Multi-level automatic driving-oriented road work control area speed limit calculation method

Abstract

The present application relates to a kind of multi-level automatic driving-oriented road operation control area speed limit calculation method. The road traffic environment information and automatic driving self car information of road operation control area are obtained, the speed limit value matching multi-level automatic driving is calculated according to the sight distance safety theory, and the speed limit failure function function matching multi-level automatic driving is further established according to reliability theory, and the speed limit failure probability is calculated. The present application can comprehensively consider the road traffic environment conditions and automatic driving level factors of road operation control area, provide the reliable degree of speed limit value, provide effective technical means for ensuring the automatic driving travel safety of road operation control area.

Description

Technical Field

[0001] This invention belongs to the field of autonomous driving speed control technology, specifically relating to a method for calculating speed limits in road operation control zones for multi-level autonomous driving. Background Technology

[0002] High-level autonomous vehicles are equipped with high-performance sensors, perception systems, and onboard computing systems, significantly mitigating the behavioral shortcomings of traditional drivers and improving driving stability and precision, thus expected to significantly enhance driving safety. However, the evolution from traditional manned vehicles to fully autonomous driving requires multiple levels of autonomous driving technology upgrades, including: Level 0 emergency assistance, Level 1 partial driver assistance, Level 2 combined driver assistance, Level 3 conditional automation, Level 4 highly automated driving, and Level 5 fully automated driving. It is foreseeable that vehicles with multiple levels of autonomous driving will coexist in existing road traffic environments. Existing research has found that compared to traditional manned vehicles, vehicles with intermediate levels of autonomous driving (such as Levels 1-3) do not show significant improvements in driving safety, and may even have negative impacts. Therefore, research on autonomous driving should be expanded to include multiple levels of autonomous driving, comprehensively considering factors at each level.

[0003] On the other hand, the Road Operations Control Zone (ROC) is a significant bottleneck or road segment within the existing road traffic environment. When a vehicle enters this zone, it needs to decelerate to the prescribed speed limit within the warning area; when traffic flow becomes congested and queues form due to the reduction of drivable lanes in this zone, upstream vehicles may even need to decelerate to a stop. Furthermore, this zone typically contains various traffic participants (such as traffic guides, non-motorized vehicles, etc.) and stationary obstacles (such as water-filled barriers), posing a significant challenge to autonomous vehicles' ability to overcome the long-tail effect of driving scenarios. When an autonomous vehicle fails to recognize stationary obstacles or traffic guides, it may lead to a collision risk. However, current design operating conditions for various levels of autonomous driving do not include provisions for the ROC. Therefore, to further promote the application of various levels of autonomous vehicles in existing road traffic environments, it is necessary to customize the speed limits for vehicles entering the ROC. Summary of the Invention

[0004] The purpose of this invention is to provide a method for calculating speed limits in road operation control areas for multi-level automated driving, and to provide the reliability of the speed limit values. This method can comprehensively consider the road traffic environment conditions and automated driving level factors in the road operation control area, providing an effective technical means to ensure the driving safety of automated driving in the road operation control area and to guide the optimization of the speed control algorithm for automated driving.

[0005] This invention obtains road traffic environment information and autonomous vehicle information in the road operation control area, calculates speed limit values ​​matching multiple levels of autonomous driving based on line-of-sight safety theory, further establishes a speed limit failure function matching multiple levels of autonomous driving based on reliability theory, and calculates the speed limit failure probability.

[0006] To achieve the above objectives, the technical solution of the present invention is: a method for calculating speed limits in road operation control areas for multi-level automated driving, which obtains road traffic environment information and autonomous vehicle information in the road operation control area, calculates speed limits matching multi-level automated driving based on line-of-sight safety theory, further establishes a speed limit failure function matching multi-level automated driving based on reliability theory, and calculates the speed limit failure probability.

[0007] In one embodiment of the present invention, the method includes the following steps:

[0008] Step S1: Obtain road traffic environment information and autonomous driving vehicle information within the road operation control area;

[0009] The road traffic environment information of the road operation control area includes: road geometry information of the road operation control area, layout information of the road operation control area, and vehicle queue length S. q Weather and environmental information;

[0010] The road geometry information of the road operation control area includes the road curvature radius;

[0011] The road operation control zone layout information includes the warning zone length L. w ;

[0012] The weather environment information includes: weather type and its corresponding weather attribute information, wherein when the weather type is rainy, the corresponding weather attribute information includes rainfall level and rainfall intensity; when the weather type is snowy, the corresponding weather attribute information includes snowfall level and rainfall intensity; when the weather type is foggy, the corresponding weather attribute information includes fog intensity level and visibility.

[0013] The autonomous vehicle information includes: autonomous driving level, driving speed V, and perception-braking reaction time t. pbr Braking deceleration A d The system presets the braking deceleration A during the driver's takeover period. dp Driver takeover time t to Sensor configuration and deployment scheme;

[0014] The autonomous driving levels include: Level 0 emergency assistance, Level 1 partial driving assistance, Level 2 combined driving assistance, Level 3 conditional autonomous driving, Level 4 highly automated driving, and Level 5 fully automated driving.

[0015] The sensor configuration scheme includes: sensor type and quantity, detection distance, horizontal field of view, vertical field of view, horizontal angular resolution, vertical angular resolution, and obstacle perception algorithm;

[0016] The sensor deployment scheme includes the sensor installation locations;

[0017] Step S2: Calculate the speed limit values ​​for matching multiple levels of automated driving based on the line-of-sight safety theory;

[0018] Step S3: Establish a speed limit failure function to match multi-level automated driving based on reliability theory;

[0019] Step S4: Calculate the probability of speed limit failure for matching multi-level autonomous driving.

[0020] In one embodiment of the present invention, step S2 is implemented as follows:

[0021] ①When S q When V = 0, calculate the speed limit value V for autonomous driving levels 0-5. l The formula is:

[0022]

[0023] ②When S q When the speed limit is greater than 0, the speed limit value V is calculated based on the autonomous driving level. l :

[0024] When the autonomous driving level is 0, 1, 2, 4, or 5, V l The formula is:

[0025]

[0026] When the autonomous driving level is Level 3, V l The formula is:

[0027]

[0028] In one embodiment of the present invention, step S3 is implemented as follows:

[0029] Step S31: Establish the required parking line-of-sight function S based on the autonomous driving level. rs (V l ):

[0030] When the autonomous driving level is 0, 1, 2, 4, or 5, S rs (V l The formula is:

[0031]

[0032] When the autonomous driving level is Level 3, S rs (V l The formula is:

[0033]

[0034] Step S32: Based on the acquired road geometry information, weather environment information, autonomous driving level, and perception sensor configuration and deployment scheme of the road operation control area, look up the table in the database of available line-of-sight distances based on autonomous driving virtual testing to obtain the available line-of-sight distance S for obstacles for autonomous driving. a ;

[0035] Step S33: Based on reliability theory, establish the speed limit failure function Z to match multi-level automated driving. s (V l The formula is:

[0036] Z s =S a -S rs .

[0037] In one embodiment of the present invention, step S4 is specifically implemented as follows:

[0038] Step S41, Divide Z s (V l S in ) q S a L w A d A dp V is a deterministic parameter, t pbr t to It is a random variable;

[0039] Step S42: Based on the acquired road traffic environment information and autonomous driving vehicle information of the road operation control area, determine S q S a L w A d A dp Calculated values ​​of V, t pbr t to The mean, standard deviation, and probability distribution form;

[0040] Step S43: Calculate the speed limit reliability index β using reliability theory, where β is a dimensionless number;

[0041] Step S44: Calculate Z s (V l The probability P of speed limit failure f The formula is:

[0042] P f =100[1-Φ(β)]

[0043] In the formula, Φ(β) is the standard normal distribution probability corresponding to the standard normal distribution variable value β.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] (1) Considering the combined influence of road traffic environment conditions and automatic driving level factors in the existing road operation control area, it can be compatible with different automatic driving levels and adapt to the existing road operation control without changing its original settings, while making the speed limit calculation results of the technical solution disclosed in this invention more realistic and effective.

[0046] (2) It fills the technical gap in the calculation scheme of speed limit for autonomous driving in road operation control area, and can provide a theoretical basis for ensuring the driving safety of autonomous driving in road operation control area. It also helps to guide the optimization of speed control algorithm for autonomous driving and refine the design and operation conditions for road operation control area. Attached Figure Description

[0047] Figure 1 This is a flowchart of a method for calculating speed limits in road operation control zones for multi-level automated driving, provided in an embodiment of the present invention.

[0048] Figure 2 This refers to the road traffic environment information and autonomous vehicle information of the road operation control area obtained in this embodiment of the invention;

[0049] Figure 3 This invention provides a method for obtaining speed limit values ​​(S) that match multiple levels of automated driving. q =0) is a calculation scenario diagram. In the diagram: 1 is the autonomous vehicle and 2 is the road operation control area.

[0050] Figure 4 This invention provides a method for obtaining speed limit values ​​(S) that match multiple levels of automated driving. q A schematic diagram of the computational scenario for >0), where: 1 represents an autonomous vehicle, 2 represents a queuing vehicle, and 3 represents a road operation control area;

[0051] Figure 5 This is a flowchart of an embodiment of the present invention for establishing a speed limit failure function that matches multiple levels of autonomous driving;

[0052] Figure 6 This is the S provided in the embodiments of the present invention. a Scene illustration;

[0053] Figure 7This is a flowchart illustrating the calculation of the speed limit failure probability for matching multi-level autonomous driving in an embodiment of the present invention. Detailed Implementation

[0054] To make the features and advantages of this patent more apparent and understandable, specific embodiments are provided below for detailed explanation:

[0055] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0056] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0057] like Figure 1 As shown, this invention proposes a method for calculating speed limits in road operation control zones for multi-level automated driving, comprising the following steps:

[0058] (1) Obtain road traffic environment information and autonomous vehicle information within the road operation control area; the information involved in this step includes, for example: Figure 2 As shown;

[0059] The road traffic environment information of the road operation control area includes at least: road geometry information of the control area, layout information of the control area, and vehicle queue length S. q Weather and environmental information;

[0060] The road geometry information of the control area includes at least the road curvature radius;

[0061] The control area layout information includes at least the warning zone length L. w ;

[0062] The weather environment information includes at least: weather type and its corresponding weather attribute information, wherein when the weather type is rainy, the corresponding weather attribute information includes at least rainfall level and rainfall intensity; when the weather type is snowy, the corresponding weather attribute information includes at least snowfall level and rainfall intensity; when the weather type is foggy, the corresponding weather attribute information includes at least fog intensity level and visibility.

[0063] The autonomous vehicle information includes at least: autonomous driving level, driving speed V, and perception-braking reaction time t. pbrBraking deceleration A d The system presets the braking deceleration A during the driver's takeover period. dp Driver takeover time t to Sensor configuration and deployment scheme;

[0064] The autonomous driving levels include Level 0 emergency assistance, Level 1 partial driving assistance, Level 2 combined driving assistance, Level 3 conditional autonomous driving, Level 4 highly autonomous driving, and Level 5 fully autonomous driving.

[0065] The sensor configuration scheme includes at least: sensor type and quantity, detection distance, horizontal field of view, vertical field of view, horizontal angular resolution, vertical angular resolution, and obstacle perception algorithm;

[0066] The sensor deployment scheme includes at least the sensor installation locations;

[0067] Among them, the autonomous driving level, perception sensor configuration and deployment scheme in the autonomous vehicle information are inherent attributes of autonomous vehicles and can be directly extracted; driving speed V, braking deceleration A d The system presets the braking deceleration A during the driver's takeover period. dp Capable of collecting data in real time during autonomous driving; perception-braking response time t pbr Driver takeover time t to It can be extracted from the historical database of autonomous vehicles during their operation. The road geometry and layout information of the control area within the road traffic environment information can be obtained through on-site data collection at the roadside or by relevant data provided by the road design department; vehicle queue length S q Information can be obtained through on-site data collection at the roadside; weather and environmental information can be obtained through on-site data collection or statistical analysis of publicly available weather information data.

[0068] (2) Calculate the speed limit values ​​for matching multiple levels of automated driving based on the line-of-sight safety theory;

[0069] ①When S q When V = 0, calculate the speed limit value V for autonomous driving levels 0-5. l The formula is:

[0070]

[0071] In the formula, S q The unit is m, V l V is in km / h, L w The unit is meters (m).

[0072] ②When S qWhen the speed limit is greater than 0, the speed limit value V is calculated based on the autonomous driving level. l :

[0073] When the autonomous driving level is 0, 1, 2, 4, or 5, V l The formula is:

[0074]

[0075] In the formula, A d The unit is m / s 2 , t pbr The unit is seconds (s).

[0076] When the autonomous driving level is Level 3, V l The formula is:

[0077]

[0078] In the formula, A dp The unit is m / s 2 , t to The unit is seconds (s).

[0079] Among them, the scenario corresponding to ① is as follows: Figure 3 As shown, the scenario corresponding to ② is as follows: Figure 4 As shown.

[0080] (3) Establish a speed-limiting failure function to match multi-level automated driving based on reliability theory; the flowchart of this step is as follows. Figure 5 As shown;

[0081] 1) Establish the required parking line-of-sight function S based on the level of autonomous driving. rs (V l ):

[0082] When the autonomous driving level is 0, 1, 2, 4, or 5, S rs (V l The formula is:

[0083]

[0084] When the autonomous driving level is Level 3, S rs (V l The formula is:

[0085]

[0086] 2) Based on the acquired road geometry information, weather environment information, autonomous driving level, and perception sensor configuration and deployment scheme of the control area, a table is consulted in the database of available line-of-sight distances based on autonomous driving virtual testing to obtain the available line-of-sight distance S for obstacles for autonomous driving. a ;

[0087] Among them, the autonomous driving virtual testing method can be achieved independently or jointly with scenario-based autonomous driving virtual testing software such as PreScan and CarSim. The modeling effectiveness of the above software has been widely verified in the field.

[0088] 3) Based on reliability theory, establish the speed-limit failure function Z for matching multi-level automated driving. s (V l The formula is:

[0089] Z s =S a -S rs ;

[0090] In the formula, S a The unit is m, S a like Figure 6 As shown.

[0091] (4) Calculate the probability of speed limit failure when matching multiple levels of automated driving; the flowchart for this step is as follows. Figure 7 As shown;

[0092] 1) Divide Z s (V l S in ) q S a L w A d A dp V is a deterministic parameter, t pbr t to It is a random variable;

[0093] 2) Based on the obtained road traffic environment information and autonomous driving vehicle information of the road operation control area, determine S q S a L w A d A dp Calculated values ​​of V, t pbr t to The mean, standard deviation, and probability distribution form;

[0094] 3) Calculate the speed limit reliability index β using reliability theory, where β is a dimensionless number;

[0095] Among them, reliability theory methods such as the first second moment method and Monte Carlo simulation can be used to calculate β;

[0096] 4) Calculate Z s (V l The probability P of speed limit failure f The formula is:

[0097] P f =100[1-Φ(β)]

[0098] In the formula, P f The unit is %, and Φ(β) is the standard normal distribution probability corresponding to the value β of the variable.

[0099] In summary, this invention presents a method for calculating speed limits in road operation control zones for multi-level automated driving. By acquiring road traffic environment information and autonomous vehicle information within the road operation control zone, it calculates speed limits matching the multi-level automated driving based on line-of-sight safety theory. Furthermore, it establishes a speed limit failure function matching the multi-level automated driving based on reliability theory and calculates the speed limit failure probability. The implementation of this invention makes the calculated speed limit results more realistic and effective. It fills the technical gap in speed limit calculation schemes for automated driving in road operation control zones, providing a theoretical basis for ensuring the safety of automated driving in these zones. It also helps guide the optimization of speed control algorithms for automated driving and refines the design and operating conditions for road operation control zones.

[0100] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0102] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0103] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0104] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0105] This patent is not limited to the above-described preferred embodiment. Anyone can derive other forms of road traffic node segment driving adaptability evaluation method for autonomous driving based on the inspiration of this patent. All equivalent changes and modifications made within the scope of the patent application of this invention shall be covered by this patent.

[0106] The above are preferred embodiments of the present invention. Any changes made to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for calculating speed limits in road operation control zones for multi-level automated driving, characterized in that, The process involves acquiring road traffic environment information and autonomous vehicle information within the road operation control area, calculating speed limits for matching multiple levels of autonomous driving based on line-of-sight safety theory, further establishing a speed limit failure function for matching multiple levels of autonomous driving based on reliability theory, and calculating the speed limit failure probability. The steps include: Step S1: Obtain road traffic environment information and autonomous driving vehicle information within the road operation control area; Road traffic environment information in the road operation control area This includes: road geometry information of the road operation control area, layout information of the road operation control area, and vehicle queue length S. q Weather and environmental information; The road geometry information of the road operation control area includes the road curvature radius; The road operation control zone layout information includes the warning zone length L. w ; The weather environment information includes: weather type and its corresponding weather attribute information, wherein when the weather type is rainy, the corresponding weather attribute information includes rainfall level and rainfall intensity; when the weather type is snowy, the corresponding weather attribute information includes snowfall level and rainfall intensity; when the weather type is foggy, the corresponding weather attribute information includes fog intensity level and visibility. The autonomous vehicle information includes: autonomous driving level, driving speed V, and perception-braking reaction time t. pbr Braking deceleration A d The system presets the braking deceleration A during the driver's takeover period. dp Driver takeover time t to Sensor configuration and deployment scheme; The autonomous driving levels include: Level 0 emergency assistance, Level 1 partial driving assistance, Level 2 combined driving assistance, Level 3 conditional autonomous driving, Level 4 highly automated driving, and Level 5 fully automated driving. The sensor configuration scheme includes: sensor type and quantity, detection distance, horizontal field of view, vertical field of view, horizontal angular resolution, vertical angular resolution, and obstacle perception algorithm; The sensor deployment scheme includes the sensor installation locations; Step S2: Calculate the speed limit values ​​for matching multiple levels of automated driving based on the line-of-sight safety theory; Step S3: Establish a speed limit failure function to match multi-level automated driving based on reliability theory; Step S4: Calculate the probability of speed limit failure for matching multiple levels of autonomous driving; The specific implementation of step S2 is as follows: ①When S q When V = 0, calculate the speed limit value V for autonomous driving levels 0-5. l The formula is: ； ②When S q When the speed limit is greater than 0, the speed limit value V is calculated based on the level of automated driving. l : When the autonomous driving level is 0, 1, 2, 4, or 5, V l The formula is: ； When the autonomous driving level is Level 3, V l The formula is: 。 2. The method for calculating speed limits in road operation control zones for multi-level automated driving according to claim 1, characterized in that, The specific implementation of step S3 is as follows: Step S31: Establish the required parking line-of-sight function S based on the autonomous driving level. rs (V l ): When the autonomous driving level is 0, 1, 2, 4, or 5, S rs (V l The formula is: ； When the autonomous driving level is Level 3, S rs (V l The formula is: ； Step S32: Based on the acquired road geometry information, weather environment information, autonomous driving level, and perception sensor configuration and deployment scheme of the road operation control area, look up the table in the database of available line-of-sight distances based on autonomous driving virtual testing to obtain the available line-of-sight distance S for obstacles for autonomous driving. a ; Step S33: Based on reliability theory, establish the speed limit failure function Z to match multi-level automated driving. s (V l The formula is: 。 3. The method for calculating speed limits in road operation control zones for multi-level automated driving according to claim 2, characterized in that, The specific implementation of step S4 is as follows: Step S41, Divide Z s (V l S in ) q S a L w A d A dp V is a deterministic parameter, t pbr t to It is a random variable; Step S42: Based on the acquired road traffic environment information and autonomous driving vehicle information of the road operation control area, determine S q S a L w A d A dp The calculated value of V, t pbr t to The mean, standard deviation, and probability distribution form; Step S43: Calculate the speed limit reliability index β using reliability theory, where β is a dimensionless number; Step S44: Calculate Z s (V l The probability P of speed limit failure f The formula is: In the formula, Φ(β) is the standard normal distribution probability corresponding to the standard normal distribution variable value β.

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