A traffic accident road environment cause correlation analysis system involving assisted driving function

Abstract

This invention discloses a road environment causal correlation analysis system for traffic accidents involving assisted driving functions. This system belongs to the field of road safety and accident investigation. The system includes: a static road facility survey module for acquiring static traffic condition information; a dynamic traffic feature survey module for collecting dynamic traffic information; an instantaneous weather condition survey module for acquiring traffic meteorological environment monitoring information; a road environment factor analysis module for determining road environment interference factors involved in assisted driving traffic accidents; and a causal correlation result output module for determining the cause of the traffic accident. By comprehensively analyzing factors such as static road facilities, dynamic traffic features, and instantaneous weather conditions, the present invention provides a comprehensive traffic accident causal correlation analysis for assisted driving functions.

Description

Technical Field

[0001] The present invention belongs to the technical field of road safety and accident investigation, and in particular relates to a traffic accident road environment causal correlation analysis system involving an assisted driving function. Background Art

[0002] With the rapid development of the intelligent driving vehicle industry in recent years, traffic accidents involving assisted driving vehicles have become increasingly frequent. Unlike aviation, railway, and shipping accidents, investigations into assisted driving vehicle traffic accidents primarily rely on vehicle recording system data from assisted driving vehicle manufacturers and assisted driving technology providers to explain the accident process and causes. There is a lack of investigation and analysis of the road environment involved in the traffic accidents, making it difficult to fully restore the true accident process. Currently, domestic and international research on intelligent connected vehicle traffic accident investigations mainly focuses on the operational reliability of autonomous driving systems at level 3 and above, and the requirements for the stability of autonomous driving systems' perception of road environment factors. There is a lack of research on traffic accident investigations involving vehicles equipped with assisted driving functions below level 3, and autonomous driving vehicles at level 3 and above with some assisted driving functions enabled, particularly regarding the content and methods of investigating road environment interference factors. Compared with the causal methods of road traffic accidents involving traditional cars and self-driving cars, the investigation objects of car traffic accidents involving assisted driving functions are expanded to the assisted driving system and the human driver's operating behavior of the vehicle. The impact mechanism of road environment interference factors on the assisted driving system and human drivers is more complex. When different assisted driving functions are turned on in the car, the function has different perception requirements for road environment factors. Therefore, how to scientifically and systematically complete the causal analysis of road environment interference factors corresponding to different assisted driving function activation conditions, and support the rapid search for the causes of car traffic accidents involving assisted driving functions in the "human-machine mixed driving" mode and the accurate determination of accident responsibility, is a key issue that needs to be solved urgently. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention proposes a traffic accident road environment cause correlation analysis system involving an assisted driving function to solve the problems existing in the above-mentioned prior art.

[0004] To achieve the above objectives, the present invention provides a traffic accident road environment cause correlation analysis system involving an assisted driving function, comprising:

[0005] Road static facilities investigation module, traffic dynamic characteristics investigation module, weather instantaneous condition investigation module, road environment factor analysis module and cause-related result output module;

[0006] The road static infrastructure survey module is used to collect infrastructure information of the entire road and the local location where the assisted driving vehicle traffic accident occurred, as well as static traffic information displayed by traffic control facilities;

[0007] The traffic dynamic characteristics investigation module is used to collect the overall traffic flow operation characteristic information of the road where the accident occurred and the local traffic flow video image information at the accident location;

[0008] The instantaneous weather condition investigation module is used to collect local traffic meteorological environment monitoring information that interferes with the normal operation of the assisted driving function;

[0009] The road environment factor analysis module is connected to the road static facility investigation module, the traffic dynamic characteristic investigation module, and the weather instantaneous condition investigation module respectively. The road environment factor analysis module is used to perform data analysis on infrastructure information, static traffic information, traffic flow operation characteristic information, traffic flow video image information, and local traffic meteorological environment monitoring information to determine road environment interference factors involved in traffic accidents involving the assisted driving function;

[0010] The cause correlation result output module is connected to the road environment element analysis module, and the cause correlation result output module is used to determine the cause of the traffic accident based on the road environment interference element and output the accident cause analysis result.

[0011] Optionally, the road static facility survey module includes: a road attribute element collector, a road infrastructure element collector, a high-precision map information element collector, a signal control facility element collector, an information release facility element collector, and a traffic warning facility element collector;

[0012] The road attribute element collector is used to collect road geometry data interference value variables and road association relationship data interference value variables;

[0013] The road infrastructure element collector is used to collect the changing status of the basic traffic facility components;

[0014] The high-precision map information element collector is used to collect lane data interference value variables, lane attribute data interference value variables and digital traffic sign and marking data interference value variables;

[0015] The signal control facility element collector is used to collect the interference value variables of intersection signal light elements, lane traffic signal light element interference value variables, and special point segment signal light element interference value variables;

[0016] The information release facility element collector is used to collect variable information indicator board element interference value variables, variable speed limit sign screen element interference value variables, and service area guidance facility element interference value variables;

[0017] The traffic warning facility element collector is used to collect the interference value variables of the yellow flashing warning light element, the interference value variables of the severe weather warning device element, the interference value variables of the temporary safety warning light element, and the interference value variables of the traffic warning pile element.

[0018] Optionally, the road attribute element collector, road infrastructure element collector, signal control facility element collector, information release facility element collector and traffic warning facility element collector are all composite three-dimensional laser scanners.

[0019] Optionally, the traffic dynamic characteristics investigation module includes: a traffic flow element collector, a traffic event element collector and an important facility status element collector;

[0020] The traffic flow element collector is used to capture 360-degree panoramic images of road conditions and accident locations.

[0021] The traffic event element collector is used to monitor the event process that affects the normal traffic order on the road in real time and analyze the operating status of motor vehicles on the road and road condition information;

[0022] The important facility status element collector is used to monitor the status information of traffic facilities in different occasions.

[0023] Optionally, the instantaneous weather condition investigation module includes: a visibility collector, a road surface temperature collector, a road surface condition collector, a wind speed collector, a wind direction collector, and a precipitation collector;

[0024] The visibility collector is used to determine the meteorological optical range by emitting infrared pulse light to calculate the intensity of pulse light forward scattered by aerosol particles in the atmosphere;

[0025] The road surface temperature collector is used to determine the surface temperature of the road surface by measuring the infrared energy radiated by the road surface itself;

[0026] The road surface condition collector is used to detect the thickness of ice, snow and water on the road surface through multi-spectral measurement technology;

[0027] The wind speed collector determines the wind speed based on the ultrasonic sensor collector;

[0028] The wind direction collector determines the wind direction based on the ultrasonic sensor collector;

[0029] The precipitation collector is used to measure the size of raindrops and the precipitation speed.

[0030] Optionally, the road environment element analysis module is a VLIW server equipped with a road environment element interference model.

[0031] Optionally, the road environment element interference model is:

[0032]

[0033]

[0034] Where HJ i is the interference model of road environment factors, j is the weight coefficient of road environment factor i on the traffic accident of assisted driving vehicle, μ j is the weight coefficient of the road static facility elements, is the weight coefficient of traffic dynamic characteristic factors, ω j The weight coefficient of the instantaneous weather condition factor, i is the road environment factor of the assisted driving car traffic accident, M is the road environment factor collection, ∈ means belongs to, i∈M means that the road environment factor i belongs to the road environment factor collection M, f(RO i ) is the interference function of the static road facilities in the road environment element i, f(DT i ) is the interference function of traffic dynamic characteristic elements in road environment element i, f(TQ i ) is the interference function of the instantaneous weather condition factor in the traffic road environment factor i.

[0035] Optionally, the causal association result output module includes: a typical case causal association processor, a multi-case causal statistics display and a multi-case causal association processor;

[0036] The typical case cause association processor is used to analyze each traffic accident using the accident map method and output a typical case AcciMap analysis result map;

[0037] The multi-case cause statistics display is used to display the statistical results of several typical case AcciMap analysis result graphs;

[0038] The multi-case cause association processor is used to classify and merge the same road environment causes based on the statistical results, and output a final AcciMap statistical analysis result map.

[0039] Compared with the prior art, the present invention has the following advantages and technical effects:

[0040] The present invention provides a comprehensive traffic accident cause correlation analysis for the assisted driving function by comprehensively analyzing factors such as road static facilities, traffic dynamic characteristics, and instantaneous weather conditions. Through the road static facility investigation module, the system can collect basic road information and static traffic conditions of traffic control facilities to ensure the completion of a digital content investigation of the environmental infrastructure conditions of the road where the accident occurred; the traffic dynamic characteristics investigation module and the weather instantaneous condition investigation module further collect dynamic traffic information such as video image information and meteorological environment monitoring information to provide the system with multi-source data support such as video and images; the road environment element analysis module conducts a comprehensive analysis and processing of this information to determine the key factors that lead to traffic accidents due to interference from road environment conditions under different assisted driving function activation conditions. Finally, the cause correlation result output module determines the cause of the accident based on these factors, conducts accident cause correlation analysis and outputs the analysis results, providing method and system support for accident cause investigation, effectively improving the accuracy and timeliness of road environment cause investigations of traffic accidents involving assisted driving functions, and preventing and reducing traffic accidents involving assisted driving vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0042] Figure 1 This is a schematic structural diagram of a traffic accident road environment cause correlation analysis system involving an assisted driving function according to an embodiment of the present invention;

[0043] Figure 2 This is a typical accident AcciMap analysis diagram of a traffic accident road environment cause correlation analysis system involving an assisted driving function according to an embodiment of the present invention;

[0044] Figure 3 Schematic diagram of accident factors in a traffic accident road environment cause correlation analysis system involving an assisted driving function according to an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram summarizing multiple-case AcciMap analyses of a traffic accident road environment causal correlation analysis system involving an assisted driving function in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0047] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0048] Example 1

[0049] The present invention provides a road environment causal correlation analysis system for traffic accidents involving assisted driving functions. Based on the determination of vehicle and driver investigation content, once the necessary elements required by a certain assisted driving function for the road environment are determined, it supplements the investigation content of road environment interference elements related to assisted driving car traffic accidents in the "human-machine mixed driving" scenario, and performs correlation analysis on the elements of the road environment interference layer, providing content and method references for automobile traffic accident investigation and causal analysis involving assisted driving functions, so as to solve the problem that the road environment causes in traffic accidents involving vehicles with assisted driving functions cannot be correlated and analyzed and accurately identified.

[0050] like Figure 1 As shown, this embodiment provides a traffic accident road environment cause correlation analysis system involving an assisted driving function, the system comprising:

[0051] The road static facility investigation module 101, the traffic dynamic characteristics investigation module 102, the instantaneous weather condition investigation module 103, the road environmental factor analysis module 104, and the cause correlation result output module 105 are respectively connected to the road environmental factor analysis module 104, and the road environmental factor analysis module 104 is connected to the cause correlation result output module 105.

[0052] As a specific implementation of this embodiment, the road static facility investigation module 101 is used to investigate and collect basic information about the road where the assisted driving vehicle traffic accident occurred and static traffic condition information displayed by the traffic control facilities installed on the road, specifically including road information elements such as the geographical location, attributes, content, road network topology, road surface conditions, and road alignment of the road on which the assisted driving vehicle was traveling, as well as elements such as road traffic signs, traffic markings, traffic lights, and road traffic information display devices;

[0053] Traffic dynamic characteristics investigation module 102 is used to investigate information on other motor vehicles involved in the accident, non-motor vehicles involved in the accident, pedestrians involved in the accident, other traffic participants, and infrastructure status, mainly including micro-level traffic participant types, location trajectories, travel purposes, speeds, and macro-level road network traffic flow status, traffic events, traffic organization, and other traffic dynamic information;

[0054] The instantaneous weather condition investigation module 103 is used to investigate the traffic meteorological environment monitoring information of the impact of meteorological change factors on the driving safety of the assisted driving vehicle, mainly including factors such as visibility, road surface temperature, road surface condition, wind speed, wind direction, precipitation, and freezing degree;

[0055] The road environment factor analysis module 104 is used to define a road environment factor interference model, and analyze the data collected by the road static facility investigation module, the traffic dynamic characteristics investigation module, and the weather instantaneous condition investigation module through the road environment factor interference model to determine the road environment interference factors involved in traffic accidents involving assisted driving functions. The interference factors are used to determine the causes of traffic accidents; the cause correlation result output module 105 is used to output the accident cause analysis results, analyze each accident using the accident map (AcciMap) method, and perform cause correlation analysis and statistical display on typical cases and multiple cases.

[0056] Through the application of this system, according to the background of different assisted driving functions of intelligent driving vehicles when road traffic accidents occur, corresponding investigation contents such as road static facilities, traffic dynamic characteristics, and instantaneous weather conditions can be collected, and road environment factor investigation processes and methods covering accident evidence extraction, cause analysis, hidden danger tracing, and responsibility determination are proposed. This solves the problem of the lack of a road environment causal correlation analysis system for traffic accidents involving assisted driving functions in the "human-machine mixed driving" mode, and greatly improves the accuracy, scientificity and fairness of assisted driving car traffic accident investigations.

[0057] In some optional implementations, the road static facility survey module 101 provided by the present invention includes: a road attribute element collector 1011, a road infrastructure element collector 1012, a high-precision map information element collector 1013, a signal control facility element collector 1014, an information release facility element collector 1015, and a traffic warning facility element collector 1016.

[0058] The road attribute element collector 1011 is used to collect road geometry data interference value variables and road association data interference value variables. The road geometry data interference value is generally the change state of the shape of the right lane marking of the first lane on the left side along the direction of travel of the assisted driving car, generally the change state of the absolute accuracy and relative error of the plane position of the lane edge line, lane dividing line, and special vehicle dedicated lane line; the road association data interference value generally includes the road entrance and exit and road association data, and the road and navigation map association data, mainly including other information, generally the change state of the relevant data of road direction, road type, number of lanes, lane coding, ramp type, functional level and other attributes. Other attributes may include entrance and exit control, speed limit, traffic restriction and other related data according to actual conditions.

[0059] The road infrastructure element collector 1012 is used to collect the changing status of basic traffic facility components such as traffic signs, markings, traffic guardrails, etc. defined in the traffic infrastructure construction specifications, mainly including the changing status of traffic signs, traffic markings, guardrails, speed bumps, various roadside facilities, bridges, tunnels, toll stations, service areas, roadside buildings and other related road facility data.

[0060] The high-precision map information element collector 1013 is used to collect lane data interference value variables, lane attribute data interference value variables and digital traffic sign and marking data interference value variables. The lane data interference value variables include the change status of lane type, lane traffic status, lane traffic direction, lane number and lane restriction related data; the lane data interference value variables should include the change status of lane type, lane line color, lane line material, lane line width and lane line number related data; the digital traffic sign and marking data interference value variables include the change status of geographical location, applicable scope (road section scope, driving direction, lane, vehicle model), effective time, traffic sign and marking information (category, content information, additional instructions), verification information (verification of digital traffic sign and marking coding), etc.

[0061] The signal control facility element collector 1014 is used to collect three categories of signal light element interference value variables, namely, intersection signal light element interference value variables, lane traffic signal light element interference value variables, and special point segment signal light element interference value variables. Among them, the signal light element interference value variables at intersections are generally the changing states of signal lights set at intersections of ordinary highways and urban roads, including changes in red, green, yellow flashing warning lights, and U-turn signals; the lane traffic signal light element interference value variables are generally the changing states of lane traffic signal lights set at highway toll station entrances, expressway driving lanes, tunnels, bridges and other sections, including changes in red and green light signals; the signal light element interference value variables at special points are generally the changing states of warning signal lights set at special points such as ramp entrances and exits, tunnel entrances, and long downhill safety lane entrances, including changes in red, green, and yellow flashing warning lights.

[0062] The information release facility element collector 1015 is used to collect the interference value variables of the variable information indicator board element, the interference value variables of the variable speed limit sign screen element, and the interference value variables of the service area guidance facility element.

[0063] The traffic warning facility element collector 1016 is used to collect traffic warning facility element interference value variables, mainly including yellow flashing warning light element interference value variables, severe weather warning device element interference value variables, temporary safety warning light element interference value variables, and traffic warning pile element interference value variables.

[0064] As specific implementation methods of this embodiment, the road attribute element collector 1011, the road infrastructure element collector 1012, the signal control facility element collector 1014, the information release facility element collector 1015, and the traffic warning facility element collector 1016 are all composite three-dimensional laser scanners, which integrate infrared + blue laser technology to perform high-precision three-dimensional measurement of the surrounding environment at the accident location. The scanning area is 1440x860 mm, the maximum resolution is 0.01 mm, the scanning accuracy is 0.02 mm, and the volume accuracy is 0.03 mm / m. In high-speed scanning mode, the number of blue cross laser lines can reach 34 beams, and the scanning rate can reach up to 4,150,000 measurements / second. It is not easily affected by on-site vibration, temperature, humidity and other environmental factors, and is convenient for measurement anytime and anywhere. It has material adaptability and can easily capture three-dimensional data of highlights and black surfaces. The measurement results are comprehensive and reliable.

[0065] The high-precision map information element collector 1013 is an intelligent driving electronic map that contains a detailed lane model and positioning feature layers. It is used to assist in environmental perception, positioning, lane-level path planning, vehicle control, etc. The absolute accuracy of the high-precision map is not less than 1 meter, and the relative position accuracy is not less than 20 centimeters.

[0066] Through the road attribute element collector 1011, road infrastructure element collector 1012, high-precision map information element collector 1013, signal control facility element collector 1014, information release facility element collector 1015, and traffic warning facility element collector 1016 provided in the embodiment of the present invention, the auxiliary driving functions turned on by the vehicle before the traffic accident and the corresponding road static facility element data obtained are comprehensively collected, providing reliable and accurate data support for traffic accident analysis.

[0067] In some optional implementations, the traffic dynamic characteristics investigation module 102 provided by the present invention includes: a traffic flow element collector 1021 , a traffic event element collector 1022 , and an important facility status element collector 1023 .

[0068] The traffic flow element collector 1021 is a full-view video traffic flow information collection device. It uses high-definition camera technology to capture 360-degree panoramic images of road conditions and accident locations through cameras installed on the road. The traffic flow detection accuracy is not less than 98%.

[0069] The traffic event element collector 1022 is a video traffic event detection collector. After converting the camera video signal (digital or analog), it uses video-based motion object detection, target tracking and pattern recognition technology to monitor the event process that affects the normal traffic order on the road in real time, and analyze the operating status of motor vehicles on the road and road conditions. The detection accuracy of traffic congestion events is not less than 99%, the detection accuracy of vehicle stop events is not less than 99%, and the detection accuracy of vehicle wrong-way events is not less than 99%.

[0070] The Important Facility Status Element Collector 1023 is a multi-channel, highly integrated, low-power monitoring data acquisition device that can operate around the clock in various harsh environments and can meet the automated monitoring needs in various occasions such as roads, bridges, and tunnels.

[0071] In some optional embodiments, the instantaneous weather condition investigation module 103 provided by the present invention includes: a visibility collector 1031, a road surface temperature collector 1032, a road surface condition collector 1033, a wind speed collector 1034, a wind direction collector 1035, and a precipitation collector 1036.

[0072] The visibility collector 1031 consists of main components such as a light transmitter, a light receiver and a microprocessor controller. The transmitter emits infrared pulse light, and the receiver simultaneously detects the intensity of the pulse light forward scattered by aerosol particles in the atmosphere. All measurement information is collected by the microprocessor controller and converted into meteorological optical visibility through a special mathematical model algorithm.

[0073] The road surface temperature collector 1032 is an integrated infrared temperature measurement and detection collector. The optical system and electronic circuit are integrated in a metal shell. The interior is composed of a thermopile and a thermistor. It uses non-contact infrared temperature measurement technology to accurately determine the surface temperature of the road surface by measuring the infrared energy radiated by the road surface itself.

[0074] The road condition collector 1033 is a road condition detector based on remote sensing. It avoids the interference with traffic caused by the installation of road weather stations. Through multi-spectral measurement technology, it can accurately detect the thickness of ice, snow and water on the road surface. It can be installed on existing weather stations or on other buildings with unobstructed view of the road surface.

[0075] The wind speed collector 1034 and the wind direction collector 1035 are both ultrasonic sensor collectors that emit continuously variable frequency ultrasonic signals and detect wind speed and direction by measuring relative phase. The probe top cover is hidden to avoid interference from rain and snow accumulation; the wind speed collection range is between 0 and 60 meters per second (±0.1 meters per second), with a resolution of 0.01 meters per second; the wind direction collection range is between 0 and 360 degrees (±2 degrees), with a resolution of 1 degree.

[0076] The precipitation collector 1036 is a precipitation weather phenomenon meter. Based on the infrared measurement principle, it can automatically measure precipitation and accurately measure the size of raindrops and precipitation speed. The working principle is to actively emit infrared light bands and calculate the spectral distribution of the falling speed and size of precipitation particles by measuring the changes in the received light energy when precipitation particles pass through the light bands. It also automatically identifies and outputs precipitation weather phenomena based on the empirical model of the precipitation particle spectrum. The automatic recognition rate is not less than 95%. The collection range is 0.1 to 5 mm for liquid particle size, 0.1 to 25 mm for solid particle size, and 0.1 to 20 m / s for speed.

[0077] The embodiment of the present invention does not limit the form of the collector, and those skilled in the art can determine it according to actual needs.

[0078] In some optional embodiments, the road environment element analysis module 104 is a VLIW (Very Long Instruction Word) server equipped with a road environment element interference model. It adopts a "clear parallel instruction" design and can run 20 instructions per clock cycle. The advantage is that it simplifies the processor structure and eliminates many complex control circuits within the processor. It is particularly suitable for complex traffic accident analysis and high-density computing environments. Microprocessors based on this instruction architecture mainly include Intel's IA-64 and AMD's x86-64. The VLIW server includes multiple data access ports for respectively connecting to the road static facility investigation module 101, the traffic dynamic characteristics investigation module 102, and the weather instantaneous condition investigation module 103, so as to collect road environment element information when the assisted driving vehicle traffic accident occurs.

[0079] Furthermore, the VLIW server performs a road environment factor analysis that affects the assisted driving function based on the following road environment factor interference model:

[0080]

[0081]

[0082] Where HJ i is the interference model of road environment factors, j is the weight coefficient of road environment factor i on the traffic accident of assisted driving vehicle, μ j is the weight coefficient of the road static facility elements, is the weight coefficient of traffic dynamic characteristic factors, ω j The weight coefficient of the instantaneous weather condition factor, i is the road environment factor of the assisted driving car traffic accident, M is the road environment factor collection, ∈ means belongs to, that is, i∈M means that the road environment factor i belongs to the road environment factor collection M, f(RO i) is the interference function of the static road facilities in the road environment element i, f(DT i ) is the interference function of traffic dynamic characteristic elements in road environment element i, f(TQ i ) is the interference function of the instantaneous weather condition factor in the traffic road environment factor i.

[0083] As a specific implementation of this embodiment, HJi in this embodiment of the present invention is a set of factors used to summarize and describe various road environment factors, including various road environment factors related to the assisted driving function that cause traffic accidents, and the specific impact degree values ​​corresponding to each road environment factor. In this embodiment, the impact mechanism of road environment interference factors on the assisted driving system and human drivers is more complex. When the car turns on different assisted driving functions, the function has different perception requirements for road environment factors. The unsafe factors that lead to traffic accidents include static road facility factors, dynamic traffic feature factors, instantaneous weather conditions factors, etc. Since the importance of traffic accidents caused by different factors is different, the weight coefficient μ is used to calculate the impact of road environment factors on the assisted driving system and human drivers. j 、 and ω j To describe the proportion of each factor's impact on the traffic accident, that is, whether the above factors are included in this traffic accident, and if so, describe the corresponding importance.

[0084] In some optional implementations, the road static facility element interference function is calculated as follows:

[0085]

[0086] Where, f(SX i ) is the interference function of road attribute elements, f(SS i ) is the interference function of road infrastructure elements, f(GT i ) is the interference function of lane information elements in high-precision maps, f(XH i ) is the interference function of traffic signal control facilities, f(FB i ) is the interference function of traffic information release facilities, f(JS i ) is the interference function of traffic warning facility elements.

[0087] As a specific implementation of this embodiment, the traffic dynamic characteristic element interference function is calculated as follows:

[0088]

[0089] Where, f(JT i ) is the traffic flow element interference function, f(ST i ) is the interference function of traffic event factors, f(ZT i) is the interference function of important facility status factors.

[0090] In some optional implementations, the weather instantaneous condition factor interference function is calculated as follows:

[0091]

[0092] Where, f(EV i ) is the visibility interference function, f(ET i ) is the road surface temperature interference function, f(ER i ) is the road condition interference function, f(EW i ) is the wind speed interference function, f(ED i ) is the wind direction interference function, f(RA i ) is the precipitation interference function.

[0093] The definitions and summary of the variables of road environment interference factors are shown in Table 1.

[0094] Table 1

[0095]

[0096]

[0097]

[0098]

[0099] By calculating the interference value variables of the road environment factors, the present invention comprehensively considers the impact of various road environment factors on the satisfaction of the assisted driving function requirements and the occurrence of traffic accidents, determines more accurate unsafe factors, and further provides a reliability analysis basis for the causes of traffic accidents.

[0100] In some optional implementations, the causal association result output module 105 includes a typical case causal association processor 1051 , a multi-case causal statistics display 1052 , and a multi-case causal association processor 1053 .

[0101] Among them, the typical case cause association processor 1051, the multi-case cause statistical display 1052, and the multi-case cause association processor 1053 are communicatively connected to the road environment element analysis module 104; the typical case cause association processor 1051 uses the accident map (AccidentMap, AcciMap) method to analyze each traffic accident and outputs a typical case AcciMap analysis result diagram; the multi-case cause statistical display 1052 displays the statistical results of the AcciMap analysis of multiple cases through a liquid crystal display and numbers them; the multi-case cause association processor 1053 classifies and merges the same road environment causes and outputs the final AcciMap statistical analysis result diagram, using the size of the circle and the thickness of the connecting line to represent the number of occurrences of the road environment cause and the number of road environment cause connection relationships respectively. The AcciMap statistical analysis result diagram corresponds to the number in the multi-case cause statistical display.

[0102] Furthermore, the typical case cause association processor 1051 selects a typical assisted driving car traffic accident for analysis, such as Figure 2 As shown, the accident occurred in clear weather with good visibility. The driver of the assisted-driving vehicle was distracted for an extended period, causing the vehicle to exit navigation-assisted driving mode, which then automatically downgraded to adaptive cruise control. Driving through a road section with a steep outward slope at the guardrail end and a long distance between guardrail posts, the assisted-driving vehicle lost its guidance function and collided with the roadside guardrail at the outward end. An in-depth investigation and analysis of the traffic accident revealed that the safety awareness of the human driver of the assisted-driving vehicle (hereinafter referred to as the human driver), road markings, and the installation of roadside guardrails all indirectly influenced the accident.

[0103] Summarize all the accident participants in the accident elements, there are 25 types of participants, such as Figure 3 Among all participants, the road environment interference layer (40%) had the highest proportion of participants, followed by the enterprise management layer (32%), the department supervision layer (8%), the assisted driving vehicle technology layer (8%), and the accident process and driver operation layer (8%). The government decision-making layer (4%) had the lowest proportion of participants. The road environment interference layer and enterprise management layer (32%) accounted for 72% of the participants, indicating that in assisted driving vehicle traffic accidents, road environment interference and enterprise management factors have a greater contribution to the occurrence of accidents.

[0104] Table 2

[0105]

[0106]

[0107] As shown in Table 2, the enterprise management layer more often reflects the road maintenance units, engineering consulting and supervision units, and construction units. For example, the road maintenance units have not effectively fulfilled their main responsibility for the management of hidden dangers in the central guardrail, and the construction units lack traffic organization safety facilities and insufficient protection capabilities at the construction site. The road environmental factor interference layer more often reflects the impact of central median guardrails, roadside guardrails, and road markings on the process of assisted driving vehicle traffic accidents.

[0108] After analyzing the causes of typical assisted driving car traffic accidents from six levels using the AcciMap method, the causes of each accident were summarized. In the AcciMap analysis of 5 accidents, a total of 104 accident causes were identified, of which 46 causes were independent and 58 factors were similar to other causes. The same accident causes were classified and merged, and finally a total of 59 accident factors were obtained, such as Figure 3 As shown in the figure, the cause of the accident corresponding to each accident participant is described, and N represents the number of times the cause occurs. Figure 4 As shown in the figure, the size of the circle and the thickness of the connecting line respectively represent the number of occurrences of accident causes and the number of connection relationships between accident causes. The AcciMap diagram corresponds to the numbers in the accident factor diagram.

[0109] The AcciMap diagram shows the direct causes of the accident as the human driver's overreliance on the assisted driving function and its limitations. Specifically, these include speeding, prolonged inattention, the assisted driving vehicle's failure to recognize a heavy-duty special-purpose vehicle ahead, a central support pier, lane recognition errors, and the inability to identify obstacles such as pile buckets. The following sections explain the causal relationships between the different levels of the AcciMap analysis and the causes of the accidents.

[0110] The "government decision-making level" encompasses four elements. Four of these accidents involved the inspection, management, and oversight of road hazards, primarily due to non-compliant central divider guardrails, roadside guardrails, and construction site safety precautions. Two of these accidents involved industry regulation of assisted-driving vehicle manufacturers and sellers, impacting the "corporate management" level. Car sales companies exaggerated the features of assisted-driving vehicles, leading to a lack of understanding and overreliance on these features among human drivers. In one accident, the local government's lack of regulations regarding ride-sharing indirectly contributed to the accident.

[0111] The "departmental supervision level" includes three elements, among which one factor is influenced by the "government decision-making level", which led to inadequate supervision of online ride-hailing services and insufficient work on hazard investigation and control. Two accidents affected the road maintenance units and failed to effectively implement the central government's main responsibility for hazard control.

[0112] The "Company Management" level comprises 14 elements. This level primarily describes the impact of management practices by assisted-driving vehicle manufacturers and distributors, road maintenance companies, engineering consulting and supervision organizations, construction companies, and other organizations on other levels. Elements 10 and 11 describe factors attributable to road maintenance organizations, while six elements describe factors attributable to construction organizations in guardrail design, construction, acceptance, and rectification. Three elements describe inadequate driver safety warnings and exaggerated promotion of assisted-driving features by vehicle manufacturers and distributors, which led to accidents.

[0113] The "assisted driving vehicle technology layer" includes 6 elements, among which 4 accidents involved human drivers' over-reliance on assisted driving functions, 3 accidents involved the application limitations of the adaptive cruise function of assisted driving vehicles, and elements 24, 25, 26, and 27 involved vehicle functional accessories such as assisted driving vehicle batteries, doors, recognition modules, and adaptive cruise function modules.

[0114] The "Accident Process and Driver Operation Layer" contains 22 elements, primarily representing the behaviors of human drivers and assisted-driving vehicles, as well as the accident outcomes. This layer is often linked to the "Assisted-driving Vehicle Technology Layer" and this layer. The most common occurrence is speeding, which is closely linked to the human driver's failure to maintain control of the vehicle and take emergency measures. Elements 34, 36, 37, 38, and 39, respectively, describe accident scenarios involving assisted-driving vehicles colliding with toll booth safety islands, high-speed rail bridge support piers, rear-end collisions with heavy-duty special-purpose vehicles, head-on collisions with heavy-duty semitrailers, and collisions with the extended ends of roadside guardrails.

[0115] The "road environment interference layer" contains 10 elements. Elements 51, 52, 53, and 54 all relate to safety and protection in construction areas, while elements 50, 55, 56, 57, and 58 address road infrastructure. Among the road environment elements, issues such as missing traffic signs, insufficient safety signs in construction areas, and inappropriate guardrail placement affect the decision-making of assisted-driving vehicles and the operational behavior of human drivers. The road environment interference model for assisted-driving vehicle traffic accidents is used to categorize these elements, as shown in Table 3.

[0116] Table 3

[0117]

[0118] Note: “—” indicates that the item is not applicable.

[0119] The present invention provides a road environment cause correlation analysis system for traffic accidents involving an assisted driving function, comprising: a road static facility survey module, a traffic dynamic feature survey module, a weather instantaneous condition survey module, a road environment element analysis module, and a cause correlation result output module; the road static facility survey module, the traffic dynamic feature survey module, and the weather instantaneous condition survey module are used to collect road environment element data when the assisted driving function is turned on; then, a road environment element interference model is defined through the road environment element analysis module to clarify the collection requirements of different assisted driving functions for road environment elements, and the data collected by the road static facility survey module, the traffic dynamic feature survey module, and the weather instantaneous condition survey module are analyzed through the road environment element interference model to determine the road environment interference elements involved in traffic accidents involving the assisted driving function, and the interference elements are used to determine the causes of traffic accidents; the cause correlation result output module is used to output the accident cause analysis results, analyze each accident using the accident map (AcciMap) method, and perform cause correlation analysis and statistical display on typical cases and multiple cases.

[0120] Through the application of this system, according to the different backgrounds of assisted driving functions turned on in vehicles when road traffic accidents occur, corresponding investigation contents such as road static facilities, traffic dynamic characteristics, instantaneous weather conditions, etc. are collected, and road environment factor investigation processes and methods covering accident evidence extraction, cause analysis, hidden danger tracing, and responsibility determination are proposed. This solves the problem of the lack of a road environment causal correlation analysis system for traffic accidents involving assisted driving functions in the "human-machine mixed driving" mode, and greatly improves the accuracy, scientificity and fairness of assisted driving car traffic accident investigations.

[0121] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A traffic accident road environment cause correlation analysis system involving assisted driving function, characterized in that: include: Road static facilities investigation module, traffic dynamic characteristics investigation module, weather instantaneous condition investigation module, road environment factor analysis module and cause-related result output module; The road static infrastructure survey module is used to collect infrastructure information of the entire road and the local location where the assisted driving vehicle traffic accident occurred, as well as static traffic information displayed by traffic control facilities; The road static facility survey module includes: a road attribute element collector, a road infrastructure element collector, a high-precision map information element collector, a signal control facility element collector, an information release facility element collector, and a traffic warning facility element collector; The road attribute element collector is used to collect road geometry data interference value variables and road association relationship data interference value variables; The road infrastructure element collector is used to collect the changing status of the basic traffic facility components; The high-precision map information element collector is used to collect lane data interference value variables, lane attribute data interference value variables and digital traffic sign and marking data interference value variables; The signal control facility element collector is used to collect the interference value variables of intersection signal light elements, lane traffic signal light element interference value variables, and special point segment signal light element interference value variables; The information release facility element collector is used to collect variable information indicator board element interference value variables, variable speed limit sign screen element interference value variables, and service area guidance facility element interference value variables; The traffic warning facility element collector is used to collect the yellow flashing warning light element interference value variable, the severe weather warning device element interference value variable, the temporary safety warning light element interference value variable, and the traffic warning pile element interference value variable; The traffic dynamic characteristics investigation module is used to collect the overall traffic flow operation characteristic information of the road where the accident occurred and the local traffic flow video image information at the accident location; The instantaneous weather condition investigation module is used to collect local traffic meteorological environment monitoring information that interferes with the normal operation of the assisted driving function; The road environment factor analysis module is connected to the road static facility investigation module, the traffic dynamic characteristic investigation module, and the weather instantaneous condition investigation module respectively. The road environment factor analysis module is used to perform data analysis on infrastructure information, static traffic information, traffic flow operation characteristic information, traffic flow video image information, and local traffic meteorological environment monitoring information to determine road environment interference factors involved in traffic accidents involving the assisted driving function; The road environment factor analysis module is a VLIW server equipped with a road environment factor interference model; The road environment factor interference model is: i∈M,μ j +φ j +oh j =1 Where HJ i is the interference model of road environment factors, j is the weight coefficient of road environment factor i on the traffic accident of assisted driving vehicle, μ j is the weight coefficient of road static facility elements, φ j is the weight coefficient of traffic dynamic characteristic factors, ω j The weight coefficient of the instantaneous weather condition factor, i is the road environment factor of the assisted driving car traffic accident, M is the road environment factor collection, ∈ means belongs to, i∈M means that the road environment factor i belongs to the road environment factor collection M, f(RO i ) is the interference function of the static road facilities in the road environment element i, f(DT i ) is the interference function of traffic dynamic characteristic elements in road environment element i, f(TQ i ) is the interference function of the instantaneous weather condition factor in the traffic road environment factor i; The cause correlation result output module is connected to the road environment factor analysis module, and the cause correlation result output module is used to determine the cause of the traffic accident based on the road environment interference factors and output the accident cause analysis result; The cause association result output module includes: a typical case cause association processor, a multi-case cause statistics display and a multi-case cause association processor; The typical case cause association processor is used to analyze each traffic accident using the accident map method and output a typical case AcciMap analysis result map; The multi-case cause statistics display is used to display the statistical results of several typical case AcciMap analysis result graphs; The multi-case cause association processor is used to classify and merge the same road environment causes based on the statistical results, and output a final AcciMap statistical analysis result map.

2. The traffic accident road environment cause correlation analysis system involving the assisted driving function according to claim 1 is characterized in that: The road attribute element collector, road infrastructure element collector, signal control facility element collector, information release facility element collector and traffic warning facility element collector are all composite three-dimensional laser scanners.

3. The traffic accident road environment cause correlation analysis system involving the assisted driving function according to claim 1 is characterized in that: The traffic dynamic characteristics investigation module includes: a traffic flow element collector, a traffic event element collector and an important facility status element collector; The traffic flow element collector is used to capture 360-degree panoramic images of road conditions and accident locations. The traffic event element collector is used to monitor the event process that affects the normal traffic order on the road in real time and analyze the operating status of motor vehicles on the road and road condition information; The important facility status element collector is used to monitor the status information of traffic facilities in different occasions.

4. The traffic accident road environment cause correlation analysis system involving the assisted driving function according to claim 1, characterized in that: The instantaneous weather condition survey module includes: visibility collector, road surface temperature collector, road surface condition collector, wind speed collector, wind direction collector and precipitation collector; The visibility collector is used to determine the meteorological optical range by emitting infrared pulse light to calculate the intensity of pulse light forward scattered by aerosol particles in the atmosphere; The road surface temperature collector is used to determine the surface temperature of the road surface by measuring the infrared energy radiated by the road surface itself; The road surface condition collector is used to detect the thickness of ice, snow and water on the road surface through multi-spectral measurement technology; The wind speed collector determines the wind speed based on the ultrasonic sensor collector; The wind direction collector determines the wind direction based on the ultrasonic sensor collector; The precipitation collector is used to measure the size of raindrops and the precipitation speed.

Images