US20150316387A1 - Detailed map format for autonomous driving - Google Patents

Detailed map format for autonomous driving Download PDF

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Publication number
US20150316387A1
US20150316387A1 US14301079 US201414301079A US2015316387A1 US 20150316387 A1 US20150316387 A1 US 20150316387A1 US 14301079 US14301079 US 14301079 US 201414301079 A US201414301079 A US 201414301079A US 2015316387 A1 US2015316387 A1 US 2015316387A1
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lane
traffic
associated
plurality
map format
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US14301079
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Kentaro Ichikawa
Michael J. Delp
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Toyota Motor Engineering and Manufacturing North America Inc
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Toyota Motor Engineering and Manufacturing North America Inc
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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/26Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
    • G01C21/28Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • G01C21/30Map- or contour-matching
    • G01C21/32Structuring or formatting of map data
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/26Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
    • G01C21/34Route searching; Route guidance
    • G01C21/36Input/output arrangements of navigation systems
    • G01C21/3626Details of the output of route guidance instructions
    • G01C21/3658Lane guidance
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/09Arrangements for giving variable traffic instructions
    • G08G1/095Traffic lights

Abstract

A computer-readable detailed map format is disclosed. The detailed map format includes a plurality of lane segments and a plurality of lane links. Each of the lane links can extend between two lane segments across a traffic intersection. Each of the lane links can also be associated with one of a plurality of traffic signals. A transition rule is associated with a first lane link and based on information associated with the one of the plurality of traffic signals associated with the first lane link. An interlock rule can be based on information associated with the one of the plurality of traffic signals associated with a second lane link. The first lane link and second lane link can be associated with different traffic signals and can extend between different lane segments across the traffic intersection.

Description

    CROSS-REFERENCE TO RELATED APPLICATIONS
  • This application is a continuation-in-part of U.S. application Ser. No. 14/265,370, filed Apr. 30, 2014, which is hereby incorporated by reference in its entirety.
  • BACKGROUND
  • Fully or highly automated, e.g. autonomous or self-driven, driving systems are designed to operate a vehicle on the road either without or with low levels of driver interaction or other external controls. Autonomous driving systems require certainty in the position of and distance to geographic features surrounding the vehicle with a sufficient degree of accuracy to adequately control the vehicle. Details about the road or other geographic features surrounding the vehicle can be recorded on a detailed virtual map. The more accurate the detailed virtual map, the better the performance of the autonomous driving system. Existing virtual maps do not include sufficient or sufficiently accurate geographic feature details for optimized autonomous operation.
  • Autonomous driving systems can also be programmed to follow transition rules, or traffic operation rules, associated with a traffic intersection when localized to (exactly positioned in respect to) the traffic intersection. Though an autonomous driving system can recognize and implement some transition rules by observing traffic signals along the a navigation route of the autonomous vehicle, information related to additional traffic signals and the associated actions of other vehicles within the traffic intersection can improve the performance of the autonomous driving system.
  • SUMMARY
  • The detailed map format described here can improve operation of a highly-automated or autonomous vehicle at traffic intersections by improving both localization (exact positioning) and control over the vehicle. The detailed map format can include lane segments associated with branches of a traffic intersection and lane links that indicate the transition path between the lane segments across the traffic intersection. Each of the lane links can be associated with transition rules governing the action of the autonomous vehicle based on the state of detected traffic signals. Each of the transition rules can be further associated with interlock rules that provide assumptions regarding the actions of other vehicles through the traffic intersection as based on the state of traffic signals that are not directly detected by the autonomous vehicle.
  • In one implementation, a computer-readable map format is disclosed. The map format includes a plurality of lane segments, each lane segment associated with a branch of a traffic intersection; a plurality of lane links, each lane link associated with two of the plurality of lane segments and extending between two of the branches of the traffic intersection; a plurality of traffic signals, each traffic signal associated with at least one of the plurality of lane links; a transition rule associated with a first lane link, wherein the transition rule is based on information associated with the one of the plurality of traffic signals associated with the first lane link; and an interlock rule based on information associated with the one of the plurality of traffic signals associated with a second lane link.
  • In another implementation, a computer-readable map format is disclosed. The map format includes a plurality of lane segments; a plurality of lane links, each lane link extending between two lane segments across a traffic intersection and associated with one of a plurality of traffic signals; a transition rule associated with a first lane link, wherein the transition rule is based on information associated with the one of the plurality of traffic signals associated with the first lane link; and an interlock rule based on information associated with the one of the plurality of traffic signals associated with a second lane link.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • The description herein makes reference to the accompanying drawings wherein like reference numerals refer to like parts throughout the several views, and wherein:
  • FIG. 1 is a block diagram of a computing device;
  • FIG. 2 is a schematic illustration of an autonomous vehicle including the computing device of FIG. 1;
  • FIG. 3 shows an example two-dimensional representation of a portion of a four-way intersection as represented within a detailed map format for use with the autonomous vehicle of FIG. 2;
  • FIG. 4 shows the example two-dimensional representation of the portion of the four-way intersection of FIG. 3 including a representation of transition and interlock rules; and
  • FIG. 5 shows an example two-dimensional representation of a portion of another four-way intersection as represented within a detailed map format for use with the autonomous vehicle of FIG. 2.
  • DETAILED DESCRIPTION
  • A computer-readable, highly detailed map format for an autonomous vehicle is disclosed. The detailed map format includes information representing the geographical location, travel direction, and speed limit of lanes on a road using lane segments formed of waypoints. Beyond this basic information, the detailed map format also includes lane links that represent transitions between lane segments across traffic intersections, transition rules based on the state of detected traffic signals that govern the actions of the autonomous vehicle across lane links, and interlock rules based on the inferred state of undetected traffic signals that would govern the actions of other vehicles across different lane links. The use of lane links, transition rules, and interlock rules within a detailed map formant can greatly improve the performance of an autonomous driving system.
  • FIG. 1 is a block diagram of a computing device 100, for example, for use with the autonomous driving system. The computing device 100 can be any type of vehicle-installed, handheld, desktop, or other form of single computing device, or can be composed of multiple computing devices. The processing unit in the computing device can be a conventional central processing unit (CPU) 102 or any other type of device, or multiple devices, capable of manipulating or processing information. A memory 104 in the computing device can be a random access memory device (RAM) or any other suitable type of storage device. The memory 104 can include data 106 that is accessed by the CPU 102 using a bus 108.
  • The memory 104 can also include an operating system 110 and installed applications 112, the installed applications 112 including programs that permit the CPU 102 to perform automated driving methods using the detailed map format described below. The computing device 100 can also include secondary, additional, or external storage 114, for example, a memory card, flash drive, or any other form of computer readable medium. The installed applications 112 can be stored in whole or in part in the external storage 114 and loaded into the memory 104 as needed for processing.
  • The computing device 100 can also be in communication with one or more sensors 116. The sensors 116 can capture data and/or signals for processing by an inertial measurement unit (IMU), a dead-reckoning system, a global navigation satellite system (GNSS), a light detection and ranging (LIDAR) system, a radar system, a sonar system, an image-based sensor system, or any other type of system capable of capturing information specific to the environment surrounding a vehicle for use in creating a detailed map format as described below, including information specific to objects such as features of the route being travelled by the vehicle or other localized position data and/or signals and outputting corresponding data and/or signals to the CPU 102.
  • In the examples described below, the sensors 116 can capture, at least, signals for a GNSS or other system that determines vehicle position and velocity and data for a LIDAR system or other system that measures vehicle distance from lane lines (e.g., route surface markings or route boundaries), obstacles, objects, or other environmental features including traffic lights and road signs. The computing device 100 can also be in communication with one or more vehicle systems 118, such as vehicle braking systems, vehicle propulsions systems, etc. The vehicle systems 118 can also be in communication with the sensors 116, the sensors 116 being configured to capture data indicative of performance of the vehicle systems 118.
  • FIG. 2 is a schematic illustration of an autonomous vehicle 200 including the computing device 100 of FIG. 1. The computing device 100 can be located within the vehicle 200 as shown in FIG. 2 or can be located remotely from the vehicle 200 in an alternate location (not shown). If the computing device 100 is located remotely from the vehicle 200, the vehicle 200 can include the capability of communicating with the computing device 100.
  • The vehicle 200 can also include a plurality of sensors, such as the sensors 116 described in reference to FIG. 1. One or more of the sensors 116 shown can be configured to capture the distance to objects within the surrounding environment for use by the computing device 100 to estimate position and orientation of the vehicle 200, images for processing by an image sensor, vehicle position in global coordinates based on signals from a plurality of satellites, or any other data and/or signals that could be used to determine the current state of the vehicle or determine the position of the vehicle 200 in respect to its environment for use in either creating a detailed map format or comparing the vehicle's 200 position to the detailed map format. Recognized geographic features such as those described below can be used to build a detailed map format, and objects such as other vehicles can be recognized and excluded from the detailed map format.
  • Map formats can be constructed using geographic features captured by the vehicle 200 such as lane lines and curbs proximate the vehicle 200 as it travels a route. These geographic features can be captured using the above described LIDAR system and/or cameras in combination with an algorithm such as random sample consensus (RANSAC) to find lines, record the position of the vehicle 200, and collect data on position from a GNSS and/or an IMU. The captured geographic features can then be manipulated using a simultaneous localization and mapping (SLAM) technique to position all of the geographic features in relation to the vehicle's 200 position. Some of the geographic features can be categorized as lane borders, and lane centers can be determined based on the lane borders. Alternatively, map formats can be constructed using overhead images (e.g. satellite images) of geographic features traced by a map editor that allows selection of different categories for each geographic feature.
  • FIG. 3 shows an example two-dimensional representation of a portion of a four-way intersection as represented within a detailed map format for use with the autonomous vehicle 200 of FIG. 2. The intersection in this example map format includes four branches 300, 302, 304, 306. Each of the branches 300, 302, 304, 306 can include traffic lanes represented by portions of lane segments 308, 310, 312, 314, 316, 318, 320, 322, 324, 326. Each of the lane segments 308, 310, 312, 314, 316, 318, 320, 322, 324, 326 can end in a waypoint 328, 330, 332, 334, 336, 338, 340, 342, 344, 346 at the traffic intersection.
  • For example, the lane segment 308 extends from the waypoint 328 away from the intersection and the lane segment 310 extends to the waypoint 330 toward the intersection. Information can be associated with the waypoints 328, 330, 332, 334, 336, 338, 340, 342, 344, 346 and stored as part of the map format. For example, each waypoint 328, 330, 332, 334, 336, 338, 340, 342, 344, 346 can include information such as geographical location, lane speed, and lane direction. The map information associated with the lanes and intersection can be stored, for example, in the form of spline points or as curves with knot vectors in the memory 104 of the computing device 100 or can be available from a remote location.
  • In the example map format shown in FIG. 3, the lane segment 310 is shown as having a bottom-to-top direction by the arrow at the end of the lane segment 310 touching the waypoint 330 and the lane segment 316 is shown as having a right-to-left direction by the arrow at the end of the lane segment 316 touching the waypoint 336. The overall computer-readable map format can be stored in plain text, binary, or xml, for example. The basic map information can be gathered from a route network definition file (RNDF) or any other available source. However, this basic map information is not sufficient to operate the autonomous vehicle 200 safely through the traffic intersection.
  • Additional detail can be added to the map format in order to improve the map format for use with the autonomous vehicle 200. As shown in FIG. 3, a plurality of lane links 348, 350, 352, 354, 356, 357, 358, 360, 362, 364, 366, 368 can be included in the map format. Each of the lane links 348, 350, 352, 354, 356, 357, 358, 360, 362, 364, 366, 368 can be associated with two of the lane segments 308, 310, 312, 314, 316, 318, 320, 322, 324, 326 and can extend between two of the branches 300, 302, 304, 306 of the traffic intersection. For example, the lane link 352 extends between the lane segment 316 and the lane segment 308 and represents a left turn for the autonomous vehicle 200 from branch 302 of the traffic intersection to branch 300 of the traffic intersection. In another example, the lane link 350 extends between the lane segment 316 and the lane segment 324 and represents a pass straight through the traffic intersection from branch 302 to branch 306.
  • In addition to the lane links 348, 350, 352, 354, 356, 357, 358, 360, 362, 364, 366, 368, a plurality of traffic signals can be included in the map format. Each of the traffic signals can be associated with at least one of the lane links 348, 350, 352, 354, 356, 357, 358, 360, 362, 364, 366, 368 and information associated with the traffic signals can include a geographical location, a traffic signal type, and a traffic signal state. Traffic signal type can include information on the structure and orientation of a traffic light or traffic sign. Traffic signal structure and orientation for a traffic light can include “vertical three,” “vertical three left arrow,” “horizontal three,” “right arrow,” etc. Traffic signal state for a traffic light can include, for example, “green,” “green arrow,” “yellow,” “blinking yellow,” or “red.”
  • In FIG. 3, each of the traffic signals shown is a traffic light 369, 370, 372, 374, 376, 378, 380, 382 having a “vertical three” structure and orientation. Each pair of traffic signals at each branch 300, 302, 304, 306 of the traffic intersection can be configured to have the same structure and orientation as well as the same state. In one example, the traffic light 376 can be associated both with the lane link 348 and the lane link 350. This relationship is shown by using the same pattern to display both the lane links 348, 350 and the traffic light 376 within the map format. Given the structure of the intersection and position of the traffic light 376 in reference to the lane segments 316, 318, 324, the lane link 348 is understood to indicate a right turn from the lane segment 316 to the lane segment 318 and the lane link 350 is understood to indicate a straight pass through the intersection from the lane segment 316 to the lane segment 324.
  • Similarly, the traffic light 378 can be associated with the lane link 352 and displayed as such using the same pattern in the map format. The lane link 352 is understood to indicate a left turn from the lane segment 316 to the lane segment 308. Both of the traffic lights 376, 378 directing traffic exiting branch 302 of the traffic intersection can have the same structure, orientation, and state at the same time. In one more example, the traffic light 382 can be associated with the lane link 354, where the lane link 354 represents a right turn from the lane segment 322 to the lane segment 324.
  • FIG. 4 shows the example two-dimensional representation of the portion of the four-way intersection of FIG. 3 including a representation of transition and interlock rules. Transition rules can be used to control the autonomous vehicle 200 to follow one of the lane links 348, 350, 352, 354, 356, 357, 358, 360, 362, 364, 366, 368 based on the state of at least one of the traffic signals and can be saved in the detailed map format. Example transition rules can include “stop,” “prefer stop,” “go,” “stop and go,” and “yield,” with each of the transition rules indicating an available maneuver for either the autonomous vehicle 200 or any other vehicle approaching the four-way intersection.
  • As described in FIG. 3, the state of the traffic light 376 can govern the type of maneuver the autonomous vehicle 200 can undertake as associated with the lane links 348, 350. This governance is also reflected in FIG. 4. For example, a transition rule “go” is highlighted as associated with the lane links 348, 350 and can direct the autonomous vehicle 200 to either proceed straight through the intersection from the lane segment 316 to the lane segment 324 or proceed in a right turn through the intersection from the lane segment 316 to the lane segment 318 given the state of the traffic light 376 of “green.”
  • This transition rule of “go” is represented within the map format using a first line type, a dashed line, in association with the lane links 348, 350. The “green” state of the traffic light 376 is also shown with a specific line type, in this case, a solid line, and can, for example, be detected by one or more of the sensors 116 disposed on the autonomous vehicle 200 when the autonomous vehicle 200 is located on lane segment 316. The lane links 348, 350 and the traffic light 376 are shown in a bold style to indicate that in the example of FIG. 4, the traffic light 376 is directly detected by the autonomous vehicle 200.
  • Though a transition rule can be based on the state of one of the traffic signals as directly detected by the autonomous vehicle 200 while navigating along a route, additional information regarding the transition of other vehicles through the traffic intersection would improve the performance of the automated driving system. Hence, the detailed map format has been improved to include interlock rules. Each interlock rule can be inferred from one of the transition rules governed by an interlocked traffic signal. An interlocked traffic signal refers to a traffic signal having a specific traffic signal state based on the traffic signal state of a different traffic signal.
  • For example, if the autonomous vehicle 200 is positioned along lane segment 316, the state of at least one of the traffic lights 376, 378 can be captured directly by the autonomous vehicle 200, for example, as “green.” The state of the traffic lights 372, 374 can then be inferred to be “red,” which is shown in the detailed map format using a different line style than the solid line used for the traffic lights 376, 378, since the traffic lights 372, 374 are interlocked traffic signals to the traffic lights 376, 378 given the structure of the traffic intersection. That is, if traffic is free to proceed from the branch 302 to the branch 306, traffic must not be allowed to proceed from the branch 300 to the branch 304 at the same time. Further, the transition rule “go” as associated with the lane links 348, 350 and the traffic light 376 when the state of the traffic light 376 is “green” leads to an inference of the interlock rule “stop” associated with the lane link 366 and the traffic light 374 based on the interlocked state of the traffic light 374 as “red.”
  • In another example, the transition rule “go” associated with the lane links 348, 350 and the traffic light 376 indicating that the autonomous vehicle 200 can proceed either straight or right through the traffic intersection when the state of the traffic light 376 is “green” can lead to the inference of the interlock rule “stop” associated with the lane link 356 and the traffic light 380 indicating that another vehicle must stop at the traffic intersection at the end of the lane segment 322 and cannot proceed through the traffic intersection to the lane segment 308 since the state of the traffic light 380 is inferred to be “red” given the state of the traffic light 376 being “green.” The interlock rule “stop” as associated with the traffic lights 374, 380 and the lane links 356, 366 and as inferred from the transition rule “go” as associated with the traffic light 376 and the lane links 348, 350 is shown in this example map format by using the same type of line to represent the lane links 356, 366, a closely spaced dotted line.
  • In the prior two examples, the lane links 348, 350 and the lane links 356, 366 are associated with different traffic signals, specifically, the traffic light 376 and the traffic lights 374, 380, and extend between different branches 300, 302, 304, 306 of the traffic intersection. Any number of interlock rules can be inferred from a given transition rule depending on the structure of the traffic intersection as detailed within the map format. In the example map format of FIG. 4, the lane links 348, 350, 360, 362 are all shown with the same line style. This line style is associated with the traffic light 376 having a “green” state, the lane links 348, 350 having “go” transition rules, the traffic light 369 having an interlocked “green” state, and the lane links 360, 362 having “go” interlock rules. That is, if the autonomous vehicle 200 is free to travel along the lane links 348, 350 given a “green” state for the traffic light 376, another vehicle would also be free to travel along the lane links 360, 362 based on an interlocked “green” state for the traffic light 369.
  • Another set of interlock rules represented in the example map format of FIG. 4 include “stop and go” interlock rules for the lane links 354, 368 based on the interlocked state of “red” for the traffic lights 372, 382 given the detected state of “green” for the traffic light 376. That is, when the state of the traffic light 376 is “green,” the interlocked state of the traffic lights 372, 382 is “red,” and any vehicle seeking to turn right along either of the lane segments 354, 368 would need to first stop at the traffic intersection and check for oncoming traffic before turning right. A final set of interlock rules represented in the example map format of FIG. 4 include “yield” interlock rules for the lane links 352, 358 based on the interlocked state of “green” for the traffic lights 370, 378 given the detected state of “green” for the traffic light 376. That is, when the state of the traffic light 376 is “green,” the interlocked state of the traffic lights 370, 378 is also “green,” and any vehicle seeking to turn left along either of the lane segments 352, 358 would need to first yield to oncoming vehicles before turning left.
  • Though the example transition rules and interlock rules that are possible to guide vehicles through the traffic intersection based on the traffic signal types and traffic signal states described in reference to FIG. 4 reflect commonly understood traffic signals in the United States, other traffic signals types and traffic signal states are also possible that could influence the operation of the transition rules and the interlock rules.
  • FIG. 5 shows an example two-dimensional representation of a portion of another four-way intersection as represented within a detailed map format for use with the autonomous vehicle 200 of FIG. 2. The intersection in this example map format also includes four branches 400, 402, 404, 406. The branches 402, 406 include five and six lanes, respectively, represented by waypoints 408, 410, 412, 414, 416, 420, 422, 424, 426, 428, 430 at the end of the lane segments 432, 434, 436, 438, 440, 442, 444, 446, 448, 450, 452. Only two of the branches 402, 406 are described for simplicity. Similar information as described above in reference to FIG. 3 is associated with the waypoints 408, 410, 412, 414, 416, 420, 422, 424, 426, 428, 430 shown in FIG. 4.
  • Each of the lane segments 432, 434, 436, 438, 440, 442, 444, 446, 448, 450, 452 within the branches 402, 406 can be further associated with borders formed of one or more border segments extending between at least two borderpoints. For simplicity, only a few of the border segments and borderpoints are numbered in the example map format shown in FIG. 4. For example, border segment 454 extends between borderpoints 456, 458 and border segment 460 extends between the borderpoints 462, 464. These border segments 454, 460 can be associated with the lane segments 432, 434 and the lane segments 448, 450, respectively. The border segments 454, 460 and borderpoints 456, 458, 462, 464 can be associated with various border types (e.g. solid lines and dashed lines) and border colors (e.g. white and yellow) for use in establishing driving rules to associate with the lane segments 432, 434, 448, 450. The use of driving rules applies while the autonomous vehicle 200 approaches the traffic intersection, but does not directly impact the various transition rules and interlock rules further described below.
  • FIG. 5 also shows additional features added to the map format in order to improve the map format for use with the autonomous vehicle 200 of FIG. 2. First, two of the lane segments 434, 436 are associated with a stop line 466 within branch 402 of the traffic intersection. The stop line 466 can be linked to the end of the lanes associated with the lane segments 434, 436 and information associated with the stop line 466 can include a geographical location of a position where the vehicle 200 must stop before the traffic intersection. In the example of FIG. 5, the stop line 466 extends between border segments associated with the lane segments 434, 436 and denotes the geographical location at which the autonomous vehicle 200 should be positioned if stopping in front of the traffic intersection. Another stop line 468 is also shown as associated with the three lane segments 446, 448, 450 within branch 406 of the traffic intersection.
  • The additional information provided by the stop lines 466, 468 is useful in operation of the autonomous vehicle 200 because the stop lines 466, 468 allow the autonomous vehicle 200 to be positioned at the traffic intersection in a manner consistent with manual operation of a vehicle. For example, if the autonomous vehicle 200 approaches the traffic intersection along the lane segment 434, instead of stopping at the waypoint 410 denoting the end of the lane segment 434, the autonomous vehicle 200 can be controlled to move forward to the stop line 466. This maneuver is more consistent with how a driver would manually operate a vehicle, for example, to pull forward to a designated location when stopping at a traffic intersection. Though not shown, crosswalks can also be included in the detailed map format in a manner similar to that used for the stop lines 466, 468. Information associated with the crosswalks can include a geographical location of a position of the crosswalk and a driving rule associated with the crosswalk that directs the automated vehicle system to implement additional safety protocols.
  • Traffic signals are also included in the map format shown in FIG. 5. As described above in reference to FIGS. 3 and 4, traffic signals can include information such as geographical location, traffic signal type, and traffic signal state. Traffic signal type can include information on the structure and orientation of a traffic light or traffic sign. Traffic signal structure and orientation for a traffic light can include “vertical three,” “vertical three left arrow,” “horizontal three,” “right arrow,” etc. Traffic signal state for a traffic light can include, for example, “green,” “green arrow,” “yellow,” “blinking yellow,” or “red.” In the map format shown in FIG. 5, four traffic lights 470, 472, 474, 476 are labeled within the branches 402, 406 of the traffic intersection. As was described above in reference to FIGS. 3 and 4, each of the traffic lights 470, 472, 474, 476 can be associated with at least one lane link and a transition rule governing the operation of the autonomous vehicle 200 can be further associated with each lane link.
  • In FIG. 5, only eight lane links 477, 478, 480, 482, 484, 486, 488, 490 are labeled, those associated with the autonomous vehicle 200 exiting the branches 402, 406 of the traffic intersection. For example, the lane link 478 extends from the lane segment 432 to the lane segment 442. The operation of the autonomous vehicle 200 across this lane link 478 can be controlled, using a transition rule, based on the state of the traffic light 474 and is represented within the map format by using the same pattern for the lane link 478 as is used to display the traffic light 474. In another example, the lane link 486 extends from the lane segment 448 to the lane segment 438. The operation of the autonomous vehicle 200 across this lane link 486 can be controlled, again using a transition rule, based on the state of the traffic light 472 and is represented within the map format by using the same pattern for the lane link 486 as is used to display the traffic light 472.
  • In addition to including transition rules associated with the lane links 477, 478, 480, 482, 484, 486, 488, 490 and based on the state of the traffic lights 470, 472, 474, 476, the map format of FIG. 5 can include interlock rules inferred from the transition rules. For example, given the transition rule “go” associated with the lane link 480 that indicates that the autonomous vehicle 200 can proceed from the lane segment 434 to the lane segment 444 across the traffic intersection if the traffic light 476 has a state of “green,” an interlock rule “go” can be inferred for the lane link 486 that other vehicles are also able to proceed from the lane segment 448 to the lane segment 438 across the traffic intersection since the traffic light 472 will have a state of “green” while the traffic light 476 has a state of “green.” Again, interlock rules are based on the states of traffic signals that cannot be directly detected by the sensors 116 of the autonomous vehicle 200 using traffic operation rules based on the given structure of a traffic intersection.
  • The foregoing description relates to what are presently considered to be the most practical embodiments. It is to be understood, however, that the disclosure is not to be limited to these embodiments but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. The scope of the claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures as is permitted under the law.

Claims (20)

    What is claimed is:
  1. 1. A computer-readable map format, comprising:
    a plurality of lane segments, each lane segment associated with a branch of a traffic intersection;
    a plurality of lane links, each lane link associated with two of the plurality of lane segments and extending between two of the branches of the traffic intersection;
    a plurality of traffic signals, each traffic signal associated with at least one of the plurality of lane links;
    a transition rule associated with a first lane link, wherein the transition rule is based on information associated with the one of the plurality of traffic signals associated with the first lane link; and
    an interlock rule based on information associated with the one of the plurality of traffic signals associated with a second lane link.
  2. 2. The map format of claim 1, wherein the first lane link and the second lane link are associated with different traffic signals.
  3. 3. The map format of claim 1, wherein the first lane link and the second lane link extend between different branches of the traffic intersection.
  4. 4. The map format of claim 1, wherein the information associated with each traffic signal includes a geographical location and a traffic signal type and a traffic signal state.
  5. 5. The map format of claim 4, wherein the traffic signal type includes information regarding structure and orientation for at least one of a traffic light and a traffic sign.
  6. 6. The map format of claim 4, wherein the traffic signal type is a traffic light and the traffic signal state includes at least one of green, green arrow, yellow, blinking yellow, and red.
  7. 7. The map format of claim 4, wherein the transition rule is based on the traffic signal state of the one of the plurality of traffic signals associated with the first lane link.
  8. 8. The map format of claim 4, wherein the interlock rule is based on an inferred traffic signal state for the one of the plurality of traffic signals associated with the second lane link.
  9. 9. The map format of claim 1, further comprising:
    a stop line associated with an end of at least one of the plurality of lane segments, wherein information associated with the stop line includes a geographical location, the geographical location representing a position where a vehicle must stop before the traffic intersection.
  10. 10. The map format of claim 1, wherein the lane segment is formed from a plurality of waypoints and wherein information associated with each waypoint includes at least one of a geographical location and a lane speed and a lane direction.
  11. 11. A computer-readable map format, comprising:
    a plurality of lane segments;
    a plurality of lane links, each lane link extending between two lane segments across a traffic intersection and associated with one of a plurality of traffic signals;
    a transition rule associated with a first lane link, wherein the transition rule is based on information associated with the one of the plurality of traffic signals associated with the first lane link; and
    an interlock rule based on information associated with the one of the plurality of traffic signals associated with a second lane link.
  12. 12. The map format of claim 11, wherein the first lane link and the second lane link are associated with different traffic signals.
  13. 13. The map format of claim 11, wherein the first lane link and the second lane link extend between a different set of two lane segments across the traffic intersection.
  14. 14. The map format of claim 11, wherein the information associated with each traffic signal includes a geographical location and a traffic signal type and a traffic signal state.
  15. 15. The map format of claim 14, wherein the traffic signal type includes information regarding structure and orientation for at least one of a traffic light and a traffic sign.
  16. 16. The map format of claim 14, wherein the traffic signal type is a traffic light and the traffic signal state includes at least one of green, green arrow, yellow, blinking yellow, and red.
  17. 17. The map format of claim 14, wherein the transition rule is based on the traffic signal state of the one of the plurality of traffic signals associated with the first lane link.
  18. 18. The map format of claim 14, wherein the interlock rule is based on an inferred traffic signal state for the one of the plurality of traffic signals associated with the second lane link.
  19. 19. The map format of claim 11, further comprising:
    a stop line associated with an end of at least one of the plurality of lane segments, wherein information associated with the stop line includes a geographical location, the geographical location representing a position where a vehicle must stop before the traffic intersection.
  20. 20. The map format of claim 11, wherein the lane segment is formed from a plurality of waypoints and wherein information associated with each waypoint includes at least one of a geographical location and a lane speed and a lane direction.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150363652A1 (en) * 2014-06-16 2015-12-17 Thinkware Corporation Electronic apparatus, control method of electronic apparatus and computer readable recording medium
WO2018044340A1 (en) * 2016-08-29 2018-03-08 Baidu Usa Llc Method and system to construct surrounding environment for autonomous vehicles to make driving decisions
US10000216B2 (en) 2012-11-30 2018-06-19 Waymo Llc Engaging and disengaging for autonomous driving
US10082789B1 (en) 2010-04-28 2018-09-25 Waymo Llc User interface for displaying internal state of autonomous driving system
US10093324B1 (en) * 2010-04-28 2018-10-09 Waymo Llc User interface for displaying internal state of autonomous driving system

Citations (47)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3613073A (en) * 1969-05-14 1971-10-12 Eugene Emerson Clift Traffic control system
US4704610A (en) * 1985-12-16 1987-11-03 Smith Michel R Emergency vehicle warning and traffic control system
US4775865A (en) * 1985-12-16 1988-10-04 E-Lited Limited, A California Limited Partnership Emergency vehicle warning and traffic control system
US4884072A (en) * 1985-09-12 1989-11-28 Heinrich Horsch Device for photographic monitoring of cross-roads
US5041828A (en) * 1987-08-19 1991-08-20 Robot Foto Und Electronic Gmbh U. Co. Kg Device for monitoring traffic violating and for recording traffic statistics
US5278554A (en) * 1991-04-05 1994-01-11 Marton Louis L Road traffic control system with alternating nonstop traffic flow
US5798949A (en) * 1995-01-13 1998-08-25 Kaub; Alan Richard Traffic safety prediction model
US5801646A (en) * 1997-08-22 1998-09-01 Pena; Martin R. Traffic alert system and method for its use
US5873674A (en) * 1996-12-05 1999-02-23 Hohl; Barney K. Roadway safety warning system and method of making same
US6232889B1 (en) * 1999-08-05 2001-05-15 Peter Apitz System and method for signal light preemption and vehicle tracking
US6317058B1 (en) * 1999-09-15 2001-11-13 Jerome H. Lemelson Intelligent traffic control and warning system and method
US6338021B1 (en) * 1999-09-29 2002-01-08 Matsushita Electric Industrial Co., Ltd. Route selection method and system
US6418371B1 (en) * 1998-02-27 2002-07-09 Mitsubishi International Gmbh Traffic guidance system
US20030016143A1 (en) * 2001-07-23 2003-01-23 Ohanes Ghazarian Intersection vehicle collision avoidance system
US6919823B1 (en) * 1999-09-14 2005-07-19 Redflex Traffic Systems Pty Ltd Image recording apparatus and method
US20060184321A1 (en) * 2005-02-17 2006-08-17 Denso Corporation Navigation system, program thereof and map data thereof
US20060224303A1 (en) * 2005-03-30 2006-10-05 Denso Corporation Navigation system and program for the same
US20070021912A1 (en) * 2005-01-06 2007-01-25 Aisin Aw Co., Ltd. Current position information management systems, methods, and programs
US20070200730A1 (en) * 2006-02-27 2007-08-30 Woo Jeon Green Co., Ltd. Integrated traffic signal, sign and information display device
US20070296610A1 (en) * 2006-06-24 2007-12-27 Machinery Verification & Documentation Service, Inc. Traffic light safety zone
US20080012726A1 (en) * 2003-12-24 2008-01-17 Publicover Mark W Traffic management device and system
US20080097689A1 (en) * 2004-08-04 2008-04-24 Speedalert Pty Ltd An information apparatus for an operator of a land or water based motor driven conveyance
US20080162027A1 (en) * 2006-12-29 2008-07-03 Robotic Research, Llc Robotic driving system
US20080172171A1 (en) * 2007-01-17 2008-07-17 Gregory Mikituk Kowalski Methods and systems for controlling traffic flow
US20080238720A1 (en) * 2007-03-30 2008-10-02 Jin-Shyan Lee System And Method For Intelligent Traffic Control Using Wireless Sensor And Actuator Networks
US20080284616A1 (en) * 2005-10-26 2008-11-20 Azael Flores Rendon Quick return
US20080291052A1 (en) * 2007-05-25 2008-11-27 Spot Devices, Inc. Alert and warning system and method
US20090135024A1 (en) * 2006-03-17 2009-05-28 Park Jin-Gu Display control system of traffic light and display method
US20090312888A1 (en) * 2008-02-25 2009-12-17 Stefan Sickert Display of a relevant traffic sign or a relevant traffic installation
US20090326751A1 (en) * 2008-06-16 2009-12-31 Toyota Jidosha Kabushiki Kaisha Driving assist apparatus
US20100073194A1 (en) * 2002-07-22 2010-03-25 Ohanes Ghazarian Intersection vehicle collision avoidance system
US20110006915A1 (en) * 2009-07-13 2011-01-13 Sower Charles D Turn/no turn on red traffic light signal
US20110025528A1 (en) * 2010-03-02 2011-02-03 Mohammadreza Rejali Control system and a method for information display systems for vehicles on cross roads
US20110080303A1 (en) * 2009-09-01 2011-04-07 Goldberg Allen Computerized traffic signal system
US20110182473A1 (en) * 2010-01-28 2011-07-28 American Traffic Solutions, Inc. of Kansas System and method for video signal sensing using traffic enforcement cameras
US20110187559A1 (en) * 2010-02-02 2011-08-04 Craig David Applebaum Emergency Vehicle Warning Device and System
US8121749B1 (en) * 2008-09-25 2012-02-21 Honeywell International Inc. System for integrating dynamically observed and static information for route planning in a graph based planner
US20120095646A1 (en) * 2009-09-15 2012-04-19 Ghazarian Ohanes D Intersection vehicle collision avoidance system
US20120112927A1 (en) * 2010-11-05 2012-05-10 International Business Machines Corporation Traffic light preemption management system
US20120123640A1 (en) * 2010-04-19 2012-05-17 Toyota Jidosha Kabushiki Kaisha Vehicular control apparatus
EP2466566A1 (en) * 2009-01-23 2012-06-20 Hella KGaA Hueck & Co. Method and device for controlling at least one traffic light assembly of a pedestrian crossing
WO2012163573A1 (en) * 2011-05-31 2012-12-06 Robert Bosch Gmbh Driver assistance system and method for operating a driver assistance system
US20130018572A1 (en) * 2011-07-11 2013-01-17 Electronics And Telecommunications Research Institute Apparatus and method for controlling vehicle at autonomous intersection
US20130038433A1 (en) * 2011-02-10 2013-02-14 Audi Ag Method and system for line-of-sight-independent data transmission
US20130335238A1 (en) * 2011-03-03 2013-12-19 Parallels IP Holdings GmbH Method and device for traffic control
US8712624B1 (en) * 2012-04-06 2014-04-29 Google Inc. Positioning vehicles to improve quality of observations at intersections
US8917190B1 (en) * 2013-01-23 2014-12-23 Stephen Waller Melvin Method of restricting turns at vehicle intersections

Family Cites Families (66)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080147253A1 (en) 1997-10-22 2008-06-19 Intelligent Technologies International, Inc. Vehicular Anticipatory Sensor System
JPH09325913A (en) 1996-06-05 1997-12-16 Toshiba Corp Semiconductor memory
US5926126A (en) 1997-09-08 1999-07-20 Ford Global Technologies, Inc. Method and system for detecting an in-path target obstacle in front of a vehicle
JP3500928B2 (en) 1997-09-17 2004-02-23 トヨタ自動車株式会社 Map data processing device, map data processing method and a map data processing system
US8000897B2 (en) 1997-10-22 2011-08-16 Intelligent Technologies International, Inc. Intersection collision avoidance techniques
US8630795B2 (en) 1999-03-11 2014-01-14 American Vehicular Sciences Llc Vehicle speed control method and arrangement
US8060308B2 (en) 1997-10-22 2011-11-15 Intelligent Technologies International, Inc. Weather monitoring techniques
US7610146B2 (en) 1997-10-22 2009-10-27 Intelligent Technologies International, Inc. Vehicle position determining system and method
US7202776B2 (en) 1997-10-22 2007-04-10 Intelligent Technologies International, Inc. Method and system for detecting objects external to a vehicle
US20080106436A1 (en) 1997-10-22 2008-05-08 Intelligent Technologies International, Inc. In-Vehicle Signage Techniques
US20090043506A1 (en) 1997-10-22 2009-02-12 Intelligent Technologies International, Inc. Method and System for Controlling Timing of Vehicle Transmissions
US7796081B2 (en) 1997-10-22 2010-09-14 Intelligent Technologies International, Inc. Combined imaging and distance monitoring for vehicular applications
US7085637B2 (en) 1997-10-22 2006-08-01 Intelligent Technologies International, Inc. Method and system for controlling a vehicle
US6720920B2 (en) 1997-10-22 2004-04-13 Intelligent Technologies International Inc. Method and arrangement for communicating between vehicles
US7295925B2 (en) 1997-10-22 2007-11-13 Intelligent Technologies International, Inc. Accident avoidance systems and methods
US7110880B2 (en) 1997-10-22 2006-09-19 Intelligent Technologies International, Inc. Communication method and arrangement
US6405132B1 (en) 1997-10-22 2002-06-11 Intelligent Technologies International, Inc. Accident avoidance system
US20080154629A1 (en) 1997-10-22 2008-06-26 Intelligent Technologies International, Inc. Vehicle Speed Control Method and Arrangement
US7629899B2 (en) 1997-10-22 2009-12-08 Intelligent Technologies International, Inc. Vehicular communication arrangement and method
US8965677B2 (en) 1998-10-22 2015-02-24 Intelligent Technologies International, Inc. Intra-vehicle information conveyance system and method
US7418346B2 (en) 1997-10-22 2008-08-26 Intelligent Technologies International, Inc. Collision avoidance methods and systems
US7426437B2 (en) 1997-10-22 2008-09-16 Intelligent Technologies International, Inc. Accident avoidance systems and methods
US7791503B2 (en) 1997-10-22 2010-09-07 Intelligent Technologies International, Inc. Vehicle to infrastructure information conveyance system and method
US7912645B2 (en) 1997-10-22 2011-03-22 Intelligent Technologies International, Inc. Information transfer arrangement and method for vehicles
US8255144B2 (en) 1997-10-22 2012-08-28 Intelligent Technologies International, Inc. Intra-vehicle information conveyance system and method
US8209120B2 (en) 1997-10-22 2012-06-26 American Vehicular Sciences Llc Vehicular map database management techniques
JP3869108B2 (en) 1998-02-23 2007-01-17 株式会社小松製作所 Guided travel method of interference prediction apparatus and an unmanned vehicle of the unmanned vehicle
US6202482B1 (en) 1998-03-23 2001-03-20 Lehighton Electronics, Inc. Method and apparatus for testing of sheet material
JP4791649B2 (en) 2001-05-07 2011-10-12 株式会社ゼンリン Electronic map data, the display control apparatus and a computer program
US6526352B1 (en) 2001-07-19 2003-02-25 Intelligent Technologies International, Inc. Method and arrangement for mapping a road
JP4023201B2 (en) 2002-04-25 2007-12-19 アイシン・エィ・ダブリュ株式会社 Navigation device
US7433889B1 (en) * 2002-08-07 2008-10-07 Navteq North America, Llc Method and system for obtaining traffic sign data using navigation systems
US9341485B1 (en) 2003-06-19 2016-05-17 Here Global B.V. Method and apparatus for representing road intersections
US7482916B2 (en) 2004-03-15 2009-01-27 Anita Au Automatic signaling systems for vehicles
JP4291741B2 (en) 2004-06-02 2009-07-08 トヨタ自動車株式会社 Lane departure warning device
JP4742285B2 (en) 2005-09-20 2011-08-10 株式会社ゼンリン Map information creating apparatus and method, and program
JP5075331B2 (en) * 2005-09-30 2012-11-21 アイシン・エィ・ダブリュ株式会社 Map database generating system
US7400236B2 (en) 2005-10-21 2008-07-15 Gm Global Technology Operations, Inc. Vehicular lane monitoring system utilizing front and rear cameras
JP4702149B2 (en) 2006-04-06 2011-06-15 株式会社日立製作所 Vehicle location positioning device
US7477988B2 (en) 2006-05-16 2009-01-13 Navteq North America, Llc Dual road geometry representation for position and curvature-heading
JP4561769B2 (en) 2007-04-27 2010-10-13 アイシン・エィ・ダブリュ株式会社 Route guidance system and route guidance method
JP2009015504A (en) 2007-07-03 2009-01-22 Aisin Aw Co Ltd Traffic restriction position detection device, traffic restriction position detection method and computer program
JP5227065B2 (en) 2008-01-25 2013-07-03 株式会社岩根研究所 Three-dimensional mechanical map, three-dimensional mechanical map generating device, a navigation device and an automatic operation system
JP5359085B2 (en) 2008-03-04 2013-12-04 日産自動車株式会社 Lane keeping assist device and a lane keeping assist method
US8311283B2 (en) 2008-07-06 2012-11-13 Automotive Research&Testing Center Method for detecting lane departure and apparatus thereof
US8099213B2 (en) 2008-07-18 2012-01-17 GM Global Technology Operations LLC Road-edge detection
JP5353097B2 (en) * 2008-07-22 2013-11-27 朝日航洋株式会社 Road network data generation device and intersections in the lane generator, as well as the methods and program
US20100020170A1 (en) 2008-07-24 2010-01-28 Higgins-Luthman Michael J Vehicle Imaging System
US8150620B2 (en) 2009-04-14 2012-04-03 Alpine Electronics, Inc. Route search method and apparatus for navigation system utilizing map data of XML format
JP5135321B2 (en) 2009-11-13 2013-02-06 株式会社日立製作所 Autonomous equipment
DE102010049087A1 (en) 2010-10-21 2012-04-26 Gm Global Technology Operations Llc (N.D.Ges.D. Staates Delaware) The method for judging driver's attention
DE102010049086A1 (en) 2010-10-21 2012-04-26 Gm Global Technology Operations Llc (N.D.Ges.D. Staates Delaware) The method for judging driver's attention
US8700299B2 (en) * 2011-02-24 2014-04-15 Mitsubishi Electric Corporation Navigation device, recommended speed arithmetic device, and recommended speed presentation device
JP5652364B2 (en) 2011-09-24 2015-01-14 株式会社デンソー The vehicle behavior control device
CN103906993A (en) 2011-10-28 2014-07-02 诺基亚公司 Method and apparatus for constructing a road network based on point-of-interest (poi) information
US8761991B1 (en) * 2012-04-09 2014-06-24 Google Inc. Use of uncertainty regarding observations of traffic intersections to modify behavior of a vehicle
JP5505453B2 (en) 2012-04-26 2014-05-28 株式会社デンソー The vehicle behavior control device
US8527199B1 (en) 2012-05-17 2013-09-03 Google Inc. Automatic collection of quality control statistics for maps used in autonomous driving
US8855904B1 (en) * 2012-10-10 2014-10-07 Google Inc. Use of position logs of vehicles to determine presence and behaviors of traffic controls
WO2014064805A1 (en) 2012-10-25 2014-05-01 日産自動車株式会社 Vehicle travel support device
DE102012111740A1 (en) * 2012-12-03 2014-06-05 Continental Teves Ag & Co. Ohg A method for supporting a traffic light detecting Ampelphase wizard of a vehicle
US20140257659A1 (en) 2013-03-11 2014-09-11 Honda Motor Co., Ltd. Real time risk assessments using risk functions
US9881220B2 (en) * 2013-10-25 2018-01-30 Magna Electronics Inc. Vehicle vision system utilizing communication system
DE102014205953A1 (en) * 2014-03-31 2015-10-01 Robert Bosch Gmbh A method for analyzing a traffic situation of a vehicle environment,
CN104036275B (en) 2014-05-22 2017-11-28 东软集团股份有限公司 A vehicle blind spot within the target object detection method and apparatus
US20160318490A1 (en) * 2015-04-28 2016-11-03 Mobileye Vision Technologies Ltd. Systems and methods for causing a vehicle response based on traffic light detection

Patent Citations (48)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3613073A (en) * 1969-05-14 1971-10-12 Eugene Emerson Clift Traffic control system
US4884072A (en) * 1985-09-12 1989-11-28 Heinrich Horsch Device for photographic monitoring of cross-roads
US4704610A (en) * 1985-12-16 1987-11-03 Smith Michel R Emergency vehicle warning and traffic control system
US4775865A (en) * 1985-12-16 1988-10-04 E-Lited Limited, A California Limited Partnership Emergency vehicle warning and traffic control system
US5041828A (en) * 1987-08-19 1991-08-20 Robot Foto Und Electronic Gmbh U. Co. Kg Device for monitoring traffic violating and for recording traffic statistics
US5278554A (en) * 1991-04-05 1994-01-11 Marton Louis L Road traffic control system with alternating nonstop traffic flow
US5798949A (en) * 1995-01-13 1998-08-25 Kaub; Alan Richard Traffic safety prediction model
US5873674A (en) * 1996-12-05 1999-02-23 Hohl; Barney K. Roadway safety warning system and method of making same
US5801646A (en) * 1997-08-22 1998-09-01 Pena; Martin R. Traffic alert system and method for its use
US6418371B1 (en) * 1998-02-27 2002-07-09 Mitsubishi International Gmbh Traffic guidance system
US6232889B1 (en) * 1999-08-05 2001-05-15 Peter Apitz System and method for signal light preemption and vehicle tracking
US6919823B1 (en) * 1999-09-14 2005-07-19 Redflex Traffic Systems Pty Ltd Image recording apparatus and method
US6317058B1 (en) * 1999-09-15 2001-11-13 Jerome H. Lemelson Intelligent traffic control and warning system and method
US6338021B1 (en) * 1999-09-29 2002-01-08 Matsushita Electric Industrial Co., Ltd. Route selection method and system
US20030016143A1 (en) * 2001-07-23 2003-01-23 Ohanes Ghazarian Intersection vehicle collision avoidance system
US20100073194A1 (en) * 2002-07-22 2010-03-25 Ohanes Ghazarian Intersection vehicle collision avoidance system
US20080012726A1 (en) * 2003-12-24 2008-01-17 Publicover Mark W Traffic management device and system
US20080097689A1 (en) * 2004-08-04 2008-04-24 Speedalert Pty Ltd An information apparatus for an operator of a land or water based motor driven conveyance
US20070021912A1 (en) * 2005-01-06 2007-01-25 Aisin Aw Co., Ltd. Current position information management systems, methods, and programs
US20060184321A1 (en) * 2005-02-17 2006-08-17 Denso Corporation Navigation system, program thereof and map data thereof
US20060224303A1 (en) * 2005-03-30 2006-10-05 Denso Corporation Navigation system and program for the same
US20080284616A1 (en) * 2005-10-26 2008-11-20 Azael Flores Rendon Quick return
US20070200730A1 (en) * 2006-02-27 2007-08-30 Woo Jeon Green Co., Ltd. Integrated traffic signal, sign and information display device
US20090135024A1 (en) * 2006-03-17 2009-05-28 Park Jin-Gu Display control system of traffic light and display method
US20070296610A1 (en) * 2006-06-24 2007-12-27 Machinery Verification & Documentation Service, Inc. Traffic light safety zone
US20080162027A1 (en) * 2006-12-29 2008-07-03 Robotic Research, Llc Robotic driving system
US20080172171A1 (en) * 2007-01-17 2008-07-17 Gregory Mikituk Kowalski Methods and systems for controlling traffic flow
US20080238720A1 (en) * 2007-03-30 2008-10-02 Jin-Shyan Lee System And Method For Intelligent Traffic Control Using Wireless Sensor And Actuator Networks
US20080291052A1 (en) * 2007-05-25 2008-11-27 Spot Devices, Inc. Alert and warning system and method
US20090312888A1 (en) * 2008-02-25 2009-12-17 Stefan Sickert Display of a relevant traffic sign or a relevant traffic installation
US20090326751A1 (en) * 2008-06-16 2009-12-31 Toyota Jidosha Kabushiki Kaisha Driving assist apparatus
US8121749B1 (en) * 2008-09-25 2012-02-21 Honeywell International Inc. System for integrating dynamically observed and static information for route planning in a graph based planner
EP2466566A1 (en) * 2009-01-23 2012-06-20 Hella KGaA Hueck & Co. Method and device for controlling at least one traffic light assembly of a pedestrian crossing
US20110006915A1 (en) * 2009-07-13 2011-01-13 Sower Charles D Turn/no turn on red traffic light signal
US20110080303A1 (en) * 2009-09-01 2011-04-07 Goldberg Allen Computerized traffic signal system
US20120095646A1 (en) * 2009-09-15 2012-04-19 Ghazarian Ohanes D Intersection vehicle collision avoidance system
US20110182473A1 (en) * 2010-01-28 2011-07-28 American Traffic Solutions, Inc. of Kansas System and method for video signal sensing using traffic enforcement cameras
US20110187559A1 (en) * 2010-02-02 2011-08-04 Craig David Applebaum Emergency Vehicle Warning Device and System
US20110025528A1 (en) * 2010-03-02 2011-02-03 Mohammadreza Rejali Control system and a method for information display systems for vehicles on cross roads
US20120123640A1 (en) * 2010-04-19 2012-05-17 Toyota Jidosha Kabushiki Kaisha Vehicular control apparatus
US20120112927A1 (en) * 2010-11-05 2012-05-10 International Business Machines Corporation Traffic light preemption management system
US20130038433A1 (en) * 2011-02-10 2013-02-14 Audi Ag Method and system for line-of-sight-independent data transmission
US20130335238A1 (en) * 2011-03-03 2013-12-19 Parallels IP Holdings GmbH Method and device for traffic control
WO2012163573A1 (en) * 2011-05-31 2012-12-06 Robert Bosch Gmbh Driver assistance system and method for operating a driver assistance system
US20140200798A1 (en) * 2011-05-31 2014-07-17 Michael Huelsen Driver assistance system and method for operating a driver assistance system
US20130018572A1 (en) * 2011-07-11 2013-01-17 Electronics And Telecommunications Research Institute Apparatus and method for controlling vehicle at autonomous intersection
US8712624B1 (en) * 2012-04-06 2014-04-29 Google Inc. Positioning vehicles to improve quality of observations at intersections
US8917190B1 (en) * 2013-01-23 2014-12-23 Stephen Waller Melvin Method of restricting turns at vehicle intersections

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10082789B1 (en) 2010-04-28 2018-09-25 Waymo Llc User interface for displaying internal state of autonomous driving system
US10093324B1 (en) * 2010-04-28 2018-10-09 Waymo Llc User interface for displaying internal state of autonomous driving system
US10000216B2 (en) 2012-11-30 2018-06-19 Waymo Llc Engaging and disengaging for autonomous driving
US20150363652A1 (en) * 2014-06-16 2015-12-17 Thinkware Corporation Electronic apparatus, control method of electronic apparatus and computer readable recording medium
US20170256064A1 (en) * 2014-06-16 2017-09-07 Thinkware Corporation Automatic detection and analysis of traffic signal type information using image data captured on a vehicle
WO2018044340A1 (en) * 2016-08-29 2018-03-08 Baidu Usa Llc Method and system to construct surrounding environment for autonomous vehicles to make driving decisions

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