Source: https://patents.google.com/patent/KR20190003800A/en
Timestamp: 2019-12-09 04:48:06
Document Index: 731639521

Matched Legal Cases: ['art 501', 'art 501', 'art 501', 'art 501', 'art 501', 'art 501', 'art 501', 'art 501', 'art 900', 'art 1000', 'art 1100']

KR20190003800A - Sensor field selection - Google Patents
Sensor field selection Download PDF
KR20190003800A
KR20190003800A KR1020187037284A KR20187037284A KR20190003800A KR 20190003800 A KR20190003800 A KR 20190003800A KR 1020187037284 A KR1020187037284 A KR 1020187037284A KR 20187037284 A KR20187037284 A KR 20187037284A KR 20190003800 A KR20190003800 A KR 20190003800A
KR1020187037284A
나다니엘 페어필드
지아준 즈후
드미트리 에이. 돌고브
2011-06-01 Priority to US13/150,385 priority Critical patent/US8589014B2/en
2011-06-01 Priority to US13/150,385 priority
2011-09-30 Application filed by 웨이모 엘엘씨 filed Critical 웨이모 엘엘씨
2011-09-30 Priority to PCT/US2011/054157 priority patent/WO2012166170A1/en
2019-01-09 Publication of KR20190003800A publication Critical patent/KR20190003800A/en
Embodiments of the present disclosure generally relate to safe and effective use of autonomous vehicles. More specifically, the autonomous vehicle 301, 501 can detect objects in the sensor fields 410, 411, 430, 431, 420A-423A, 420B-423B, 570-75, have. In response to the detection of an object, the computer 110 may adjust the speed of the autonomous vehicle or change its direction. However, in some instances, the sensor fields may be altered or less reliable depending on objects or other features in the environment of the vehicle. As a result, the vehicle's computer 110 can calculate the size and shape reduction (620, 720) of the area of the sensor and calculate a new sensor field based on this area reduction. In response to reducing the area of the sensor or identifying a new sensor field, the vehicle's computer may change the control strategy of the vehicle.
Sensor field selection {SENSOR FIELD SELECTION}
This application claims the benefit of US patent application Ser. No. 13 / 150,385 entitled "Sensor Field Selection" filed on June 1, 2011, the entire disclosure of which is incorporated herein by reference in its entirety, Lt; / RTI &gt;
Autonomous vehicles use a variety of computing systems to help transport passengers from one location to another. Some autonomous vehicles may require any initial or continuous input from an operator, such as a pilot, driver, or passenger. Other systems, for example, autopilot systems, can be used only when the system is involved, which allows the operator to set the operator in the manual mode To perform the switching from the self-running mode (in which the vehicle is essentially self-driven in this self-running mode) to any mode between them.
Such vehicles are equipped with various types of sensors for detecting objects in the surrounding environment. For example, an autonomous vehicle may include a laser, a sonar, a radar, a camera, and other devices that scan and record data from the surrounding environment of the vehicle. These devices can be combined (and in some cases alone) used to construct a 3D model of the object detected in the environment of the vehicle.
One embodiment of the present disclosure provides a method for controlling a vehicle having an autonomous operation mode. The method includes the steps of controlling the operation of the vehicle by a processor based on a first control strategy; Identifying a sensor field based on a field of view of one or more sensors of the vehicle; Receiving sensor data from selected ones of the one or more sensors; Identifying a change in the sensor sense force of the one or more sensors based on the sensor data, wherein the change in the sensor sense force includes a reduction in the ability to sense objects within the sensor field; Determining a second control strategy based on the change; And controlling the operation of the vehicle by the processor based on the second control strategy.
In one example, the method also includes detecting a position of an object and an object within the sensor field based on the received sensor data, wherein the step of identifying the change is based on the position of the object. In another example, identifying the change in the sensor sensed force includes calculating an area where the sensor sense power is reduced based on the position of the object in the sensor field, and determining the second control strategy includes sensing the sensor Based on the reduced area. In another example, identifying the change in sensor perception includes calculating a current sensor field based on the position of the object in the sensor field, and determining the second control strategy is based on the current sensor field . In another example, the object is another vehicle. In yet another example, the method also includes determining if the sensor data indicates that a given one of the one or more sensors is providing unreliable information, The step is based on said indication being determined. In another example, the untrusted information comprises one or more camera images. In another example, the method also includes determining whether there is no longer a change in the sensor sensed power of the one or more sensors based on the sensor data, and the step of controlling the operation of the vehicle further comprises: Is performed according to the first control strategy based on the determination that there is no more. In another example, the step of controlling the operation of the vehicle based on the first control strategy includes the step of controlling the vehicle in the first direction, and the step of controlling the operation of the vehicle based on the second control strategy includes And adjusting the vehicle in a second direction different from the first direction. In another example, the step of controlling the operation of the vehicle based on the first control strategy includes the step of controlling the vehicle at the first speed, and the step of controlling the operation of the vehicle based on the second control strategy includes And adjusting the vehicle at a second speed different from the speed.
Another embodiment of the present disclosure provides a device for controlling a vehicle having an autonomous mode of operation. The device includes one or more sensors for detecting objects in the environment of the vehicle and a processor coupled to the one or more sensors. The processor controls the operation of the vehicle based on the first control strategy; Identify a sensor field based on a viewing angle of one or more sensors of the vehicle; Receiving sensor data from selected ones of the one or more sensors; Identify a change in sensor perception of one or more sensors based on the sensor data, wherein the change in sensor perception includes a reduction in the ability to detect objects within the sensor field; Determine a second control strategy based on the change; And is operable to control the operation of the vehicle based on the second control strategy.
In one example, the processor is also operable to detect the location of objects and objects within the sensor field based on the received sensor data, and the processor identifies the changes based on the location of the object. In yet another example, the processor is operable to identify a change in sensor perceived force by calculating an area in which the sensor perceived force is reduced based on the location of the object in the sensor field, and determining the second control strategy includes: Based. In yet another example, the processor is operable to identify a change in sensor perception by calculating a current sensor field based on the position of an object in the sensor field, and determining a second control strategy is based on the current sensor field. In yet another example, the processor is also operable to identify the object as another different vehicle. In another example, the processor is further operable to determine whether the sensor data indicates that a given one of the one or more sensors is providing untrusted information, and the processor is further configured to determine, based on the determined indication, Lt; / RTI &gt; In another example, the untrusted information comprises one or more camera images. In yet another example, the processor is further configured to determine, based on the sensor data, whether there is no longer a change in the sensor perception of the one or more sensors, and to determine, based on the determination that the change is no longer present, Lt; / RTI &gt; In another example, the processor may also be configured to control the operation of the vehicle based on the first control strategy by steering the vehicle in a first direction and adjusting the vehicle in a second direction different from the first direction, And is operable to control the operation of the vehicle on the basis of the vehicle speed. In another example, the processor may also be configured to control the operation of the vehicle based on a first control strategy by steering the vehicle at a first speed, and adjusting the vehicle at a second speed different from the first speed, And is operable to control the operation of the vehicle on the basis of the vehicle speed.
Another embodiment of the present disclosure provides a computer readable storage medium in which computer readable instructions of a program are stored, wherein instructions, when executed by a processor, cause the processor to control a vehicle having an autonomous mode of operation . The method comprises: controlling the operation of the vehicle by a processor based on a first control strategy; Identifying a sensor field based on a viewing angle of one or more sensors of the vehicle; Receiving sensor data from selected ones of the one or more sensors; Identifying a change in the sensor perceived force of the one or more sensors based on the sensor data, wherein the change in the sensor perceived force includes a decrease in the ability to detect objects within the sensor field; Determining a second control strategy based on the change; And controlling the operation of the vehicle by the processor based on the second control strategy.
In one example, the method also includes detecting a position of an object and an object within the sensor field based on the received sensor data, wherein identifying the change is based on the position of the object. In another example, identifying a change in the sensor sensing force includes calculating an area in which the sensor sensing force is reduced based on the position of the object in the sensor field, and determining the second control strategy comprises: Based. In another example, identifying a change in sensor perception includes calculating a current sensor field based on the position of an object in the sensor field, and determining a second control strategy based on the current sensor field. In another example, the object is another vehicle. In yet another example, the method also includes determining if the sensor data indicates that a given one of the one or more sensors is providing untrusted information, . In another example, the untrusted information comprises one or more camera images. In another example, the method also includes determining whether there is no longer a change in the sensor perception of one or more of the sensors based on the sensor data, and controlling the operation of the vehicle further comprises: Based on the first control strategy. In another example, controlling the operation of the vehicle based on the first control strategy includes steering the vehicle in a first direction, and controlling the operation of the vehicle based on the second control strategy comprises: Includes adjusting the vehicle in another second direction. In another example, controlling the operation of the vehicle based on the first control strategy includes steering the vehicle at a first speed, and controlling operation of the vehicle based on the second control strategy comprises: Includes adjusting the vehicle at a different second speed.
1 is a functional block diagram of a system according to an exemplary embodiment.
2 is an interior view of an autonomous vehicle according to an exemplary embodiment.
3 is an exterior view of an autonomous vehicle according to an exemplary embodiment.
4A to 4D are views of an autonomous vehicle according to an exemplary embodiment.
5A and 5B are views of an autonomous vehicle according to an exemplary embodiment.
6A and 6B are views of an autonomous vehicle according to an exemplary embodiment.
7A and 7B are views of an autonomous vehicle according to an exemplary embodiment.
8A and 8B are views of an autonomous vehicle according to an exemplary embodiment.
9 is a flow chart according to an exemplary embodiment.
10 is a flow diagram according to an exemplary embodiment.
11 is a flow diagram according to an exemplary embodiment.
As shown in FIG. 1, an autonomous navigation system 100 in accordance with an embodiment of the present disclosure includes a vehicle 101 having various components. While particular embodiments of the present disclosure may be particularly useful in connection with certain types of vehicles, the vehicle may be any type of vehicle, including, but not limited to, a car, truck, motorcycle, bus, boat, airplane, helicopter, lawnmower Recreational vehicles, amusement park vehicles, farm equipment, construction equipment, trams, golf carts, trains, and trolleys, But are not limited to these. A vehicle may include one or more computers (e.g., processor 120, memory 130, and computer 110 including other components typically present in a general purpose computer).
Memory 130 stores information accessible by processor 120 (including instructions 132 and data 134 that may or may not be executed by processor 120). The memory 130 may be any type capable of storing information accessible by a processor, including but not limited to a hard-drive, a memory card, a ROM, a RAM, a DVD or other optical disk, as well as other write- Readable media) that stores data that can be read via an electronic device such as a read-only memory, or other media. The systems and methods may include different combinations of the foregoing, so that different portions of the instructions and data are stored in different types of media.
The instructions 132 may be any set of instructions to be executed directly by a processor (such as machine code) or indirectly (such as with scripts). For example, the instructions may be stored as computer code in a computer-readable medium. In this regard, the terms " command " and " program " may be used interchangeably herein. The instructions may be stored in an object code format for direct processing by the processor or may be stored in a script of independent source code modules (either precompiled or interpreted on demand) (E.g., including collections of collections or collections). The functions, methods and routines of the instructions are described in more detail below.
The data 134 may be retrieved, stored, or modified by the processor 120 in accordance with the instructions 132. For example, although the main content disclosed herein is not limited to any particular data structure, the data may be stored in computer registers or may be stored in a plurality of different fields and records, Or flat files, and stored in a relational database. The data may also be formatted in any computer-readable format. In merely illustrative further example, the image data may be stored in a computer-readable storage medium such as a computer-readable medium, such as a computer-readable medium for lossless (e.g., BMP) or lossy (e.g., JPEG) and bitmap or vector based (e.g., SVG) Or as bitmaps consisting of grids of pixels that are stored according to formats that are compressed or decompressed. The data may be stored in memory, such as by numbers, descriptive text, proprietary codes, references to data stored in the same memory or other areas of different memories (including other network locations) And may include any information sufficient to identify such related information, such as the information used.
The processor 120 may be any conventional processor, such as commercially available CPUs. Alternatively, the processor may be a dedicated device such as an ASIC or other hardware-based processor. Although FIG. 1 illustrates functionally the processor, memory, and other elements of computer 110 as being within the same block, those of ordinary skill in the art will appreciate that such processor, computer, computers, or memories that may or may not be stored in a housing of a computer system. For example, the memory may be a hard drive or other storage medium located in a different housing than the computer 110. Thus, it should be understood that reference to a processor or computer includes references to processors or computers or a collection of memories that may or may not operate in parallel. Rather than using a single processor to perform the steps described herein, some of the components, such as the steering component and the deceleration component, may each have their own processor performing only the calculations associated with the component-specific functions.
In various embodiments described herein, the processor may be remote from the vehicle and communicate wirelessly with the vehicle. In other embodiments, some of the processes described herein are executed on a processor located within a vehicle, and others are executed by a remote processor, including taking the necessary actions to perform a single pilot.
The computer 110 may include all of the components typically used in connection with a computer, and may include a memory, such as, for example, a central processing unit (CPU), a web browser, , A RAM and an internal hard drive), an electronic display 142 (e.g., a screen, a small LCD touch-screen or any other electrical device operable to display information), a user input 140 (E.g., keyboard, touchscreen and / or microphone), as well as explicit information (e.g., gestures) or tacit information (e.g., "sleeping" (E. G., A video camera) for &lt; / RTI &gt;
In one example, the computer 110 may be an autonomous mobile computing system integrated into the vehicle 101. Figure 2 shows an exemplary design of the interior of an autonomous vehicle. An autonomous vehicle may include all of the characteristics of a non-autonomous vehicle, for example, a steering device such as a steering wheel 210, a running display device such as a navigation display 215, And a gear selector device such as a gear shifter 220. The vehicle may also be provided with various user input devices (not shown) to enable or disable one or more autonomous modes of operation and to enable the driver or passenger 290 to provide information, such as a navigation destination, For example, a gear transmission 220, a touch screen 217, or button inputs 219.
Vehicle 101 may include one or more additional displays. For example, the vehicle may include a display 225 for displaying information regarding the state of computing of the autonomous drive vehicle or the autonomous drive vehicle. In another example, the vehicle may also include a status indicator 138 (see FIG. 1), such as status bar 230, to indicate the current status of the vehicle 101. In the example of FIG. 2, the status bar 230 indicates that the vehicle is in the current drive mode and has " D " and " 2 mph " indicating that the vehicle is moving at a speed of 2 miles per hour Display. In this regard, the vehicle may display text on the electronic display, illuminate a portion of the vehicle 101 (e.g., the handle 210), or may provide various other types of indicia .
An autonomous computing system can communicate with various components of a vehicle. 1, the computer 110 may be in communication with the central processor 160 of the vehicle and may be coupled to a variety of vehicles 101 to control the movement, speed, etc. of the vehicle 101. For example, May transmit and receive information from the systems (e.g., braking system 180, acceleration system 182, signaling system 184, and travel system 186). Additionally, when the computer 100 is involved, the computer 110 may control some or all of these functions of the vehicle 101 and may thus be completely autonomous or only partially autonomous. It should be understood that although various systems and computers 110 are shown in the vehicle 101, these elements may be external to the vehicle 101 or physically separated.
The vehicle may also include a geolocation component 144 that communicates with the computer 110 to determine the geographic location of the device. For example, the positioning component may include a GPS receiver for determining the latitude, longitude, and / or altitude position of the device. Other position determination systems, such as laser-based localization systems, inertial-assisted GPS (GPS), or camera-based localization, Lt; / RTI &gt; The location of the vehicle can include absolute geographic locations (e.g., latitude, longitude, and altitude), as well as relative location information (e.g., relative location to other cars in the immediate vicinity, Position may be determined to be less than the noise).
The vehicle may also include other devices in communication with the computer 110, such as, for example, an accelerometer, gyroscope or other direction / velocity detection device (146). By way of example only, the acceleration device 146 may determine the pitch, yaw or roll (or change) for the gravity direction or plane perpendicular thereto. Devices can also track increases or decreases in speed and the direction of these changes. Device provision of location and orientation data as described herein may be provided automatically to the user, computer 110, other computers, and combinations thereof.
The computer 110 may control the direction and speed of the vehicle by controlling various components. For example, if the vehicle is operating in fully autonomous mode, the computer 110 may cause the vehicle to accelerate (e.g., by increasing the fuel or other energy provided to the engine) (For example, by reducing the fuel supplied to the engine or by applying a brake), and to change the direction (for example, by turning the two front wheels to the left and right).
The vehicle may include components for detecting objects on the exterior of the vehicle, such as other vehicles, obstacles on the road, traffic signals, signs, trees, and the like. The detection system may comprise a laser, sonar, radar, camera, or any other detection device (which records data that can be processed by the computer 110). For example, if the vehicle is a small passenger vehicle, the vehicle may include a laser mounted on a roof or other convenient location.
As shown in Fig. 3, the small passenger car 301 may include a laser 310 and a laser 311 mounted on the front and top of the vehicle, respectively. The laser 310 may have a reach distance of approximately 150 meters, a vertical viewing angle of 30 degrees, and a horizontal viewing angle of approximately 30 degrees. The laser 311 may have a reach distance of approximately 50-80 meters, a vertical viewing angle of 30 degrees, and a horizontal viewing angle of 360 degrees. Lasers can provide the vehicle with reach and strength information that the computer can use to identify the location and distance of various objects. In one embodiment, the laser is capable of measuring the distance between the vehicle and the surface of the object (facing such a vehicle) by rotating the axis and changing the pitch.
The vehicle may also include various radar detection units such as those used for adaptive cruise control systems. The radar detection units may be located on both sides of the front bumper as well as the front and rear of the vehicle. As shown in the example of Fig. 3, the vehicle 301 includes radar detection units 320-323 located on the side of the vehicle (only one side is shown in the figure), front and rear. Each of these radar detection units can have a reach distance of about 200 meters for a viewing angle of about 18 degrees, as well as a reach distance of about 60 meters for a viewing angle of about 56 degrees.
In another example, various cameras may be mounted in the vehicle. The cameras can be mounted at a predetermined distance so that a parallax from the images of two or more cameras can be used to compute distances to various objects. As shown in FIG. 3, the vehicle 301 may include two cameras 330-331 mounted below the windshield 340 near a rear view mirror (not shown). The camera 330 may include a reach distance of approximately 200 meters and a horizontal viewing angle of approximately 30 degrees while the camera 331 may include a reach distance of approximately 100 meters and a horizontal viewing angle of approximately 60 degrees.
Each sensor may be associated with a particular sensor field in which the sensor can be used to detect an object. 4A is a top view of an approximate sensor field of various sensors. Figure 4B shows approximate sensor fields 410 and 411 for lasers 310 and 311, respectively, based on the viewing angle for these sensors. For example, the sensor field 410 includes a horizontal viewing angle of approximately 30 degrees for approximately 150 meters, and the sensor field 411 includes a 360 degree horizontal viewing angle for approximately 80 meters.
Fig. 4C shows approximate sensor fields 420A-423B for radar detection units 320-323, respectively, based on the viewing angles for these sensors. For example, the radar detection unit 320 includes sensor fields 420A and 420B. The sensor field 420A includes a horizontal viewing angle of approximately 18 degrees for approximately 200 meters and the sensor field 420B comprises a horizontal viewing angle of approximately 56 degrees for approximately 80 meters. Similarly, radar detection units 321-323 include sensor fields 421A-423A and 421B-423B. The sensor fields 421A-423A include a horizontal viewing angle of approximately 18 degrees for approximately 200 meters and the sensor fields 421B-423B comprise a horizontal viewing angle of approximately 56 degrees for approximately 80 meters. The sensor fields 421A and 422A extend beyond the edges of Figs. 4A and 4C.
FIG. 4D shows an approximate sensor field 430-431 for cameras 330-331, respectively, based on the viewing angle for these sensors. For example, the sensor field 430 of the camera 330 includes a viewing angle of approximately 30 degrees for approximately 200 meters, and the sensor field 431 of the camera 430 includes a viewing angle of approximately 60 degrees for approximately 100 meters do.
In yet another example, the autonomous vehicle may include a sonar device, stereo cameras, a localization camera, a laser and a radar detection unit, each having a different viewing angle. The sonar can have a horizontal viewing angle of approximately 60 degrees for a maximum distance of approximately 6 meters. The stereo camera may have an overlap area with a horizontal viewing angle of approximately 50 degrees, a vertical viewing angle of approximately 10 degrees and a maximum distance of approximately 30 meters. The local camera may have a horizontal viewing angle of about 75 degrees, a vertical viewing angle of about 90 degrees, and a maximum distance of about 10 meters. The laser may have a horizontal viewing angle of approximately 360 degrees, a vertical viewing angle of approximately 30 degrees, and a maximum distance of 100 meters. The radar may have a horizontal viewing angle of 60 degrees with respect to the near beam, a horizontal viewing angle of 30 degrees with respect to the far beam, and a maximum distance of 200 meters.
In one example, the vehicle 101 may be a small cart, such as a golf cart. Figures 5A and 5B are side views of the small cart 501 and a set of exemplary viewing angles that form the sensor field of the cart, as viewed from top to bottom. The small cart 501 may include sensors such as sonar, stereo cameras, and a local camera. The local camera can be used to localize the vehicle with centimeter accuracy on a map with map information. Each of these sensors can result in a viewing angle expansion at some horizontal and vertical angles over a distance. For example, the cart 501 may include a local camera mounted in front of the cart, resulting in a sensor field 560 with a horizontal viewing angle of about 35 degrees, which ranges from about 1.5 to 7 meters in front of the cart Respectively. The cart may also include a plurality of sonar detecting devices mounted across the front of the cart. These sonar devices can be used to detect objects by transmitting and receiving sound waves at specific frequencies of a range of frequencies. Each of these devices may cause sensor fields such as sensor fields 570-575 to be generated, each of which has a horizontal viewing angle of about 35 degrees to about 3 meters forward of the cart. The cart 501 may also include a stereo camera mounted in front of the vehicle. Stereo cameras may cause a sensor field 580 to be generated with a horizontal viewing angle of about 30 degrees from about the front of the cart to about 10 meters.
In another example where the cart includes a local camera, a stereo camera, and one or more sonar detecting devices, the cart's local camera may include a horizontal viewing angle of approximately 75 degrees, a vertical viewing angle of approximately 90 degrees, and a reach distance of approximately 10 meters . The overlapping area of stereo cameras may include a horizontal viewing angle of about 50 degrees, a vertical viewing angle of about 10 degrees, and a reach distance of about 30 meters. Each sonar detection device may have a horizontal viewing angle of approximately 60 degrees and a reach distance of approximately 6 meters.
The aforementioned sensors can enable the vehicle to evaluate and possibly respond to such environments, in order to maximize safety to passengers, as well as objects or people in the environment. It should be understood that the vehicle type, the number and type of sensors, the sensor position, the sensor viewing angle, and the sensor field of the sensor are exemplary only. Various other configurations may also be used.
In addition to the sensors described above, the computer can also use inputs from sensors of typical non-autonomous vehicles. For example, these sensors can be used to detect air temperature, humidity, or dust in the air, such as a tire air pressure sensor, an engine temperature sensor, a brake heat sensor, a brake pad status sensor, a tire tread sensor, a fuel sensor, An air quality sensor (e.g., for detection), and the like.
Many of these sensors provide data that is processed by the computer in real time, that is, sensors continuously update their output to reflect the sensing environment over a range of times or over a period of time And can provide the updated output to the computer continuously or on demand so that the computer can determine whether the vehicle's past-current direction or speed should be modified in response to the sensed environment.
In addition to processing the data provided by the various sensors, the computer may rely on environmental data obtained at a previous point in time and expected to survive regardless of whether vehicles are present in the environment. For example, referring to FIG. 1, data 134 includes detailed map information 136, such as the shape and elevation of a road, lane lines, intersections, pedestrian crossings, speed limits, traffic signals, Real-time traffic information, or other highly detailed maps that identify such objects and information. For example, the map information may include explicit speed limit information associated with various road sections. The speed limit data may be entered manually or scanned from previously captured images of the speed limit sign, for example, using optical-character recognition. The map information may include a three dimensional topographic map comprising one or more objects listed above. For example, the vehicle can be based on real-time data (e.g., using its sensors to determine the current GPS position of another vehicle) and based on other data (e.g., (E.g., by comparing the GPS location with the previously stored lane selection map data to determine if another vehicle is redirected).
Again, although map information is presented herein as a video-based map, the map information need not be entirely image-based (e.g., raster). For example, the map information may include one or more roadgraphs, or it may include graph networks of information such as roads, lanes, intersections, and links between these features. Each feature can be stored as graph data and can be associated with information such as geographic location, whether it is linked to other relevant features or not, for example, Can be linked. In some instances, the associated data may include grid-based indexes of the load graph to enable efficient reference of specific load graph features.
In addition to the operations illustrated in the drawings and described above, various operations are now considered. It should be understood that the following operations need not be performed correctly in the order described below. Rather, the various steps may be handled in different orders or simultaneously, and these steps may also be added or omitted.
An autonomous vehicle may carry itself, passengers, and / or cargo between two locations by following the path. For example, the driver can enter the destination and activate the autonomous mode of the vehicle. In response, the vehicle's computer can calculate the path based on the load graph, current location and destination. Based on this path (or as part of the path generation), the vehicle can determine a control strategy for controlling the vehicle along the path to the destination. For example, a control strategy may include where to turn, at what speed, where to find a traffic signal, where to stop for an intersection or stop sign, and so on.
As described above, while following the path, the vehicle can detect objects that are in the sensor fields in its surroundings. In response to detecting the object, the computer can adjust the speed of the autonomous vehicle or change its direction. However, in some instances, the sensor fields may be altered or less reliable depending on objects or other features in the environment of the vehicle.
Any object may occupy a portion of the vehicle &apos; s sensor field. For example, as shown in FIG. 6A, the vehicle 301 may be traveling along the side of another large moving object, such as a tractor-trailer 610. As shown in FIG. 6B, the tractor-trailer 610 occupies an area 620 in the sensor field of the vehicle 301. In another example, shown in FIG. 7A, the vehicle 301 is running alongside the buildings 710 and is approaching a bend in the road. These buildings occupy area 720 in the sensor field of vehicle 301.
If any object occupies a portion of the vehicle &apos; s sensor field, the presence of such an object may change the perception of the one or more sensors (e.g., by limiting the perception of other objects). The vehicle's computer can no longer accurately detect other objects that are not within the detection line of the sensor or in the area behind the object. In some instances, if the object is translucent or transparent (e.g., glass), the sensors may detect some objects "though", but may have a much lower accuracy . Thus, the size and accuracy of the sensor field of the vehicle can be reduced. As a result, the vehicle's computer can calculate the size and shape reduction of the area of the sensor, and calculate new or current sensor fields based on such area reduction.
6B, the computer may identify the tractor-trailer 610 as an object and may determine the size and shape of the area 620, the size and shape of the sensor field within the area 620, and / The size and shape of the sensor field that is not in the area 620 can be calculated. Any objects within the area 620 are no longer detectable by the sensors of the vehicle 301. This region represents an approximate area of " blindness ". The computer can estimate the location of objects based on information from the load graph or, for example, if the computer is tracking objects that have recently entered the area. Because the tractor-trailer is opaque, sonar, laser or camera sensors, for example, can not collect data in area 620. This opacity prevents sensors from "passing through" trucks to collect information.
Referring to the example of FIG. 7, the sensor field of the vehicle is affected by the building. The computer of the vehicle may identify the building as an object and may determine the size and shape of the area 720 and the size and shape of the sensor field in the area 720 and / Can be calculated. In this example, the vehicle can not detect the truck 730 in the area 720 that is parked next to the building 710. For example, the location of the vehicle 730 is also shown as being within the area 720 in Figure 7B.
In addition to objects, other situations can affect the sensor field of an autonomous vehicle by reducing or hindering the perceived power of one or more sensors relative to the vehicle's environment. For example, in bright sunlight, camera images can be saturated and can be unreliable. Therefore, the sensor field of the cart 501 is reduced because this bright light affects the effective operation of the camera. Accordingly, as shown in Fig. 8A, at the viewing angles 560 and 580 of the camera sensors, the object can no longer be detected due to the bright light 880. Fig. Accordingly, such a viewing angle is shown by a dotted line. In the example of the object described above, the size and accuracy of the sensor field of the cart can be reduced. The cart's computer can then simply calculate a new sensor field based solely on the sonar detection device 570-576, or simply determine that the cart can depend solely on the sensor field from the sonar device's viewing angle .
In another example, the sound 890 of FIG. 8B has a certain range of frequencies used by the sonar device of the cart, or has a frequency at or near the frequency used by the sonar device. For example, the sound may be generated by other sonar devices, or may be caused by noise near the vehicle. This sound can result in feedback that reduces the reliability of sonar data in the detection of objects. Thus, at the viewing angles 570-575 of the sonar detecting device no more objects can be detected. Accordingly, such a viewing angle is shown by a dotted line. The cart's computer can simply calculate a new sensor field based only on, for example, camera devices 560 and 580, or simply determine that the cart can depend solely on the sensor field from the viewing angle of the camera.
In response to identifying a change in the sensor field, the vehicle's computer may change the control strategy of the vehicle. For example, rather than maintaining the speed and direction of the vehicle, the vehicle's computer can reduce the speed of the vehicle, keep the lane, or increase the speed (in view of the speed limit) Response operation can be performed. In another example, instead of reducing the speed to a first speed to take the required action on the path, such as redirecting, the computer can reduce the speed of the vehicle to a much lower second speed. In some instances, the vehicle may not need to take any particular action and may maintain its current speed and direction.
In one example, referring to FIG. 6A, the vehicle 301 may be following a path that needs to move from one or more lanes (toward the tractor-trailer 610) to the left. Normally, if the vehicle is traveling faster than the tractor-trailer, the vehicle 301 may wait until it passes the tractor-trailer 610 to change lanes. However, after the computer of the vehicle has determined that there is a reduction in the area of the sensor or a reduction in the current sensor field, the computer can lower the speed of the vehicle so that the vehicle sags behind the tractor-trailer to increase the sensor field The vehicle 301 becomes able to detect the object on the left side of the tractor-trailer 610. [
Referring to FIG. 7A, in another example, the vehicle 301 may follow a path that needs to travel along the road around the building. Normally, if the vehicle is traveling along a curved road, the computer can maintain the speed of the vehicle. However, after the computer of the vehicle has determined that there is a decrease in the area of the sensor or a decrease in the current sensor field, the computer can drastically lower the speed of the vehicle so that the sensor field of the vehicle detects The vehicle can have enough time to take any necessary action to avoid the object.
Similarly, in the example of FIG. 8A, if the cart can no longer rely on the camera sensor, the cart can drastically lower the speed, and thus if the object is detected within a range of about 3 meters of sonar or angle of view, (501) has a sufficient time to move or stop by bypassing the object. As shown in FIG. 8B, if the sensor field of the cart is confined to the camera viewing angle, the cart 501 may sharply lower the speed again before rotating the direction. In this example, if the cart maintains its own speed before the validity of the sonar detecting device is reduced, the cart 501 may continue to maintain its speed or may be able to slightly reduce the speed or even increase its speed .
The computer of the autonomous vehicle may continually or periodically determine whether the sensor field has changed, whether the action should be taken, or whether the control strategy should be changed accordingly. For example, referring to FIG. 7A, if a vehicle 301 is traveling along a curved path on a road and is passing by a truck 710, the vehicle's computer will return to its original control strategy, Can be increased again. Similarly, if the cart 501 (of FIG. 8A) moves to a shaded area (where the bright light 880 no longer has the same effect on the camera sensor anymore) And can revert to the original control strategy and increase their speed. This cycle of changing the steering and control strategy and the control strategy can continue to occur based on the reduction of the area of the vehicle's sensor field or the current sensor field.
The flowchart 900 of FIG. 9 is another example of controlling the autonomous vehicle described above. At block 910, the computer of the vehicle controls the autonomous vehicle based on the control strategy. At block 920, the sensor field is identified based on the field of view or field of view of one or more of the sensors of the autonomous vehicle. At block 930, an object or its location in the sensor field is detected. For example, data from one or more sensors may be received and processed by the vehicle's computer to identify the object and its location. At block 940, based on the position of the object with respect to the sensor field, a region in which the sensor sensing force is reduced is calculated. Next, at block 950, a new control strategy is identified based on the current sensor field. Then, at block 960, the computer can control the direction and speed of the vehicle based on the area where the sensor perception is reduced. Next, at block 970, the computer determines whether the sensor field is continuously decreasing. If the sensor field is continuously decreasing, the computer may maintain a new control strategy at block 980, or may adjust the control strategy as needed and then return to block 970. If it is determined that the sensor field of the vehicle is no longer being reduced, the computer again controls the autonomous vehicle based on the original control strategy at block 990 and returns to block 930 if a new object is identified .
The flowchart 1000 of FIG. 10 is another example for controlling the autonomous vehicle described above. At block 1010, the computer of the vehicle controls the autonomous vehicle based on the control strategy. At block 1020, the sensor field is identified based on the field of view or field of view of one or more of the sensors of the autonomous vehicle. At block 1030, an object or its location in the sensor field is detected. For example, data from one or more sensors may be received and processed by the vehicle's computer to identify the object and its location. At block 1040, the current sensor field is calculated based on the position of the object relative to the sensor field. Next, at block 1050, a new control strategy is identified based on the current sensor field. Next, at block 1060, the computer can control the direction and speed of the vehicle based on the new control strategy. Next, at block 1070, the computer determines whether the sensor field is continuously decreasing. If the sensor field is continuously decreasing, the computer may maintain a new control strategy at block 1080, or may adjust the control strategy as needed, and then return to block 1070. If it is determined that the sensor field of the vehicle is no longer being reduced, the computer again controls the autonomous vehicle based on the original control strategy at block 1090 and returns to block 1030 if a new object is identified .
The flowchart 1100 of Fig. 11 is another example of controlling the autonomous vehicle described above. At block 1110, the computer of the vehicle controls the autonomous vehicle based on the control strategy. At block 1120, the sensor field is identified based on the field of view or field of view of one or more of the sensors of the autonomous vehicle. At block 1130, a change in the sensing power of the one or more sensors is identified. For example, data from one or more sensors may be received and processed by the vehicle's computer to determine whether the data from one or more sensors indicates unreliability. Then, at block 1140, a new control strategy is identified based on the change. Then, at block 1150, the computer can control the direction and speed of the vehicle based on the new control strategy. Next, at block 1160, the computer determines whether the change is still valid by, for example, receiving additional data from the sensor. If the change is persistent, the computer may maintain a new control strategy at block 1170, or may adjust the control strategy as needed, and then return to block 1160. If the computer now determines that no more changes are present, the computer again controls the autonomous vehicle based on the original control strategy at block 1180 and returns to block 1130 if a new object is identified.
These modifications and combinations of the features discussed above and other modifications and combinations can be utilized without departing from the subject matter of the present invention as defined by the appended claims. Thus, the foregoing description of the exemplary embodiments But should be considered in an illustrative sense rather than a restrictive sense of the subject matter of the present invention as defined by the appended claims. It is to be understood that the examples set forth herein (as well as the expressions set forth in such phrases as "such as", "including", "including", etc.) are intended to limit the claimed subject matter of this invention only to such specific examples It should also be understood that they should not be construed, but rather that these examples are provided to illustrate only a few of the many possible embodiments.
The present invention is of great commercial potential and includes, but is not limited to, the use of vehicles having an autonomous mode of operation.
KR1020187037284A 2011-06-01 2011-09-30 Sensor field selection KR20190003800A (en)
US13/150,385 US8589014B2 (en) 2011-06-01 2011-06-01 Sensor field selection
US13/150,385 2011-06-01
PCT/US2011/054157 WO2012166170A1 (en) 2011-06-01 2011-09-30 Sensor field selection
KR20190003800A true KR20190003800A (en) 2019-01-09
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KR1020187037284A KR20190003800A (en) 2011-06-01 2011-09-30 Sensor field selection
KR1020137034980A KR20140039243A (en) 2011-06-01 2011-09-30 Sensor field selection
US (1) US8589014B2 (en)
EP (1) EP2771751B1 (en)
JP (1) JP6468839B2 (en)
KR (2) KR20190003800A (en)
CN (1) CN103718124B (en)
WO (1) WO2012166170A1 (en)
AT508634B1 (en) * 2009-08-28 2011-05-15 Riegl Laser Measurement Sys Laser channel for assembling on the roof rack of a vehicle
US9062979B1 (en) 2013-07-08 2015-06-23 Google Inc. Pose estimation using long range features
GB2516629A (en) * 2013-07-26 2015-02-04 Randoll Electrical Ltd Interactive vehicles
EP2921362A1 (en) 2014-03-18 2015-09-23 Volvo Car Corporation Vehicle, vehicle system and method for increasing safety and/or comfort during autonomous driving
JP6260544B2 (en) * 2015-01-19 2018-01-17 トヨタ自動車株式会社 Automatic driving device
JP6222137B2 (en) 2015-03-02 2017-11-01 トヨタ自動車株式会社 Vehicle control device
US9594378B2 (en) 2015-07-31 2017-03-14 Delphi Technologies, Inc. Variable object detection field-of-focus for automated vehicle control
US10267908B2 (en) * 2015-10-21 2019-04-23 Waymo Llc Methods and systems for clearing sensor occlusions
DE102015224553A1 (en) * 2015-12-08 2017-06-08 Robert Bosch Gmbh Method, computer program, storage medium and electronic control unit for operating a vehicle
US9537956B1 (en) 2015-12-11 2017-01-03 Uber Technologies, Inc. System for acquiring time-synchronized sensor data
US9785150B2 (en) * 2015-12-11 2017-10-10 Uber Technologies, Inc. Formatting sensor data for use in autonomous vehicle communications platform
US10137890B2 (en) 2016-06-28 2018-11-27 Toyota Motor Engineering & Manufacturing North America, Inc. Occluded obstacle classification for vehicles
US10078335B2 (en) * 2016-06-28 2018-09-18 Toyota Motor Engineering & Manufacturing North America, Inc. Ray tracing for hidden obstacle detection
US20180059672A1 (en) * 2016-08-29 2018-03-01 Baidu Usa Llc Method and system to construct surrounding environment for autonomous vehicles to make driving decisions
FR3058514B1 (en) * 2016-10-26 2019-08-09 Valeo Schalter Und Sensoren Gmbh Method for evaluating the risk of lurning an image sensor and electronic unit therefor
US20180136652A1 (en) * 2016-11-14 2018-05-17 Baidu Usa Llc Planning feedback based decision improvement system for autonomous driving vehicle
US10377380B2 (en) 2017-01-09 2019-08-13 Denso International America, Inc. Vehicle safety system
US10286906B2 (en) 2017-01-24 2019-05-14 Denso International America, Inc. Vehicle safety system
US10386856B2 (en) * 2017-06-29 2019-08-20 Uber Technologies, Inc. Autonomous vehicle collision mitigation systems and methods
JP2019099035A (en) * 2017-12-06 2019-06-24 ロベルト・ボッシュ・ゲゼルシャフト・ミト・ベシュレンクテル・ハフツングＲｏｂｅｒｔ Ｂｏｓｃｈ Ｇｍｂｈ Control device and control method for controlling behavior of motor cycle
US10338223B1 (en) 2017-12-13 2019-07-02 Luminar Technologies, Inc. Processing point clouds of vehicle sensors having variable scan line distributions using two-dimensional interpolation and distance thresholding
TWI645242B (en) * 2018-03-29 2018-12-21 鴻海精密工業股份有限公司 Image capturing device
JP2583641B2 (en) 1990-05-18 1997-02-19 日産自動車株式会社 Cruise control method
JP2001034898A (en) * 1999-07-21 2001-02-09 Toyota Central Res & Dev Lab Inc Investigation disable area estimating device and driving support system
DE19945268A1 (en) * 1999-09-21 2001-03-22 Bosch Gmbh Robert Method and apparatus for detecting states in a system for automatic longitudinal and / or transverse control in a motor vehicle
DE19948254C2 (en) * 1999-10-07 2002-07-11 Bayerische Motoren Werke Ag A method for detecting contamination and / or blindness in one operating according to the radar or lidar principle sensor
KR100398529B1 (en) * 2001-06-15 2003-09-19 현대자동차주식회사 Sensing device of timing belt exchange and control method thereof
JP2003329773A (en) * 2002-05-10 2003-11-19 Hitachi Ltd Vehicle control apparatus with a plurality of distance detecting sensor installed
JP3636184B2 (en) * 2002-07-31 2005-04-06 株式会社デンソー And id registration method and id registration system of the tire air pressure sensor, tire pressure monitoring systems, tire pressure sensors and smart control systems
CN1416033A (en) * 2002-10-30 2003-05-07 徐州重型机械厂 Device for controlling outriggers of fire fighting vehicle of high altitude
US20060100783A1 (en) 2004-10-21 2006-05-11 Sick Ag Monitoring the surroundings of a vehicle
JP4517393B2 (en) * 2005-02-16 2010-08-04 株式会社デンソー Driving assistance device
JP2006260217A (en) * 2005-03-17 2006-09-28 Advics:Kk Traveling support device for vehicle
CN100447001C (en) * 2005-04-06 2008-12-31 肖 国;肖东平 Car shock-buffering method utilizing vibration between wheel and car body for power supply
CN100470411C (en) * 2005-08-25 2009-03-18 比亚迪股份有限公司 Four-wheel drive control system for pure electric automobile
JP2008293099A (en) * 2007-05-22 2008-12-04 Mazda Motor Corp Driving support device for vehicle
CN201096539Y (en) * 2007-08-09 2008-08-06 苏州工业园区美尔冷却系统有限公司 Vehicular temperature sensor
JP2009086788A (en) * 2007-09-28 2009-04-23 Hitachi Ltd Vehicle surrounding monitoring device
JP2009093431A (en) * 2007-10-09 2009-04-30 Toyota Motor Corp Speed control system
JP5319255B2 (en) * 2008-12-03 2013-10-16 綜合警備保障株式会社 Autonomous mobile body and speed control method
JP5146292B2 (en) * 2008-12-08 2013-02-20 トヨタ自動車株式会社 Course evaluation device
US20100194593A1 (en) * 2009-02-05 2010-08-05 Paccar Inc Neural network for intelligent transportation systems
JP5613398B2 (en) * 2009-10-29 2014-10-22 富士重工業株式会社 Intersection driving support device
US8761990B2 (en) * 2011-03-30 2014-06-24 Microsoft Corporation Semi-autonomous mobile device driving with obstacle avoidance
2011-06-01 US US13/150,385 patent/US8589014B2/en active Active
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US8589014B2 (en) 2013-11-19
US10347127B2 (en) 2019-07-09 Driving mode adjustment
KR20180031056A (en) 2018-03-27 A robust method for detecting traffic signals and their associated states
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