Methods and servers for controlling a relationship between a vehicle and the flow of traffic across a road segment

Abstract

Method for controlling a relationship between a vehicle and the flow of traffic over a road segment, wherein the method comprises: Receiving telemetry data from the vehicle within the road segment, wherein the telemetry data relates to the road segment; Determining a driving speed uncertainty and a pedal usage uncertainty of the road segment based on telemetry data and a road profile for the road segment, wherein the road profile represents an expected traffic flow over the road segment in the absence of a disturbing event, where the driving speed uncertainty is defined as E [v] = -∑(p v i · ln (p v i) ), where p v i a given speed v i associated probability is, and where the pedal usage uncertainty is defined as E[dv] = -∑ (p dv i · ln(p dv i )), where p dvia probability that corresponds to a given deceleration or acceleration dv i is assigned; Determining a disturbance assessment that indicates a level of disturbance in the traffic flow for the road segment, based on driving speed uncertainty, pedal usage uncertainty, and traffic flow; and Issuing a notification signal when the fault rating exceeds a selected fault threshold, with the notification signal being used to control the relationship between the vehicle and the traffic flow.

Description

INTRODUCTION

[0001] The present invention relates to navigating a vehicle based on a risk attributable to traffic conditions and in particular to a method and a server for controlling a relationship between a vehicle and the traffic flow over a road segment.

[0002] Autonomous vehicles monitor their surroundings to navigate through traffic. While these vehicles are able to assess their immediate environment, they generally lack knowledge of broader traffic problems beyond their immediate perception. In particular, current navigation systems lack the ability to evaluate traffic conditions as a whole and determine the risk level of a road, such as the degree to which traffic may become unstable and pose a danger to the vehicle or impede its movement. Accordingly, it is desirable to be able to provide a vehicle with adequate knowledge of upcoming traffic conditions that may affect its mobility and safety.

[0003] Regarding the further state of the art, reference is made to the publications US 2017 / 0 352 262 A1, US 2017 / 0 184 409 A1 and US 2022 / 0 135 039 A1. SUMMARY

[0004] According to the invention, a method for controlling a relationship between a vehicle and the traffic flow over a road segment is presented, characterized by the features of claim 1.

[0005] In addition to one or more of the features described herein, the method further includes outputting the notification signal to a display on a traffic monitoring device for analyzing traffic flow and / or to a sign to be displayed for traffic flow and / or to the vehicle to navigate the vehicle in relation to the traffic flow. Furthermore, navigating the vehicle includes selecting an alternative route for the vehicle and / or changing a lane in which the vehicle is traveling and / or changing a vehicle distance dynamic for the vehicle and / or changing a vehicle speed and / or changing a vehicle speed profile.Furthermore, the method includes collecting telemetry data from the vehicle at regular intervals while the vehicle is on the road segment, storing the collected telemetry data in a recording for the vehicle, and determining the road segment's properties from the recording. The method also includes sending the telemetry data to a remote server, determining the road segment's properties, and determining a disturbance value at the remote server. Finally, the method includes storing the properties in a road metrics database and deleting the properties from the road metrics database after a selected time period has elapsed.

[0006] Furthermore, a system for controlling the relationship between a vehicle and traffic flow over a road segment is described. The system includes a vehicle telemetry device and a processor. The vehicle's telemetry device receives telemetry data regarding the road segment traversed by the vehicle. The processor is configured to determine a property of the road segment based on the telemetry data and a road profile for the segment, to determine a disturbance rating based on this property, which indicates the level of disturbance in the traffic flow for the road segment, and to output a notification signal when the disturbance rating exceeds a selected disturbance threshold. This notification signal can be used to control the relationship between the vehicle and the traffic flow.

[0007] In addition to one or more of the features described herein, the processor is further configured to output the notification signal to a display on a traffic monitoring device for analyzing traffic flow and / or to a sign to be displayed for traffic flow and / or to the vehicle to navigate the vehicle in relation to the traffic flow. Furthermore, the processor is configured to navigate the vehicle by selecting an alternative route for the vehicle and / or by changing the lane in which the vehicle is traveling and / or by changing the vehicle's distance dynamics and / or by changing the vehicle's speed and / or by changing the vehicle's speed profile.Furthermore, the processor is configured to collect telemetry data from the vehicle at regular intervals while the vehicle is on the road segment, to store the collected telemetry data in a record for the vehicle, and to determine the road segment's characteristics from this record. The road profile represents the expected traffic flow over the road segment in the absence of a disruptive event. The system also includes a road metrics database, and the processor is further configured to store the properties in this database and delete them after a selected period of time.

[0008] According to the invention, a server for controlling the relationship between a vehicle and traffic flow over a road segment is further presented, characterized by the features of claim 5. The server includes a processor configured to receive telemetry data from the vehicle, wherein the telemetry data relates to the road segment traversed by the vehicle; to determine a property of the road segment based on the telemetry data and a road profile for the road segment; to determine a disturbance rating indicating a level of disturbance in the traffic flow for the road segment based on this property; and to output a notification signal when the disturbance rating exceeds a selected disturbance threshold, wherein the notification signal can be used to control the relationship between the vehicle and the traffic flow.

[0009] In addition to one or more of the features described herein, the processor is further configured to output the notification signal to a display on a traffic monitoring device for analyzing traffic flow and / or to a sign to be displayed for traffic flow and / or to the vehicle to navigate the vehicle in relation to the traffic flow. Furthermore, the processor is configured to navigate the vehicle by selecting an alternative route for the vehicle and / or by changing the lane in which the vehicle is traveling and / or by changing a vehicle distance dynamic and / or by changing a vehicle speed and / or by changing a vehicle speed profile.Furthermore, the processor is configured to collect telemetry data from the vehicle at regular intervals while the vehicle is on the road segment, to store the collected telemetry data in a record for the vehicle, and to determine the road segment's properties from the record. The server also contains a road metrics database, and the processor is further configured to store the properties in the road metrics database and to delete the properties from the road metrics database after a selected period of time has elapsed. Finally, the processor is configured to obtain the road profile from one of several other vehicles traveling through the road segment and from a profile service.

[0010] The above features and advantages, and further features and advantages of the invention, will be readily apparent from the following detailed description when taken together with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Further features, advantages and details appear only as examples in the following detailed description, which refers to the drawings; they show: Fig. 1 a vehicle with an associated motion path planning system, shown according to various embodiments; Fig. 2 a schematic view of an architecture of a system suitable for informing the vehicle of a road risk using telemetry data obtained through crowdsourcing, according to one embodiment; Fig. 3. A schematic diagram of details of the system. Fig. 2; Fig. 4 a schedule of the process at the road safety analyzer from Fig. 3 procedures carried out; Fig. 5. A flowchart of a procedure for processing telemetry data to determine road characteristics; Fig. 6. A schedule of a short-term maintenance routine that is executed on the historical data stored in the road metrics database; Fig. 7 a schedule of a long-term maintenance routine that is executed on the historical data in the road metrics database; Fig. 8 a timeline for a selected road segment; and Fig. 9 a table of various properties and associated scaled or rated values. DETAILED DESCRIPTION

[0012] The following description is essentially only exemplary. Naturally, corresponding reference symbols throughout the drawings denote identical or corresponding parts and features. As used here, the term "module" refers to a processing circuit arrangement that may include an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or grouped), and memory running one or more software or firmware programs, a combination logic circuit, and / or other suitable components that provide the described functionality.

[0013] According to an exemplary embodiment, Fig. Figure 1 shows a vehicle 10 with an associated motion path planning system shown at Figure 100, according to various embodiments. In general, the motion path planning system 100 determines a motion path plan for the automated driving of the vehicle 10. In general, the vehicle 10 comprises a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is arranged on the chassis 12 and essentially encloses components of the vehicle 10. The body 14 and the chassis 12 can together form a frame. The front wheels 16 and the rear wheels 18 are each rotatably coupled to the chassis 12 near a respective corner of the body 14.

[0014] According to various embodiments, the vehicle 10 is an autonomous vehicle, and the motion trajectory planning system 100 is integrated into the autonomous vehicle 10 (hereinafter referred to as the autonomous vehicle 10). The autonomous vehicle 10 is, for example, a vehicle that is automatically controlled to transport occupants from one place to another. In the illustrated embodiment, the autonomous vehicle 10 is shown as a passenger car, but it should be noted that any other vehicle, including motorcycles, trucks, all-terrain vehicles (SUVs), motorhomes (RVs), etc., can also be used. According to one exemplary embodiment, the autonomous vehicle 10 is a so-called Level 4 or Level 5 automation system.A Level 4 system indicates "high automation," which refers to the driving mode-specific performance of all aspects of the dynamic driving task by an automated driving system, even if a human driver does not respond appropriately to a request for intervention. A Level 5 system denotes "full automation," which refers to the continuous performance of all aspects of the dynamic driving task under all road and environmental conditions that can be managed by a human driver by an automated driving system.

[0015] As shown, the autonomous vehicle 10 generally includes a propulsion system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, a controller 34, and a communication system 36. According to one embodiment in which the autonomous vehicle 10 is an electric vehicle, there may be no transmission system 22. According to various embodiments, the propulsion system 20 may include an internal combustion engine, an electric working machine such as a traction motor, and / or a fuel cell propulsion system. The transmission system 22 is configured to transmit power from the propulsion system 20 to the vehicle's front wheels 16 and rear wheels 18 according to selectable gear ratios.According to various embodiments, the transmission system 22 can include a stepped automatic transmission, a continuously variable transmission, or another suitable transmission. The braking system 26 is configured to provide braking torque to the vehicle's front wheels 16 and rear wheels 18. According to various embodiments, the braking system 26 can include friction brakes, electronic brakes, a regenerative braking system such as an electric work machine, and / or other suitable braking systems. The steering system 24 influences the position of the front wheels 16 and the rear wheels 18. Although the steering system 24 is shown for illustrative purposes as including a steering wheel, according to some embodiments it may not include a steering wheel.

[0016] The sensor system 28 includes one or more scanning devices 40a-40n that scan observable conditions of the external and / or internal environment of the autonomous vehicle 10. The scanning devices 40a-40n may include radars, lidar, global positioning systems, optical cameras, thermal imaging cameras, ultrasonic sensors, and / or other sensors. The cameras may include two or more digital cameras positioned at a distance from one another, with the two or more digital cameras being used to obtain stereoscopic images of the environment in order to create a three-dimensional image. The scanning devices 40a-40n may include sensors that monitor dynamic variables of the vehicle, such as its speed, acceleration, the number of times the brake pedal is applied, etc.The actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features such as the propulsion system 20, the transmission system 22, the steering system 24 and the braking system 26.

[0017] The controller 34 includes at least one processor 44 and a computer-readable storage device or computer-readable storage media 46. The at least one processor 44 can be any custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an additional processor among several processors assigned to the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, any combination thereof, or generally any device for executing instructions. The computer-readable storage device or computer-readable storage media 46 can include volatile and non-volatile storage, e.g., in read-only memory (ROM), read / write memory (RAM), and hold memory (KAM).A KAM is a persistent or non-volatile memory that can be used to store various operating variables while at least one processor 44 is powered off. The computer-readable storage device or storage media 46 can be implemented using any number of known storage devices, such as PROMs (programmable read-only memories), EPROMs (electronic PROMs), EEPROMs (electronically erasable PROMs), flash memory, or any other electrical, magnetic, optical, or combination storage devices capable of storing data, some of which represent executable instructions used by the controller 34 in controlling the autonomous vehicle 10.

[0018] The instructions can contain one or more separate programs, each containing an ordered list of executable instructions for implementing logical functions. When executed by the at least one processor 44, the instructions receive and process signals from the sensor system 28, perform logic, calculations, procedures, and / or algorithms for automatically controlling the components of the autonomous vehicle 10, and, based on the logic, calculations, procedures, and / or algorithms, generate control signals for the actuator system 30 to automatically control the components of the autonomous vehicle 10. Although in Fig. Where only one controller is shown, embodiments of the autonomous vehicle 10 may include any number of controllers which communicate via any suitable communication medium or via a combination of communication media and which work together to process the sensor signals, execute logic, calculations, procedures and / or algorithms and generate control signals for automatically controlling features of the autonomous vehicle 10.

[0019] According to various embodiments, the motion path planning system 100 embodies one or more instructions of the controller 34, which, when executed by the at least one processor 44, generate a motion path output that addresses kinematic and dynamic constraints of the environment. For example, the instructions receive process sensor and map data as input. The instructions employ a graph-based approach with a customer-specific cost function to handle various road scenarios on both urban and highway streets.

[0020] The communication system 36 is configured to wirelessly transmit information to and from other entities 48, such as other vehicles (“V2V” communication), infrastructure (“V2I” communication), remote systems, remote servers, cloud computers, and / or personal devices. According to one exemplary embodiment, the communication system 36 is a wireless communication system configured to communicate using IEEE 802.11 standards or cellular data communication over a wireless local area network (WLAN). However, additional or alternative communication methods, such as a dedicated short-range communication channel (DSRC channel), are also considered possible.DSRC channels refer to single-directional or dual-directional short-range to medium-range wireless communication channels specifically designed for automotive use, and to a corresponding set of protocols and standards.

[0021] Fig. Figure 2 shows a schematic view of the architecture of a system 200, which is suitable for informing the vehicle 10 about a road risk using telemetry data obtained through crowdsourcing, according to one embodiment. The system 200 includes an operation within a vehicle area 202 and a cloud area 204. The vehicle area 202 contains an autonomous vehicle 10. The cloud area 204 contains one or more remote servers 206. Information is sent back and forth between the autonomous vehicle 10 in the vehicle area 202 and the one or more remote servers 206 in the cloud area 204.

[0022] In the vehicle area 202, the autonomous vehicle 10 receives measurement data about its operation using the scanning devices 40a-40n, such as its speed, acceleration, latitude, longitude, braking force, surface conditions, location of detected targets, etc. These measurements are transmitted to one or more remote servers 206. In the cloud area 204, the one or more remote servers 206 calculate various risk parameters based on the measurements from the autonomous vehicle 10 and a profile of a road segment traversed by the autonomous vehicle.

[0023] The profile can be received by one or more remote servers 206 from a profile database or from a profile service 208. The profile service 208 receives telemetry data from multiple vehicles and organizes the data according to the road segment, time frame, etc. The profile is calculated to represent traffic along a selected road segment for a selected duration, such as an hour of the day, a quarter of an hour of the day, a seasonal period, etc. According to another embodiment, the one or more remote servers 206 can compile profile data themselves using telemetry data received from multiple vehicles traveling along a road segment.

[0024] A profile represents the expected traffic flow over a road segment for a given time period. The road profile generally represents the stable traffic flow over the road segment for that time period. Stable traffic flow is traffic flow in the absence of any destabilizing influence, such as roadworks, an accident, poor road conditions, etc. Measurements taken by the vehicle and surrounding traffic are referred to as metrics. These metrics are interpreted using a model, such as a transportation research model, to generate one or more properties. These properties are then used to create a traffic flow profile, which is defined by a mean and a standard deviation of the one or more properties.

[0025] The road profile for a road segment is represented by an average value (mean) and by an expected variation (standard deviation) of one or more properties over the road segment over a given time period. These properties can include driving speed uncertainty, pedal usage uncertainty, the number of observed vehicles, average speed and speed changes, the distribution of speeds and speed changes, hard braking and acceleration, the number of full braking maneuvers, and the number of idle stops.

[0026] The one or more remote servers 206 compile the measurements from the vehicle 10 and compare the calculated properties with the road profile to determine the degree to which each property deviates from a stable traffic flow or traffic for the selected road segment for a selected time.

[0027] The uncertainty faced by a driver or vehicle in a traffic flow can be represented by a distribution. According to the invention, the vehicle speed uncertainty is obtained as an entropy value of the speed distribution, as given by equation (1): E[v]=−∑(pvi⋅ln(pvi)), where p vi a given speed v i The associated probability is similarly obtained as an entropy value of the velocity change distribution, as given by equation (2): E[dv]=−∑(pdvi⋅ln(pdvi)), where p dvi a probability that corresponds to a given deceleration or acceleration dv iis assigned. For entropies, a lower bound of zero represents complete constancy of a vehicle's speed along a road segment. The upper bound represents complete uncertainty of the speed and the pedal input necessary to adjust the vehicle's speed to the speed of the surrounding traffic flow. The entropy value is a property that can be calibrated for a road segment to ensure a stable profile flow over a given time period.

[0028] One or more remote servers 206 determine a disturbance score based on the property values ​​of the vehicle speed uncertainty, the pedal usage uncertainty, and the vehicle flow. The disturbance score can be used to identify an instability in the traffic flow, such as an unexpected event that increases the level of vehicle distance dynamics. The disturbance score indicates the level of disturbance in the traffic flow. The disturbance score, as well as other data, can be transmitted from the one or more remote servers 206 to the autonomous vehicle 10. Furthermore, the disturbance score can be used to determine an instruction for the autonomous vehicle 10, and such an instruction can be sent from the one or more remote servers 206 to the autonomous vehicle 10.The autonomous vehicle 10 can perform navigation based on instructions, fault assessment, and / or other data from one or more servers. Alternatively, if the autonomous vehicle 10 is being driven manually, the driver can read the instructions and operate it accordingly.

[0029] Fig. Figure 3 shows a schematic diagram 300, which details the system 200 from Fig. Figure 2 shows the vehicle area 202 containing the autonomous vehicle 10 and an infrastructure sign 302. The cloud area 204 contains a telemetry acquisition processor 306 and one or more remote servers 206.

[0030] The vehicle 10 contains at least one telemetry device 304 and one navigation module 330. The telemetry device 304 acquires various telemetry data or telemetry measurements, including the geographical latitude of a road user, the geographical longitude of the road user, a timestamp, the direction of travel of the road user, the speed or velocity vector of the road user, a surface condition of the road traveled by the road user, any targets detected by the road user, etc. The telemetry device 304 transmits this telemetry data from the vehicle 10 to the cloud area 204.

[0031] In cloud area 204, the telemetry acquisition processor 306 receives the telemetry data from the vehicle 10 and converts the data into a format suitable for use by the remote servers 206. The telemetry acquisition processor 306 transmits data from the autonomous vehicle 10 to one or more remote servers 206 at regularly scheduled intervals, approximately every 3 seconds.

[0032] The one or more remote servers 206 process the data from the telemetry acquisition processor 306 to determine a risk assessment for the vehicle. The one or more remote servers 206 contain a data preprocessor 308, a road safety analyzer 318, a road metrics database 320, a road summarization service 322, and a route guidance engine 326.

[0033] The data preprocessor 308 performs various operations on a publishing or subscription server 314, including map fitting 310, vehicle data grouping 312, and data publishing, to prepare the data for analysis. Map fitting 310 involves comparing the geographic longitude and latitude of the data with a map from a map database 316 to determine the location of the autonomous vehicle 10 at different times. While the autonomous vehicle 10 is within the road segment, the data preprocessor performs vehicle data grouping 312, in which the data is accumulated. When the vehicle leaves the road segment, the accumulated data is grouped into a record representing the road segment. The record is published on a publishing or subscription server 314.Generally, the record is published for the road safety analyzer 318.

[0034] The road safety analyzer 318 receives the recording from the data preprocessor 308 and performs various calculations. In particular, the road safety analyzer 318 compares the recording data with the road profile and determines one or more properties of the road segment from the recording. Furthermore, the road safety analyzer 318 calculates a performance score or a disturbance score associated with one or more properties and determines the probability of a traffic disruption or adverse driver experience, such as stopped traffic, stop-and-go traffic, etc. The operations of the road safety analyzer 318 are described here in relation to… Fig. 4 discussed.

[0035] The properties and disturbance values ​​determined by the road safety analyzer 318 can then be stored in the road metrics database 320. The road metrics database 320 provides a historical database that stores this data for a predetermined time frame and accumulates new data from other recordings obtained by other vehicles over that period. According to various embodiments, the historical data can be maintained over a short time frame, such as 15 minutes, or over a long time frame, such as one month.

[0036] The road summarization service 322 retrieves data from the road metrics database 320 to provide instructions to the autonomous vehicle 10. For example, the road summarization service 322 can retrieve historical data for the selected road segment for the selected duration from the road metrics database 320 and provide this data to the vehicle 10 when the vehicle 10 travels through a road segment during a selected duration. Specifically, the road summarization service 322 can provide the vehicle 10 with a disturbance value and / or road properties for the road segment. Furthermore, the road summarization service 322 can provide a notification signal after comparing the disturbance value with a selected disturbance threshold, such as when the disturbance value is greater than the selected disturbance threshold.

[0037] In vehicle 10, the data and / or the notification signal are received by a navigation module 330. The navigation module 330 can determine a course of action to be implemented by vehicle 10. For example, the navigation module 330 can perform a route selection 332 to choose a safer route than the current road segment. Alternatively, the navigation module 330 can perform a lane selection 334 to change the lane in which vehicle 10 is traveling to a lane with a higher degree of safety. Furthermore, the navigation module 330 can manage the operation of vehicle 10, for example, by managing its movement 336, such as a vehicle distance for vehicle 10 and / or its speed or speed profile, or its level of acceleration or deceleration, etc.

[0038] According to another embodiment, the data and / or the notification signal from the road summarization service 322 can also be sent to the infrastructure sign 302, which can display this data or a suitable warning to the traffic as a whole. According to another embodiment, the data and / or the notification signal can be sent to a traffic monitor 340, or to a traffic monitoring device or a traffic monitoring server, which may be observed by a traffic engineer or another user. The data and / or the notification signal can be displayed on a screen of the traffic monitor 340, such as a dashboard, operating at the time of display. Thus, the notification signal controls a relationship between the vehicle and the traffic flow over the road segment to generally reduce disturbance in the traffic flow over the road segment.The notification sent to the traffic engineer can be used to analyze traffic flow in order to design new traffic regulations or traffic systems. The relationship between the vehicle and the traffic flow is also established when the notification signal is sent to infrastructure sign 302 to indicate a traffic flow warning or advice, and when the notification signal is sent to the vehicle for control purposes.

[0039] The route guidance service 324 of one or more remote servers 206 receives data from the road summarization service 322 and determines the complexity of the traffic pattern. Based on a planned route or destination of the vehicle and the given traffic pattern, the route guidance engine 326 can determine an alternative route. The alternative route can be delivered to the autonomous vehicle 10.

[0040] Fig. Figure 4 shows a flow chart 400 of the road safety analyzer 318. Fig. 3. The procedure is carried out as follows. In field 402, a statistical analysis or evaluation of the measured values ​​X from the one or more records is transformed into road metrics M. In field 404, the metrics M are used to determine one or more properties of the road segment (i.e., mean and standard deviation). In field 406, the properties are used to generate a profile F for the road segment. In field 408, the properties or property values ​​are scaled or evaluated to determine their deviation from an expected profile for the road segment. The properties are used to determine a road performance score for the road segment and over time. According to one embodiment, the properties can be weighted using model coefficients for the road profile of the road segment.Each property value is multiplied by its corresponding weight and the products are summed to obtain the road performance value as shown in equation (3). S=∑i=1mωi⋅pi.

[0041] In field 408, the road performance score is used to determine a disruption score that describes the probability of an adverse driver experience along the road segment for a given time period. The process shown in flowchart 400 can be performed over additional road segments and for additional time periods.

[0042] Fig. Figure 5 shows a flowchart (500) of a procedure for processing telemetry data to determine road properties. Telemetry data is acquired in field 502. In field 504, the telemetry data is compared with a map. In field 506, a decision is made as to whether the vehicle is still within the road segment. If the vehicle has not left the road segment, the procedure returns to field 502 to acquire more telemetry data from the vehicle and thus accumulate the telemetry data for the road segment. If the vehicle has left the road segment, the procedure then proceeds to field 508. In field 508, the accumulation of telemetry data is completed and stored in a record, which is then published for the road metric analyzer. In field 510, properties of the road segment (such as vehicle distance variables) are calculated from the recorded data.Field 512 stores the properties in the road metrics database 320 in order to update the data stored therein.

[0043] Fig. Figure 6 shows a flowchart (600) of a short-term maintenance routine that is executed on the historical data stored in the road metric database (320). Field 602 references a point in time to determine if the data is older than a selected short-term duration, such as 15 minutes. If the duration has not elapsed, the procedure proceeds to field 604. Road segment data for this duration is retrieved in field 604. Field 606 updates the historical database with the properties determined from the road segment data. Field 608 deletes any metrics that have elapsed (i.e., are older than the 15-minute duration) from the historical database. Field 610 determines whether additional data from further road segments is being collected. If yes, the procedure returns to field 604, where the additional road segment data is retrieved. Otherwise, the procedure stops.

[0044] Fig. Figure 7 shows a flowchart (700) of a long-term maintenance routine that is executed on the historical data in the road metric database (320). Field 702 references a point in time to determine whether the data is older than a selected long-term duration, such as one month. Field 704 retrieves road segment data for the one-month duration. Field 706 deletes any metrics that have elapsed (i.e., are older than the one-month duration) from the historical database. Field 708 determines whether additional data from extra road segments is being collected. If yes, the procedure returns to field 704, where the additional road segment data is retrieved. Otherwise, the procedure stops.

[0045] Fig. Figure 8 shows a Time Schedule 800 for a selected road segment, such as a section of a highway. The Time Schedule 800 presents various properties calculated over a 24-hour period for the selected segment and compares these properties with an expected profile. Time is shown along the x-axis and is divided into 24 one-hour time segments. Properties are shown for each time segment. The traffic pattern shown in the Time Schedule 800 exhibits low traffic volume during the early morning hours (interval range 0 to interval range 5, approximately midnight to 6:00 AM). During this time, traffic is low and tends to flow smoothly. In interval ranges 6 to 9 (approximately 6:00 AM to 10:00 AM), a morning rush hour is shown. In interval ranges 10 to 14 (approximately 10:00 AM to 3:00 PM), a midday rush hour is shown.From interval range 15 to interval range 18 (approximately 3:00 PM to 7:00 PM), afternoon traffic congestion is shown. From interval range 19 to interval range 23 (approximately 7:00 PM to midnight), night traffic is shown.

[0046] Average speed curve 802 shows the average speed (v) for vehicles on the road segment over a 24-hour period. First deviation curve 804 shows the first standard deviation of the average speed, and second deviation curve 806 shows the second standard deviation of the average speed. For illustrative purposes, only the upper limit of the first deviation of the average speed is shown. Similarly, for illustrative purposes, only the lower limit of the second deviation of the average speed is shown.

[0047] For each of the one-hour time periods, a velocity entropy profile 808 is shown. An acceleration entropy profile 810 is also shown for each of the one-hour time periods. The velocity entropy profile 808 and the acceleration entropy profile 810 are both represented within a one-hour time period as a vertical bar, with the height of the bar indicating a value of the entropy. The velocity entropy profile 808 and the acceleration entropy profile 810 for a selected time segment can be determined from historical data obtained during the selected time segment for a selected duration, such as a week, a month, etc. Additionally, the entropy 812 of the actual velocity and the entropy 814 of the actual acceleration, obtained from current telemetry data, are shown.

[0048] The number of sharp braking events (816) within a one-hour period is represented by a bar. Curve 818, the expected vehicle density, shows the expected vehicle density on the road segment during a given time period. Curve 820, the actual vehicle density, shows the actual vehicle density on the road segment during the specified 24-hour period of the time schedule.

[0049] During the early morning hours (approximately midnight to 6:00 AM), traffic exhibits the expected pattern. The average speed curve 802 is relatively constant and generally represents a free-flowing traffic pattern. As expected, the number of vehicles (the actual vehicle density curve 820) on the road segment is relatively low. The actual speed entropy 812 and the actual acceleration entropy 814 are close to the speed entropy profile 808 and the acceleration entropy profile 810, respectively.

[0050] During the morning rush hour (approximately 6:00 a.m. to 10:00 a.m.), the number of vehicles (curve 820 of actual vehicle density) increases as expected. The entropy 812 of actual speed and the entropy 814 of actual acceleration remain close to the speed entropy profile 808 and the acceleration entropy profile 810, respectively. Curve 802 of average speed still shows a free flow of traffic during this morning rush hour.

[0051] During the midday rush hour (approximately 10:00 AM to 3:00 PM), curve 820, representing actual traffic density, shows an increase above the vehicle density of the morning rush hour. The average speed still indicates a free flow of traffic. The entropy of actual speed is significantly elevated compared to the speed entropy profile for various one-hour durations (i.e., at least in intervals 11 and 14). Similarly, the entropy 814 of actual acceleration is significantly elevated compared to the acceleration entropy profile 810 for the same one-hour duration.

[0052] During the afternoon rush hour (approximately 3:00 PM to 7:00 PM), an accident occurs at approximately 6:19 PM. The after-effects of the accident are shown in time period 830. As a result of the accident, the vehicles slow down (local speed minimum 836), and events of a recorded hard braking maneuver (bar 834) are shown. In the time graph, curve 820 of the actual vehicle density falls in interval 18 (as indicated by the local vehicle density minimum 832). In interval 18 (5:00 PM to 6:00 PM), the actual vehicle entropy 812 (compared to the speed entropy profile 808 in interval 18) is reduced to relatively low values. Similarly, the entropy 814 of the actual acceleration in the interval range 18 is reduced to relatively low values ​​(compared to the acceleration entropy profile 810).As shown by the local speed minimum 836, the speed has fallen to very low values ​​and is considerably outside the range set by the second deviation curve 806 for the average speed curve 802.

[0053] During the night traffic hours (approximately 7:00 PM to midnight), traffic recovered from the vehicle accident. For interval range 19, the entropy 840 of the local velocity exceeds the profile 842 of the entropy of the local velocity for interval range 19. Furthermore, the entropy 844 of the local acceleration for interval range 19 exceeds the profile 846 of the entropy of the local acceleration for interval range 19. As shown by the velocity point 838, the average speed for interval range 19 is greater than during the one-hour period of interval range 18 (local velocity minimum 836). However, the average speed for interval range 19 remains low and continues to lie outside the range defined by the second standard deviation for the average speed.Furthermore, the actual vehicle density in interval range 19 has increased from the local vehicle density minimum 832 in interval range 18.

[0054] Fig. Figure 9 shows a Table 900 with various properties and associated scaled or rated values. The properties are tabulated for several road segments recorded over a selected time of day for two consecutive days. The road segments are labeled 1 to 7. The first day (September 18, 2019) is shown in the first 7 rows, and the second day (September 19, 2019) is shown in the second 7 rows. As shown for the first day, road segments 4 and 5 (circle 906) both exhibit a reduction in speed compared to road segments 1-3 and 6-7 (circle 908). A disturbance rating for road segment 4 is 96, and for road segment 5, it is 90 (circle 910).

[0055] As shown for the second day (September 19, 2019), an accident occurs on road segment 4. Compared to road segments 1-3 and 6-7, the speed decreases over road segments 4 and 5 (circle 912). The disturbance score for road segment 4 is 100 (circle 914), indicating the occurrence of instability, disturbance, or a disruptive event over road segment 4. The disturbance score for the second day can be used by the vehicle to select an action, such as changing lanes or altering the route away from road segment 4.

Claims

[1] Method for controlling a relationship between a vehicle and the flow of traffic over a road segment, the method comprising: Receiving telemetry data from the vehicle within the road segment, wherein the telemetry data relates to the road segment; Determining a driving speed uncertainty and a pedal usage uncertainty of the road segment based on telemetry data and a road profile for the road segment, wherein the road profile represents an expected traffic flow over the road segment in the absence of a disturbing event, where the driving speed uncertainty is defined as E [v] = -∑(p vi · ln (p vi) ), where p vi a given speed v i associated probability is, and where the pedal usage uncertainty is defined as E[dv] = -∑ (p dvi · ln(p dvi )), where p dvia probability that corresponds to a given deceleration or acceleration dv i is assigned; Determining a disturbance assessment that indicates a level of disturbance in the traffic flow for the road segment, based on driving speed uncertainty, pedal usage uncertainty, and traffic flow; and Issuing a notification signal when the fault rating exceeds a selected fault threshold, with the notification signal being used to control the relationship between the vehicle and the traffic flow. [2] The method of claim 1, further comprising the output of the notification signal to at least one of the following: (i) a display on a traffic monitoring device for analyzing the traffic flow; (ii) a sign to be displayed for the traffic flow; and (iii) the vehicle, in order to navigate the vehicle with respect to the traffic flow. [3] The method of claim 1, further comprising collecting the telemetry data from the vehicle at regular intervals while the vehicle is on the road segment, storing the collected telemetry data in a recording for the vehicle and determining the property of the road segment from the recording. [4] Method according to claim 1, further comprising sending the telemetry data to a remote server and determining the property of the road segment and determining a disturbance value at the remote server. [5] Server for controlling a relationship between a vehicle and a traffic flow over a road segment, the server comprising: a processor configured to: Receiving telemetry data from the vehicle, wherein the telemetry data relates to the road segment traveled by the vehicle; Determining a driving speed uncertainty and a pedal usage uncertainty of the road segment based on telemetry data and a road profile for the road segment, wherein the road profile represents an expected traffic flow over the road segment in the absence of a disturbing event, where the driving speed uncertainty is defined as E[v] = -∑(p vi · ln (p vi )), where p vi a given speed v i associated probability is, and where the pedal usage uncertainty is defined as E[dv] = -∑(p dvi · ln(p dvi )), where p dvi a probability that corresponds to a given deceleration or acceleration dv i is assigned; Determining a disturbance assessment that indicates a level of disturbance in the traffic flow for the road segment, based on driving speed uncertainty, pedal usage uncertainty, and traffic flow; and Issuing a notification signal when the fault rating exceeds a selected fault threshold, with the notification signal being used to control the relationship between the vehicle and the traffic flow. [6] Server according to claim 5, wherein the processor is further configured to output the notification signal to at least one of the following: (i) a display on a traffic monitoring device for analyzing the traffic flow; (ii) a sign to be displayed for the traffic flow; and (iii) the vehicle, in order to navigate the vehicle in respect of the traffic flow. [7] Server according to claim 5, wherein the processor is further configured to collect the telemetry data from the vehicle at regular intervals while the vehicle is on the road segment, to store the collected telemetry data in a record for the vehicle and to determine the property of the road segment from the record. [8] Server according to claim 5, further comprising a road metrics database, wherein the processor is further configured to store the properties in the road metrics database and deletes the properties from the road metrics database after a selected time period has elapsed.

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