks of the other vehicles, predicts the track states of the other vehicles in the next stage, builds traffic flow information, sends the predicted track states of the other vehicles to the overtaking vehicles under the condition of not influencing vehicle overtaking precomputation, and prompts the overtaking vehicles to whistle and warn the other vehicles. It should be noted that the other vehicles in the embodiment of the present application mainly include vehicles whose oncoming traffic or overtaking behaviors are located in front of and behind the overtaking vehicle.
S160: and carrying out overtaking warning prompt.
And when the passing pre-calculation safety evaluation level reaches primary safety or secondary safety, but the passing is not allowed under the current state, immediately giving a passing warning prompt so as to prompt a driver that the current state is not suitable for passing.
Corresponding to the above method embodiment, another embodiment of the present application provides a mountain complex road overtaking prediction system based on internet of vehicles, including:
the driving factor obtaining module 210 is configured to obtain overtaking evaluation factors of the overtaking vehicle, where the overtaking evaluation factors include a road traffic real-time information flow based on a safety range of the overtaking vehicle, real-time road physical information of a driving road section of the overtaking vehicle, vehicle power parameters of the overtaking vehicle, and driver factors, the real-time road physical information includes real-time traffic control information and mountain road indexes, the mountain road indexes include a longitudinal slope value, a curve radius, an ultrahigh transverse slope, a roadbed width, and lane distribution of a mountain road, and the real-time traffic control information includes a highest speed limit, a lowest speed limit, and restriction information.
The overtaking precalculation module 220 is used for calculating to obtain an overtaking behavior comprehensive evaluation value according to the real-time information flow of road traffic, real-time road physical information, vehicle power parameters and evaluation values of driver factors and based on an overtaking evaluation calculation model, and determining an overtaking safety evaluation level of the overtaking vehicle according to the overtaking behavior comprehensive evaluation value, wherein the preset overtaking safety evaluation level is divided into a primary safety, a secondary safety, a primary danger and a secondary danger, and the evaluation value of the real-time road physical information is the product sum of a longitudinal slope value, a curve radius, an ultrahigh transverse slope, a roadbed width, lane distribution, an overtaking influence value of the highest speed limit and an influence weight coefficient.
The overtaking simulation module 230 is configured to, when the overtaking pre-calculation safety evaluation level reaches primary safety or secondary safety, judge whether the overtaking of the overtaking vehicle is allowed in the current state according to the road traffic real-time information flow, the real-time road physical information and the vehicle power parameter, obtain a maximum vehicle speed and a minimum vehicle speed for the overtaking of the overtaking vehicle through multiple iterative trial calculations if the overtaking is allowed, and perform overtaking warning prompt if the overtaking is not allowed.
And the overtaking auxiliary module 240 is used for determining the overtaking speed according to the maximum speed and the minimum speed of the overtaking vehicle in the safe overtaking process when the overtaking is allowed under the current state of the overtaking vehicle and the driver selects overtaking, and controlling the overtaking vehicle to overtake based on the overtaking speed.
And the overtaking feedback module 250 is used for feeding dynamic overtaking information of the overtaking vehicle back to other vehicles on the overtaking vehicle driving road section in real time after the driver of the overtaking vehicle selects overtaking, wherein the dynamic overtaking information comprises overtaking track information, overtaking speed and predicted overtaking time.
In summary, the embodiment of the application provides a method and a system for predicting the overtaking of a complex mountain road based on the internet of vehicles, on one hand, various obstacle information in a section of range can be searched through a road side equipment unit, including pedestrians, vehicles, other obstacles and the like, data is continuously sent to an intelligent internet vehicle running in the road section, comprehensive and dynamic road traffic continuous flow information is formed under the assistance of a high-precision map and edge cloud computing, and the method and the system have high precision and real-time performance; on the other hand, the method automatically performs edge cloud computing on the basis of the track prediction analysis of the barrier information and a plurality of overtaking influence factors such as physical performance parameters of the vehicle and linear conditions of the mountain road, so as to obtain overtaking condition judgment and overtaking driving guidance, and feeds the overtaking condition judgment and overtaking driving guidance back to the driver of the overtaking vehicle in real time, so as to prompt overtaking opportunity, overtaking behavior required to be taken and overtaking danger warning, greatly improve the safety of driving on the mountain road, reduce the occurrence probability of accidents and improve the traffic efficiency of the mountain road; in addition, a set of overtaking feedback mechanism is constructed, after the driver selects overtaking, dynamic information of the overtaking feedback mechanism is captured by road side equipment and fed back to nearby vehicles in real time to carry out new overtaking calculation adjustment, so that the safety of the overtaking of the mountain road is further improved, meanwhile, calculation feedback is continuously carried out after the overtaking is finished, and reasonable system redundancy is adjusted.
Based on the above method embodiments, another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method as described in the above network-of-vehicles-based mountain area complex road overtaking prediction method embodiment.
Based on the above method embodiment, another embodiment of the present application provides an electronic device, as shown in fig. 5, the electronic device includes a processor 310 and a memory 320, the processor 310 is coupled with the memory 320, the memory 320 is used for storing a computer program, and the computer program is executed by the processor 310, so that the electronic device implements the method according to the above network-of-vehicles based mountain area complex road overtaking prediction method embodiment.
Based on the above embodiments, another embodiment of the present application provides a computer program product, which contains instructions that, when executed on a computer or a processor, cause the computer or the processor to execute the method according to the foregoing network-of-vehicles-based mountain area complex road overtaking prediction method embodiment.
The system embodiment corresponds to the method embodiment, and has the same technical effect as the method embodiment, and for the specific description, reference is made to the method embodiment. The system embodiment is obtained based on the method embodiment, and for specific description, reference may be made to the method embodiment section, which is not described herein again. Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or processes in the figures are not necessarily required to practice the present application.
Those of ordinary skill in the art will understand that: modules in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, or may be located in one or more devices different from the embodiments with corresponding changes. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the above embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A mountain complex road overtaking prediction method based on Internet of vehicles is characterized by comprising the following steps:
acquiring overtaking evaluation factors of overtaking vehicles, wherein the overtaking evaluation factors comprise road traffic real-time information flow in a safety range of the overtaking vehicles, real-time road physical information of a running road section of the overtaking vehicles, vehicle power parameters of the overtaking vehicles and driver factors, the real-time road physical information comprises real-time traffic control information and mountain road indexes, the mountain road indexes comprise longitudinal slope values, curve radiuses, ultrahigh transverse slopes, roadbed widths and lane distribution of mountains, and the real-time traffic control information comprises highest speed limit, lowest speed limit and limited driving information;
according to the road traffic real-time information flow, the real-time road physical information, the vehicle power parameters and the evaluation values of the driver factors, calculating to obtain a comprehensive overtaking behavior evaluation value based on an overtaking evaluation calculation model, and determining an overtaking safety evaluation level of the overtaking vehicle according to the comprehensive overtaking behavior evaluation value, wherein the preset overtaking safety evaluation level is divided into primary safety, secondary safety, primary danger and secondary danger, and the evaluation value of the real-time road physical information is the product sum of the longitudinal slope value, the curve radius, the ultrahigh transverse slope, the roadbed width, the lane distribution, the overtaking influence value of the highest speed limit and the influence weight coefficient;
when the overtaking pre-calculation safety evaluation level reaches primary safety or secondary safety, judging whether overtaking is allowed or not under the current state of the overtaking vehicle according to the road traffic real-time information flow, the real-time road physical information and the vehicle power parameter, if yes, obtaining the maximum speed value and the minimum speed value of the overtaking vehicle safety overtaking through repeated iterative trial calculation, and if not, carrying out overtaking warning prompt;
when the overtaking vehicle is allowed to overtake under the current state and the driver selects overtaking, determining the overtaking speed according to the maximum speed and the minimum speed of the overtaking vehicle, controlling the overtaking vehicle to overtake based on the overtaking speed, and simultaneously feeding the overtaking dynamic information of the overtaking vehicle back to other vehicles on the overtaking vehicle running section in real time, wherein the overtaking dynamic information comprises overtaking track information, overtaking speed and predicted overtaking time.
2. The method according to claim 1, wherein the obtaining of the overtaking evaluation factors of the overtaking vehicle specifically comprises:
acquiring a dynamic three-dimensional traffic information stream within the safety range of the overtaking vehicle through a central cloud computing platform, wherein the dynamic three-dimensional traffic information stream comprises a track of a dynamic target and a fixed obstacle mark for traffic passing;
acquiring initial road physical information of a driving road section of the overtaking vehicle through a network end and a map end, wherein the initial road physical information comprises initial traffic control information and mountain road indexes;
acquiring roadside basic information of a driving road section of the overtaking vehicle through real-time interaction with a roadside device unit, wherein the roadside basic information comprises road information of the driving road section of the overtaking vehicle and basic information of a dynamic target, the road information comprises dynamic traffic control information, and the dynamic target comprises pedestrians, vehicles and other obstacles;
updating the dynamic three-dimensional traffic information stream in real time according to the roadside basic information to obtain the road traffic real-time information stream, updating the initial traffic control information in real time according to the dynamic traffic control information to obtain the real-time traffic control information, and generating the real-time road physical information according to the real-time traffic control information and the mountain road indexes, wherein the road traffic real-time information stream comprises the speed, the acceleration, the position and the direction of a traffic flow;
and extracting vehicle power parameters of the overtaking vehicle and driver factor information of a vehicle end control center, wherein the vehicle power parameters comprise the current vehicle speed, the highest vehicle speed, the acceleration performance and the maximum climbing gradient, and the driver factor information comprises the reaction time, the mental state and the driving duration of a driver.
3. The method according to claim 2, wherein the obtaining of the dynamic stereoscopic traffic information stream within the safe range of the overtaking vehicle via the central cloud computing platform specifically comprises:
the road side equipment unit acquires road information of a driving road section of the overtaking vehicle and basic information of vehicles, pedestrians and other obstacles in the driving road section of the overtaking vehicle and generates road side basic information, wherein the basic information of the vehicles comprises driving directions, spatial positions, speeds, vehicle types and vehicle body sizes of the vehicles, the basic information of the pedestrians comprises spatial positions and moving directions of the pedestrians, and the basic information of the other obstacles comprises spatial positions, moving directions, sizes, types and speeds of the other obstacles;
the roadside equipment unit sends the roadside basic information to the central cloud computing platform and interacts with the overtaking vehicle in real time;
the central cloud computing platform deconstructs and analyzes the roadside basic information to obtain road information of a driving road section of the overtaking vehicle and basic information of the dynamic target, calculates the acceleration of the vehicle according to the basic information of the vehicle, and generates multi-dimensional information of the dynamic target according to the basic information of the dynamic target and the acceleration of the vehicle;
the central cloud computing platform builds a road section model according to road information of the overtaking vehicle driving road section, dynamically simulates vehicles, pedestrians and other barriers of the overtaking vehicle driving road section according to the road section model and the multi-dimensional information of the dynamic targets, builds a road traffic flow model, obtains tracks of the dynamic targets and fixed barrier marks of traffic passing in the overtaking vehicle safety range, and sends the fixed barrier marks to the overtaking vehicle;
and the overtaking vehicle receives the track of the dynamic target and the fixed obstacle mark of traffic passage sent by the central cloud computing platform and generates a dynamic three-dimensional traffic information stream within the safety range of the overtaking vehicle.
4. The method of claim 2, wherein the hierarchy of cut-in safety assessment levels is configured to:
when the comprehensive evaluation value of the overtaking behaviors is not less than 90, classifying the overtaking safety evaluation level to primary safety;
when the comprehensive evaluation value of the overtaking behaviors is smaller than 90 but not smaller than 80, classifying the overtaking safety evaluation level into secondary safety;
when the comprehensive evaluation value of the overtaking behaviors is smaller than 80 but not smaller than 60, classifying the overtaking safety evaluation level to a first-level danger;
and when the comprehensive evaluation value of the overtaking behaviors is less than 60, classifying the overtaking safety evaluation level into a secondary danger.
5. The method of claim 4, wherein the overtaking evaluation calculation model is calculated as follows:
wherein y is the comprehensive evaluation value of the overtaking behavior, x i Evaluation value for overtaking evaluation factor (i =1,2,3,4), ω i Evaluation value x for overtaking evaluation factor i Corresponding weight coefficient (0 ≦ ω) i Less than or equal to 1), overtaking evaluation factor x i The specific calculation formula of (2) is as follows:
x 1 ＝ω 11 z 1 +ω 12 z 2 +ω 13 z 3 +ω 14 z 4
wherein x is 1 Evaluation of real-time information flows for road traffic, z 1 、z 2 、z 3 、z 4 The overtaking influence values omega of the speed, acceleration, position and direction of the traffic flow 11 、ω 12 、ω 13 、ω 14 Are each z 1 、z 2 、z 3 、z 4 A corresponding impact weight coefficient;
x 2 ＝ω 21 p 1 +ω 22 p 2 +ω 23 p 3 +ω 24 p 4 +ω 25 p 5 +ω 26 p 6
wherein x is 2 Evaluation value for real-time road physical information, p 1 、p 2 、p 3 、p 4 、p 5 、p 6 Respectively are longitudinal slope value, curve radius, ultrahigh transverse slope, roadbed width, lane distribution and overtaking influence value of highest speed limit, omega 21 、ω 22 、ω 23 、ω 24 、ω 25 、ω 26 Are each p 1 、p 2 、p 3 、p 4 、p 5 、p 6 A corresponding impact weight coefficient;
x 3 ＝ω 31 d 1 +ω 32 d 2 +ω 33 d 3 +ω 34 d 4
wherein x is 3 As an estimate of a vehicle dynamic parameter, d 1 、d 2 、d 3 、d 4 The overtaking influence values omega of the current vehicle speed, the maximum vehicle speed, the acceleration performance and the maximum climbing gradient are respectively 31 、ω 32 、ω 33 、ω 34 Are respectively d 1 、d 2 、d 3 、d 4 A corresponding impact weight coefficient;
x 4 ＝ω 41 j 1 +ω 42 j 2 +ω 43 j 3
wherein x is 4 As an evaluation value of the driver factor, j 1 、j 2 、j 3 The overtaking influence values omega of the reaction time, the mental state and the driving duration of the driver 41 、ω 42 、ω 43 Are each j 1 、j 2 、j 3 Corresponding impact weight coefficients.
6. The method according to claim 1, wherein the determining whether the passing vehicle is allowed to pass under the current state according to the road traffic real-time information flow, the real-time road physical information and the vehicle dynamic parameter specifically comprises:
and according to the road traffic real-time information flow, the real-time road physical information and the vehicle dynamic parameters, carrying out repeated tests on the overtaking vehicle overtaking simulation scene for multiple times based on a visual simulation program, and determining whether the overtaking vehicle is allowed to overtake or not in the current state according to an output result of the visual simulation program, wherein the visual simulation program is a simulink simulation program.
7. The method according to claim 3, wherein the other vehicles comprise an intelligent networked vehicle and a non-intelligent networked vehicle, and the real-time feedback of the overtaking dynamic information of the overtaking vehicle to the other vehicles in the overtaking vehicle travel section comprises:
the overtaking vehicle sends the overtaking dynamic information to a central cloud computing platform in real time;
the central cloud computing platform receives the overtaking dynamic information, performs data preprocessing on the overtaking dynamic information, and updates the road traffic flow model according to the processed overtaking dynamic information;
when the other vehicles are intelligent network vehicles, the central cloud computing platform sends the updated road traffic flow model to other vehicles on the overtaking vehicle driving road section and prompts overtaking behaviors nearby the other vehicles;
when the other vehicles are non-intelligent networking vehicles, the central cloud computing platform records the dynamic tracks of the other vehicles, predicts the track states of the other vehicles, simultaneously sends the predicted track states of the other vehicles to the overtaking vehicles, and prompts the overtaking vehicles to whistle and warn the other vehicles.
8. The method of claim 7, wherein after the central cloud computing platform sends the updated road traffic flow model to other vehicles of the cut-in vehicle travel segment, the method further comprises:
and the other vehicles recalculate and adjust the overtaking safety evaluation level of the other vehicles according to the updated road traffic flow model.
9. The utility model provides a mountain area complex road prediction system that overtakes on basis of car networking which characterized in that includes:
the system comprises a driving factor acquisition module, a driving factor acquisition module and a driving factor acquisition module, wherein the driving factor acquisition module is used for acquiring overtaking evaluation factors of overtaking vehicles, the overtaking evaluation factors comprise road traffic real-time information flow based on the safety range of the overtaking vehicles, real-time road physical information of the driving road sections of the overtaking vehicles, vehicle power parameters of the overtaking vehicles and driver factors, the real-time road physical information comprises real-time traffic control information and mountain road indexes, the mountain road indexes comprise longitudinal slope values, curve radiuses, ultrahigh transverse slopes, roadbed widths and lane distribution of mountains, and the real-time traffic control information comprises highest speed limit, lowest speed limit and restricted traffic information;
the overtaking precalculation module is used for calculating to obtain an overtaking behavior comprehensive evaluation value according to the road traffic real-time information flow, real-time road physical information, vehicle power parameters and evaluation values of driver factors and an overtaking evaluation calculation model, and determining an overtaking safety evaluation grade of the overtaking vehicle according to the overtaking behavior comprehensive evaluation value, wherein the preset overtaking safety evaluation grade is divided into primary safety, secondary safety, primary danger and secondary danger, and the evaluation values of the real-time road physical information are the product sum of a longitudinal slope value, a curve radius, an ultrahigh transverse slope, roadbed width, lane distribution, an overtaking influence value of the highest speed limit and an influence weight coefficient;
the overtaking simulation module is used for judging whether the overtaking vehicle is allowed under the current state according to the road traffic real-time information flow, the real-time road physical information and the vehicle power parameter when the overtaking pre-calculation safety evaluation level reaches primary safety or secondary safety, obtaining the maximum speed and the minimum speed of the overtaking vehicle in a safe overtaking state through repeated iterative trial calculation if the overtaking vehicle is allowed, and carrying out overtaking warning prompt if the overtaking vehicle is not allowed;
the overtaking auxiliary module is used for determining overtaking speed according to the maximum speed value and the minimum speed value of the overtaking vehicle safety overtaking when the overtaking is allowed under the current state of the overtaking vehicle and the driver selects overtaking, and controlling the overtaking vehicle to overtake based on the overtaking speed;
and the overtaking feedback module is used for feeding back the overtaking dynamic information of the overtaking vehicle to other vehicles on the overtaking vehicle running road section in real time after the driver of the overtaking vehicle selects overtaking, wherein the overtaking dynamic information comprises overtaking track information, overtaking speed and predicted overtaking time.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211519325.1A CN115880927B (en) | 2022-11-30 | 2022-11-30 | Mountain area complex road overtaking prediction method and system based on Internet of vehicles |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211519325.1A CN115880927B (en) | 2022-11-30 | 2022-11-30 | Mountain area complex road overtaking prediction method and system based on Internet of vehicles |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115880927A true CN115880927A (en) | 2023-03-31 |
| CN115880927B CN115880927B (en) | 2024-04-12 |
Family
ID=85764906
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211519325.1A Active CN115880927B (en) | 2022-11-30 | 2022-11-30 | Mountain area complex road overtaking prediction method and system based on Internet of vehicles |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115880927B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101923788A (en) * | 2009-06-15 | 2010-12-22 | 爱信艾达株式会社 | Drive supporting device and program |
| CN106874597A (en) * | 2017-02-16 | 2017-06-20 | 北理慧动（常熟）车辆科技有限公司 | A kind of highway passing behavior decision-making technique for being applied to automatic driving vehicle |
| CN107618512A (en) * | 2017-08-23 | 2018-01-23 | 清华大学 | Driving behavior safe evaluation method based on people's car environment multi-data source |
| WO2018033389A1 (en) * | 2016-08-19 | 2018-02-22 | Continental Automotive Gmbh | Driver assistance system for passing operations |
| CN108909710A (en) * | 2018-04-25 | 2018-11-30 | 江苏大学 | It is a kind of to overtake other vehicles the driving assistance method and system of operating condition applied to piggybacking |
| JP2021152488A (en) * | 2020-03-24 | 2021-09-30 | 株式会社デンソーアイティーラボラトリ | Passing support device, passing support method, and passing support program |
-
2022
- 2022-11-30 CN CN202211519325.1A patent/CN115880927B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101923788A (en) * | 2009-06-15 | 2010-12-22 | 爱信艾达株式会社 | Drive supporting device and program |
| WO2018033389A1 (en) * | 2016-08-19 | 2018-02-22 | Continental Automotive Gmbh | Driver assistance system for passing operations |
| CN106874597A (en) * | 2017-02-16 | 2017-06-20 | 北理慧动（常熟）车辆科技有限公司 | A kind of highway passing behavior decision-making technique for being applied to automatic driving vehicle |
| CN107618512A (en) * | 2017-08-23 | 2018-01-23 | 清华大学 | Driving behavior safe evaluation method based on people's car environment multi-data source |
| CN108909710A (en) * | 2018-04-25 | 2018-11-30 | 江苏大学 | It is a kind of to overtake other vehicles the driving assistance method and system of operating condition applied to piggybacking |
| JP2021152488A (en) * | 2020-03-24 | 2021-09-30 | 株式会社デンソーアイティーラボラトリ | Passing support device, passing support method, and passing support program |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115880927B (en) | 2024-04-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109829386B (en) | Intelligent vehicle passable area detection method based on multi-source information fusion | |
| CN107346612B (en) | Vehicle anti-collision method and system based on Internet of vehicles | |
| US11084494B2 (en) | Method for detecting safety of driving behavior, apparatus, device and storage medium | |
| US11138745B2 (en) | Object association for autonomous vehicles | |
| EP3542237B1 (en) | Autonomous control of a motor vehicle on the basis of lane data; motor vehicle | |
| CN112700470B (en) | Target detection and track extraction method based on traffic video stream | |
| CN110562258B (en) | Method for vehicle automatic lane change decision, vehicle-mounted equipment and storage medium | |
| CN110304074B (en) | Hybrid driving method based on layered state machine | |
| CN112224211A (en) | Driving simulation system based on multi-autonomous-body traffic flow | |
| CN108583578A (en) | The track decision-making technique based on multiobjective decision-making matrix for automatic driving vehicle | |
| DE102016217645B4 (en) | Method for providing information about a probable driving intention of a vehicle | |
| CN107886750B (en) | Unmanned automobile control method and system based on beyond-visual-range cooperative cognition | |
| CN111653113A (en) | Method, device, terminal and storage medium for determining local path of vehicle | |
| CN113291308A (en) | Vehicle self-learning lane-changing decision-making system and method considering driving behavior characteristics | |
| US11615702B2 (en) | Determining vehicle path | |
| Qi et al. | Connected cooperative ecodriving system considering human driver error | |
| GB2620506A (en) | Homotopic-based planner for autonomous vehicles | |
| DE102021132722A1 (en) | SELECTING TEST SCENARIOS TO EVALUATE AUTONOMOUS VEHICLE PERFORMANCE | |
| CN104875740B (en) | For managing the method for following space, main vehicle and following space management unit | |
| CN115662166B (en) | Automatic driving data processing method and automatic driving traffic system | |
| CN113743469A (en) | Automatic driving decision-making method fusing multi-source data and comprehensive multi-dimensional indexes | |
| CN116434523A (en) | Vehicle active safety control method and device based on constraint degree in information perception scene | |
| CN105620486B (en) | Driving mode judgment means and method applied to vehicle energy management | |
| CN116128360A (en) | Road traffic congestion level evaluation method and device, electronic equipment and storage medium | |
| CN114926984A (en) | Real-time traffic conflict collection and road safety evaluation method |
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 |