LANE GUIDANCE ASSISTANCE FOR EFFICIENT OPERATION

The automated lane management assist method, data structure and system receive unprocessed lane-specific limited-access highway information, including lane use and speed limits, from traffic detectors in the roadway or from other sources, process and develop processed and/or processed predicted information from these sources and calculate the travel time savings in adjacent lanes using the threshold value for time savings set by the driver, thus improving (decreasing) the overall travel time by assisting the driver or the automated vehicle in the selection of driving lanes and target speeds for vehicles, including in partially and fully automated vehicles.

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Description
CROSS REFERENCE OF RELATED APPLICATIONS

This patent application is a nonprovisional patent application of and claims priority from the provisional patent application Ser. No. 62/342,532 filed on May 27, 2016, and this patent application also claims the benefit of the provisional patent application Ser. No. 62/333,352 filed on May 9, 2016 and the nonprovisional patent application Ser. No. 15/288,333 claiming priority to the provisional patent application Ser. No. 62/333,352, both titled Prediction for Lane Guidance Assist (ALMAPR), all of which applications are hereby incorporated by reference in their entirety.

FIELD OF THE INVENTION

This invention was not made pursuant to any federally-sponsored research and/or development.

This patent application extends the usefulness of the following prior patents, specifically U.S. Pat. No. 9,053,636, titled Management Center Module for Advanced Lane Management Assist for Automated Vehicles and conventionally Driven Vehicles (“ALMAMC”) and U.S. Pat. No. 9,286,800, titled Guidance Assist Vehicle Module (“ALMAVM”). The disclosures of these patents are hereby incorporated by reference in their entirety.

BACKGROUND

The above-identified patents and applications describe a methodology (ALMA) for using traffic management center (TMC) information to select a most appropriate freeway lane for a driver or automated vehicle and to provide a target speed for that lane. TMC information is based on roadway sensors or is derived from vehicle transmissions. Lane speed information and traffic incident information may also originate from vehicle based information sources which is collected and processed at a central site. The TMC traffic condition information, is essentially current information on traffic speed and other variables for each through traffic lane. The information is organized according to a data structure described in in the ALMAMC patent that considers the physical and functional features of the information. The ALMAPR patent application provides predicted information for certain traffic variables in place of current information, thereby improving its timeliness. Current and/or predicted information is transmitted to the vehicle where it is further processed (ALMAVM). This additional processing develops guidance for the most appropriate lane and target speed by looking at traffic speeds for several miles ahead (downstream) of the vehicle's current position.

In ALMAVM, the vehicle operator's driving preferences (including driver aggressiveness) strongly influence the choice of lanes. While accounting for roadway and traffic conditions, guidance is provided to the vehicle operator or to the automated vehicle in accordance with the operator's preferences. Because conditions for several miles downstream of the vehicle are now considered, the decision on lane choice and target speed is improved from the traditional driver's observations or the connected or automated vehicle's choice based on the environment in close proximity to the vehicle. One major feature of these patents is to inhibit many lane changes that would be made without this assistance, thereby improving safety and driver comfort.

As does the ALMAVM patent, the current patent application (ALMATR) also provides lane guidance to the manually operated, automated and connected vehicles. Rather than emphasizing driving preferences as does ALMAVM, ALMATR suggests lane changes only when the lane change results in meaningful travel time savings. By avoidance of lane changes that do not satisfy this criterion, in addition to the economies resulting from time savings, crashes are reduced and the accelerations and decelerations resulting from unnecessary lane changes are avoided, thus saving fuel. As lane selection guidance is more highly focused on safety and economy of operation in ALMATR, it is expected that commercial vehicles will be the primary beneficiaries of these improvements.

As an example of the operation and benefits to be achieved by ALMATR, the following describes the analysis of data collected on Mar. 23, 2016 by the California Department of Transportation Performance Measurement System (PeMS) for I-880 in Fremont, Calif. PeMS data show that trucks almost exclusively drive in the lane adjacent to the right shoulder (Lane 4) and the lane just to its left (Lane 3). FIG. 1 shows the lane speeds averaged over a four mile section of roadway for five minute data samples. The speed for Lane 3 is faster than the speed for lane 4 by varying amounts. Other roadway sections show Lane 4 to be faster for some time periods. The speed difference between the lanes shows considerable variation. FIG. 2 shows a plot of the lane travel times and the differences in travel time between Lanes 3 and 4. As shown in FIG. 3, a significant difference for this four mile section may, for example, be considered to be one minute 103.

It is generally expected that when the speed difference between lanes exceeds a particular value (THR) 104, many motorists perceiving this from their local vantage will be induced to change lanes. The same may be true of automated vehicles if they base their decisions on the local environment. FIG. 3 shows a plot of the travel time difference vs the speed difference for the five minute periods for the section analyzed. This example shows that when the speed differences exceed an assumed absolute value of THR of 4 MPH), the five minute periods may be divided into two groups. One of these groups (enclosed by the solid rectangle 101) results in significant time savings while the other group (enclosed by the dashed rectangle 102) may result in non-productive lane changes. ALMATR provides appropriate guidance to the motorist or automated vehicle to select the appropriate group.

FIG. 4 shows the interrelationship of the ALMA family modules. The modules above the dashed line 201 are provided by the ALMA Management Center and provide same information to all of the vehicles in the region, while those below the dashed line 201 may be located in the vehicle and provide information to that vehicle. The principal functions of the modules are described in FIG. 4.

SUMMARY OF THE INVENTION

The ALMATR module 204 accepts information on lane status and current traffic conditions from ALMAMC 202. It may also accept predicted traffic conditions from ALMAPR 203. It computes the travel time difference between candidate adjacent lanes for the desired look ahead distance. When this difference exceeds a threshold prescribed by the vehicle's operator (RTD), the operator or automated vehicle is notified of the recommended lane.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, aspects and advantages of the novel Lane Guidance Assistance for Efficient Operation will become further understood with reference to the following description and accompanying drawings where

FIG. 1 shows an example of lane speed and lane speed differences for a four-mile section of freeway;

FIG. 2 indicates lane travel times and the difference in lane travel times for the example roadway section illustrated in FIG. 1;

FIG. 3 depicts travel time difference and speed difference between two freeway lanes for five minute periods;

FIG. 4 describes the interrelationship of the ALMA modules;

FIG. 5 is the first part of a flow chart depicting the functional relationship of this invention's modules; and

FIG. 6 is the second part of a flow chart of FIG. 5 depicting the functional relationship of this invention's modules.

 DESCRIPTION

The modules shown in FIGS. 5 and 6 and described below provide an example of the implementation of a methodology for identifying the difference in travel time between lanes for a section of freeway and providing lane guidance to the driver or automated vehicle when appropriate.

Module 1 301—Set desired look ahead difference in travel time (RTD) to change lanes. This parameter is established by the vehicle operator in conjunction with the setting for Module 3. When the absolute value of this difference is exceeded, ALMATR provides guidance to change lanes when conditions established by other modules are appropriate. The example shown in FIG. 3 uses an absolute value of one minute 103.

Module 2 302—Download parameters from the ALMA Management Center. The lane specific parameters include speed, volume, average headway, average vehicle length, passenger car equivalent volume, density, as described in Table 3 of the ALMAMC patent, as well as incident status information and lane status information (Table 4 of the ALMAMC patent), and Static Database parameters (Table 5 of the ALMAMC patent). If prediction is used in addition to the ALMAMC parameters, the predicted parameters identified in Table B1 of the ALMAPR patent application will also be downloaded. The disclosure of the ALMAPR patent application is part of the disclosure of this patent application.

Module 3 303—Set look ahead time. The vehicle operator will set or select a time period (LAT) over which the downstream conditions are to be considered for lane guidance. This time may be set in conjunction with the look ahead difference in travel time 301. For example, for a four minute look ahead time, the vehicle operator may opt to require a one minute travel time saving before a lane change becomes worth the effort or risk.

Module 4 304—Compute look ahead distance. Using current and/or predicted speed in conjunction with look ahead time (LAT) compute the look ahead distance (LEN).

Module 5 305—Identify zones for look ahead distance. This module identifies the zones and portions of zones that will be used for subsequent computations. Zones are a component of the ALMA data structure and represent a portion of the freeway. As described in the ALMAMC patent, zone boundaries are determined by such factors as traffic conditions, placement of variable message motorist information devices that provide advisory and regulatory information. Using zone lengths stored in the static database, the module identifies those zones included in the look ahead distance as well as the portion of the last zone that is also included.

Module 6 306—Compute average speed and travel time for look ahead distance. Compute the travel time for each zone or zone portion identified in Module 5 by dividing the zone length or applicable fraction of zone length by the current speed or predicted speed for that zone. Dividing the look ahead distance by the sum of the zone travel times or applicable portion provides the average speed. Travel time is computed as the quotient of look ahead distance and average lane speed (as developed by using the zone speed and zone length).

Module 7 307—Are other criteria for lane allowance satisfied? This module identifies the constraints on the choice of lane which are influenced by factors other than lane speed. Details for these factors are provided in the ALMAVM patent as noted in Table 1.

TABLE 1 Appropriate Factor ALMAVM Module Does number of vehicle occupants meet lane 2.1 requirements Does vehicle meet height limitations for 2.2 barrel Does vehicle meet weight limitations for 2.3 barrel Is vehicle class permitted in lane 2.4 Adjustment for vehicle exit 3

Module 8 308—Eliminate non-compliant lanes from further consideration. This module removes the lanes that have been found, in Module 7 to be non-compliant lanes from further consideration.

Module 9 401 Set maximum speed desired. The operator may optionally enter a speed that he does not desire to exceed for safety or fuel rate consumption purposes.

Module 10 402 Desire to remain within speed limit. The operator may elect to remain within the speed limits or not through a manual data entry capability.

Module 11 403 Automatic speed enforcement? A message from the ALMAMC module will provide information as to the presence of automatic speed enforcement. This will direct the computation sequence to other modules.

Module 12 404 Select candidate alternative lanes for further consideration. This module corresponds to Module 2 in the ALMAVM patent. Using data entry from the vehicle operator it eliminates lanes based on the following:

Number of allowable vehicle occupants for lane

Vehicle height and weight limitations

Vehicle type classification

In addition, this module implements lane closure and other lane use and speed constraints originating at the TMC and provided by the ALMAMC.

Module 13 405 Is speed for all candidate lanes above the speed limit? This module provides a logic test for this function.

Module 14 406 Select the slowest lane. If it is desired to stay within the speed limit and no lane has a speed that satisfies this criterion, the slowest lane is selected.

Module 15 407 Lane travel time difference>required threshold? This filter determines whether the travel time difference between the current lane and a candidate adjacent lane is of sufficient magnitude to warrant further consideration for lane change.

Module 16 408 Select lane, recommend target speed. For the remaining candidate adjacent lanes, and using the estimated travel times for the look ahead distance (Module 6) select the candidate adjacent lane with the largest difference between the travel time in the current lane and the candidate adjacent lanes.

Module 17 409 Lane gap test OK? This module corresponds to Module 4.3R.2.12 in the ALMAVM Module. The prior modules culminating in Module 16 have established the driver or automated vehicle preference for changing lanes, selecting the lane and the target speed. In conventionally driven vehicles it is the driver's responsibility to change lanes in a safe way, or not change if conditions are not favorable. An automated vehicle must make this choice through the use of vehicle based sensors and the accompanying logic. The literature provides numerous examples of gap acceptance criteria. Examples include Wei and Dolan1 and Ahmed2. 1 JUNQING WEI and JOHN M. DOLAN, A Robust Autonomous Freeway Driving Algorithm, IEEE 2009.2 KAZI IFTEKHAR AHMED, Modeling Drivers' Acceleration and Lane Changing Behavior, Doctoral Thesis, MIT, February 1999.

Module 18 410 Recommend retain current lane. If the prior filters result in negative choices, the current lane is recommended for retention.

Module 19 411 Recommend lane and target speed. If the prior filters result in a positive choice, the selected lane (Module 16) is recommended and the target speed is the current or predicted lane speed.

Claims

1. A method of assisting in selection of driving lanes and vehicle target speeds for travel time savings, comprising the steps of:

a. selecting a look ahead time (LAT) period or a look ahead distance (LEN) for limited-access highway downstream traffic conditions for lane guidance;
b. receiving unprocessed lane-specific limited-access highway data from a traffic management center (TMC), advanced lane management assist management center (ALMAMC), or another data source;
c. combining the unprocessed lane-specific limited-access highway data with data from a static database to create intermediate lane-specific limited-access highway data;
d. generating at least one of processed and processed predicted lane-specific limited-access highway data from the intermediate lane-specific limited-access highway data, the at least one of processed and processed predicted lane-specific limited-access highway data conforming to a spatial data structure comprising barrels divided into zones, wherein boundaries of the barrels are defined by physical roadway configuration changes and permanent changes in regulatory use of the limited-access highway lanes and wherein boundaries of the zones are defined by changes in traffic conditions along the limited-access highway resulting from entry ramps and exit ramps and locations of motorist information devices and regulatory devices that provide changeable information and active traffic management control of the limited-access highway;
e. If the look ahead time period is provided by a vehicle's operator or an automated vehicle, computing a look ahead distance (LEN) using the look ahead time period and the at least one of processed and processed predicted lane-specific limited-access highway data;
f. identifying zones for the look ahead distance;
g. computing average speed and travel time in adjacent lanes for the look ahead distance;
h. providing the at least one of processed and processed predicted lane-specific limited-access highway data to one or more vehicles, said at least one of processed and processed predicted lane-specific limited-access highway data enabling an in-vehicle guidance assist vehicle module of the one or more vehicles to select a preferred lane and target speed for the preferred lane using a copy or a subset of the static database and the travel time difference between adjacent lanes for the selected look ahead distance or look ahead time.

2. The method of claim 1, wherein the step of providing the at least one of processed and processed predicted lane-specific limited-access highway data to the one or more vehicles is dynamic.

3. The method of claim 1, wherein the step of computing average speed and travel time in adjacent lanes for the look ahead distance is performed by computing the average speed and travel time for each zone or part of each zone.

4. The method of claim 1, further comprising selecting the maximum desired speed in conjunction with the look ahead time period or the look ahead distance.

5. The method of claim 1, further comprising selecting a threshold value of travel time difference by the vehicle's operator or the automated vehicle in conjunction with the look ahead time period, wherein the vehicle's operator or the automated vehicle is notified of the recommended lane if the travel time difference exceeds the threshold value.

6. The method of claim 1, further comprising receiving a message from the ALMAMC module as to the presence of automatic speed enforcement.

7. The method of claim 1, wherein the at least one of processed and processed predicted lane-specific limited-access highway data provided to the in-vehicle guidance assist vehicle module includes one or more of lane volume, lane passenger car equivalent volume, lane average headway, lane density, lane speed, vehicle length by lane, passenger car equivalent volume, incident status information, lane status information, static and dynamic regulatory lane-use data.

8. The method of claim 1, wherein appropriate information decision zones relating to roadway geometrics and roadway traffic information devices are established, and wherein the at least one of processed and processed predicted lane-specific limited-access highway data corresponding to the information decision zones is provided to the one or more vehicles sufficiently in advance of an action required by the one or more vehicles or a vehicle operator of the one or more vehicles to facilitate safe lane changes and speed adjustments in conformance with individual motorist driving preferences.

9. The method of claim 1, further comprising using the at least one of processed and processed predicted lane-specific limited-accessed highway data in conjunction with software in the one or more vehicles.

10. The method of claim 1, further comprising using the at least one of processed and processed predicted lane-specific limited-accessed highway data in conjunction with data provided by the one or more vehicles.

11. The method of claim 1, further comprising using the at least one of processed and processed predicted lane-specific limited-accessed highway data in conjunction with data provided by an occupant of the one or more vehicles.

12. The method of claim 11, wherein the data provided by the occupant of the one or more vehicles includes one or more of vehicle characteristics, vehicle passenger occupancy, highway use preferences, toll preferences, threshold value of travel time difference, and maximum desired speed.

13. The method of claim 1, further comprising periodically providing the data from the static database according to the roadway zone based data structures to the one or more vehicles to update the copy or the subset of the static database in the one or more vehicles.

14. The method of claim 1, wherein the at least one of processed and processed predicted lane-specific limited-access highway data includes one or more of lane based traffic parameter data, TMC traffic incident report data, lane blockage information, lane closure information, limitations on lane use, shoulder information, regulatory lane use data, scheduled roadway closures, dynamic speed limits, current lane speed, volume and occupancy vehicle detector data, camera data, vehicle class based lane restrictions, vehicle overheight restrictions, vehicle overweight restrictions, vehicle occupant requirements, and toll information to assist in the generation of lane selection information and the target speed information for vehicles.

15. The method of claim 1, wherein the at least one of processed and processed predicted lane-specific limited-access highway data is generated based on one or more factors selected from the group consisting of traffic information, limitations on lane use, shoulder information, regulatory lane use data, scheduled roadway closures, toll information, dynamic speed limits, current lane speed, volume and occupancy vehicle detector data, camera data, vehicle class, vehicle overheight restrictions, vehicle overweight restrictions, vehicle occupant calls, and toll information.

16. The method of claim 1, further comprising performing one or more quality check of the processed predicted lane-specific limited-access highway data when the processed predicted lane-specific limited-access highway data is used and substituting current lane-specific limited-access highway data for the processed predicted lane-specific limited-access highway data if the processed predicted lane-specific limited-access highway data is not of sufficient quality.

17. A non-transitory computer-implemented roadway zone based data structure for expressing traffic parameters, incident data, regulatory data and toll information in geographical segments that are appropriate for limited-access highway lane selection and target speed selection for travel time savings for the chosen lanes, said data structure comprising:

a. at least one interface for receiving unprocessed lane-specific limited-access highway data from a traffic management center; and
b. a processor coupled to the at least one interface, wherein the processor receives the unprocessed lane-specific limited-access highway data through the at least one interface, processes the unprocessed lane-specific limited-access highway data, generates at least one of processed and processed predicted lane-specific limited-access highway data conforming to a spatial data structure comprising barrels divided into zones, wherein boundaries of the barrels are defined by physical roadway configuration changes and permanent changes in regulatory use of the limited-access highway lanes and wherein boundaries of the zones are defined by changes in traffic conditions along the limited-access highway resulting from entry ramps and exit ramps and locations of motorist information devices and regulatory devices that provide changeable information and active traffic management control of the limited-access highway and transmits the at least one of processed and processed predicted lane-specific limited-access highway data to at least one vehicle in a form appropriate for limited-access highway lane selection and target speed selection for travel time savings for the chosen lanes based on travel time difference between adjacent lanes.

18. The data structure of claim 17, further comprising a computer storage for storing the at least one of processed and processed predicted lane-specific limited-access highway data, wherein the processor receives the unprocessed lane-specific limited-access highway data through the at least one interface, processes the unprocessed lane-specific limited-access highway data, and outputs the at least one of processed and processed predicted lane-specific limited-access highway data to the computer storage in a form appropriate for limited-access highway lane selection and target speed selection for travel time savings for the chosen lanes.

19. The data structure of claim 18, wherein the at least one of processed and processed predicted lane-specific limited-access highway data is transmitted to the at least one vehicle from the computer storage.

20. A system for assisting in selection of driving lanes and target speeds for vehicles for travel time savings, comprising:

a. an interface for receiving unprocessed lane-specific limited-access highway data from a traffic management center (TMC), advanced lane management assist management center (ALMAMC), or another data source;
b. a processor coupled to the interface, wherein the processor receives the unprocessed lane-specific limited-access highway data through the interface, processes the unprocessed lane-specific limited-access highway data, generates at least one of processed and processed predicted lane-specific limited-access highway data conforming to a spatial data structure comprising barrels divided into zones, wherein boundaries of the barrels are defined by physical roadway configuration changes and permanent changes in regulatory use of the limited-access highway lanes and wherein boundaries of the zones are defined by changes in traffic conditions along the limited-access highway resulting from entry ramps and exit ramps and locations of motorist information devices and regulatory devices that provide changeable information and active traffic management control of the limited-access highway, and transmits the at least one of processed and processed predicted lane-specific limited-access highway data to one or more vehicles; and
c. one or more of a lane closure guidance module, lane and speed limit requirements module, dynamic lane use requirements module, toll information module, module for checking detector values for accuracy, module for formatting traffic data, miscellaneous data module, and static database module, said one or more module operatively coupled to the processor for developing driving lane and target speed selection for travel time savings for chosen lanes based on travel time difference between adjacent lanes.

21. A system of claim 20, further comprising a computer storage for storing the at least one of processed and processed predicted lane-specific limited-access highway data, said computer storage being coupled to the processor, wherein the processor receives the unprocessed lane-specific limited-access highway data through the interface, processes the unprocessed lane-specific limited-access highway data, and outputs the at least one of processed and processed predicted lane-specific limited-access highway data to the computer storage.

22. A system of claim 21, further comprising a transmitter operatively coupled to the computer storage for transmitting the at least one of processed and processed predicted lane-specific limited-access highway data to the one or more vehicles.

23. A system of claim 22, wherein the processor performs one or more quality check of the processed predicted lane-specific limited-access highway data when the processed predicted lane-specific limited-access highway data is used and substitutes current lane-specific limited-access highway data for the processed predicted lane-specific limited-access highway data if the processed predicted lane-specific limited-access highway data is not of sufficient quality.

Patent History
Publication number: 20170322046
Type: Application
Filed: Jan 23, 2017
Publication Date: Nov 9, 2017
Inventor: Robert Gordon (Plainview, NY)
Application Number: 15/412,494
Classifications
International Classification: G01C 21/36 (20060101); G08G 1/01 (20060101); B60W 30/18 (20120101); G05D 1/00 (20060101); B60W 40/08 (20120101); G08G 1/0967 (20060101); G05D 1/02 (20060101); B60W 40/08 (20120101);