System and method for curb detection and pedestrian hazard assessment

Abstract

A detection system for a vehicle is provided. The detection system may include at least one image capture device and a data interface, wherein the at least one image capture device is configured to acquire multiple images of an area in front of the vehicle, the area including a curb separating a road surface from a non-road surface. The detection system may also include at least one processing device programmed to receive the multiple images via the data interface and determine multiple curb edge line candidates in the multiple images. The at least one processing device may be further programmed to identify at least one curb edge line candidate as an edge line of the curb.

Description

[0001] This application is a divisional application of the invention patent application with application date of May 14, 2015, application number 201580038137.9, and invention name “System and method for curb detection and pedestrian hazard assessment”.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims the benefit of priority to U.S. Provisional Patent Application No. 61 / 993,050, filed May 14, 2014. The foregoing application is incorporated herein by reference in its entirety. Technical Field

[0004] The present disclosure relates generally to autonomous vehicle navigation and, more particularly, to systems and methods for using cameras to detect curbs associated with a roadway and assess potential pedestrian hazards. Background Art

[0005] As technology continues to advance, the goal of fully autonomous vehicles capable of navigating roads is nearing realization. First, autonomous vehicles are able to recognize their environment and navigate without input from a human operator. Autonomous vehicles can also consider various factors and make appropriate decisions based on these factors to safely and accurately reach their intended destination. For example, when a vehicle typically travels on a road, it encounters various objects, such as other vehicles and pedestrians. Autonomous driving systems can identify these objects in the vehicle's environment and take appropriate and timely action to avoid collisions. Furthermore, autonomous driving systems can recognize other indicators, such as traffic signals, traffic signs, and lane markings, that regulate vehicle movement (e.g., when a vehicle must stop and when it can travel, the speed a vehicle must not exceed, the location a vehicle must be on a road, etc.). Autonomous driving systems may need to determine when a vehicle should change lanes, turn at an intersection, change roads, etc. As is apparent from these examples, many factors need to be addressed in order to provide autonomous vehicles capable of navigating safely and accurately. Summary of the Invention

[0006] According to embodiments of the present disclosure, systems and methods for autonomous vehicle navigation are provided. The disclosed embodiments may use cameras to provide autonomous vehicle navigation features. For example, according to the disclosed embodiments, the disclosed system may include one, two, or more cameras that monitor the vehicle's environment and induce a navigation response based on analysis of images captured by one or more of the cameras.

[0007] According to the disclosed embodiments, a detection system for a vehicle is provided. The detection system may include at least one image capture device and a data interface, wherein the at least one image capture device is programmed to acquire multiple images of an area in front of the vehicle, the area including a curb separating a road surface from a non-road surface. The detection system may also include at least one processing device configured to receive the multiple images via the data interface and determine multiple curb edge line candidates in the multiple images. The at least one processing device may be further programmed to identify at least one curb edge line candidate as an edge line of the curb.

[0008] According to another embodiment, a vehicle is provided. The vehicle may include a vehicle body, at least one image capture device, and a data interface, wherein the at least one image capture device is configured to capture multiple images of an area in front of the vehicle, the area including a curb separating a road surface from a non-road surface. The vehicle may also include at least one processing device programmed to receive the multiple images via the data interface and determine multiple curb edge line candidates in the multiple images. The at least one processing device may be further programmed to identify at least one curb edge line candidate as an edge line of the curb, and based on the at least one curb edge line, identify areas corresponding to the road surface and the non-road surface in the multiple images.

[0009] According to another embodiment, a method for curb detection is provided. The method may include acquiring, via at least one image capture device, multiple images of an area in front of a vehicle, the area including a curb separating a road surface from a non-road surface, and determining multiple curb edge line candidates in the multiple images. The method may also include determining a height of a step function associated with each curb edge line candidate, and identifying at least one curb edge line candidate as an edge line of the curb based on the determined height.

[0010] According to other disclosed embodiments, a non-transitory computer-readable storage medium may store program instructions that are executed by at least one processing device and perform any of the methods described herein.

[0011] The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments disclosed. In the drawings:

[0013] Figure 1 is a pictorial representation of an example system in accordance with the disclosed embodiments.

[0014] Figure 2Ais a diagrammatic side view representation of an example vehicle including a system according to the disclosed embodiments.

[0015] Figure 2B According to the disclosed embodiment Figure 2A Diagrammatic top view representation of the vehicle and systems shown.

[0016] Figure 2C is a diagrammatic top view representation of another embodiment of a vehicle including a system according to the disclosed embodiments.

[0017] Figure 2D is a diagrammatic top view representation of yet another embodiment of a vehicle including a system according to the disclosed embodiments.

[0018] Figure 2E is a diagrammatic top view representation of yet another embodiment of a vehicle including a system according to the disclosed embodiments.

[0019] Figure 2F is a pictorial representation of an example vehicle control system in accordance with the disclosed embodiments.

[0020] Figure 3A is a pictorial representation of the interior of a vehicle including a rearview mirror and a user interface for a vehicle imaging system according to a disclosed embodiment.

[0021] Figure 3B is an illustration of an example of a camera installation configured to be positioned behind a rearview mirror and against a vehicle windshield in accordance with the disclosed embodiments.

[0022] Figure 3C According to the disclosed embodiment Figure 3B Illustrations of the camera installation from different perspectives are shown.

[0023] Figure 3D is an illustration of an example of a camera installation configured to be positioned behind a rearview mirror and against a vehicle windshield in accordance with the disclosed embodiments.

[0024] Figure 4 is an example block diagram of a memory configured to store instructions for performing one or more operations in accordance with the disclosed embodiments.

[0025] Figure 5A is a flow chart illustrating an example process for eliciting one or more navigation responses based on monocular image analysis in accordance with the disclosed embodiments.

[0026] Figure 5B is a flow chart illustrating an example process for detecting one or more vehicles and / or pedestrians in a set of images in accordance with the disclosed embodiments.

[0027] Figure 5Cis a flow chart illustrating an example process for detecting road markings and / or lane geometry information in a set of images according to the disclosed embodiments.

[0028] Figure 5D is a flow chart illustrating an example process for detecting traffic lights in a set of images according to the disclosed embodiments.

[0029] Figure 5E is a flow chart illustrating an example process for eliciting one or more navigation responses based on a vehicle path according to the disclosed embodiments.

[0030] Figure 5F is a flow chart illustrating an example process for determining whether a leading vehicle is changing lanes in accordance with the disclosed embodiments.

[0031] Figure 6 is a flow chart illustrating an example process for eliciting one or more navigation responses based on stereo image analysis in accordance with the disclosed embodiments.

[0032] Figure 7 is a flow chart illustrating an example process for eliciting one or more navigational responses based on analysis of three sets of images in accordance with the disclosed embodiments.

[0033] Figure 8 is a block diagram of an exemplary memory configured to store instructions for performing one or more operations in accordance with the disclosed embodiments.

[0034] Figure 9 is an illustration of an image of an environment of a vehicle in accordance with the disclosed embodiments.

[0035] Figure 10A and Figure 10B is an illustration of an image including a curb according to the disclosed embodiments.

[0036] Figure 11 is an illustration of an exemplary modeled curb according to the disclosed embodiments.

[0037] Figure 12 is a flow chart of an example process for identifying curbs within an image or video sequence according to the disclosed embodiments.

[0038] Figure 13 is a flow chart of an example process for assessing the hazard presented by a pedestrian in accordance with the disclosed embodiments. DETAILED DESCRIPTION

[0039] The following detailed description refers to the accompanying drawings. Whenever possible, the same reference numerals are used in the drawings and the following description to refer to the same or similar parts. Although several exemplary embodiments are described herein, modifications, adaptations, and other implementations are possible. For example, replacements, additions, or modifications may be made to the components shown in the drawings, and the exemplary methods described herein may be modified by replacing, reordering, removing, or adding steps to the disclosed methods. Therefore, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the proper scope is defined by the appended claims.

[0040] Figure 1 is a block diagram representation of a system 100 according to the disclosed example embodiments. Depending on the requirements of a particular implementation, the system 100 may include various components. In some embodiments, the system 100 may include a processing unit 110, an image acquisition unit 120, a position sensor 130, one or more memory units 140, 150, a map database 160, and a user interface 170. The processing unit 110 may include one or more processing devices. In some embodiments, the processing unit 110 may include an application processor 180, an image processor 190, or any other suitable processing device. Similarly, depending on the requirements of a particular application, the image acquisition unit 120 may include any number of image acquisition devices and components. In some embodiments, the image acquisition unit 120 may include one or more image capture devices (e.g., cameras), such as image capture device 122, image capture device 124, and image capture device 126. The system 100 may also include a data interface 128 that communicatively connects the processing device 110 to the image acquisition device 120. For example, the data interface 128 may include any wired and / or wireless link(s) for transmitting image data acquired by the image acquisition device 120 to the processing unit 110 .

[0041] Both the application processor 180 and the image processor 190 may include various types of processing devices. For example, either or both of the application processor 180 and the image processor 190 may include a microprocessor, a preprocessor (such as an image preprocessor), a graphics processor, a central processing unit (CPU), auxiliary circuits, a digital signal processor, an integrated circuit, a memory, or any other type of device suitable for running applications and suitable for image processing and analysis. In some embodiments, the application processor 180 and / or the image processor 190 may include any type of single-core or multi-core processor, a mobile device microcontroller, a central processing unit, etc. Various processing devices may be used, including, for example, processors available from, for example, processors from manufacturers such as , and can include various architectures (e.g., x86 processors, wait).

[0042] In some embodiments, the application processor 180 and / or the image processor 190 may include a processor that can be Any of the EyeQ series processor chips available. These processor designs include multiple processing units with local memory and instruction sets. Such processors may include video inputs for receiving image data from multiple image sensors and may also include video output capabilities. In one example, 90 nanometer-micron technology operating at 332 MHz was used. The architecture consists of two floating-point hyperthreaded 32-bit RISC CPUs ( cores), five Visual Computing Engines (VCEs), and three vector microcode processors The MIPS34K CPU manages the five VCEs, three VMPs, and a series of peripherals. TM and DMA, a second MIPS34KCPU and multi-channel DMA and other peripherals. These five VCEs, three and MIPS34K CPU can perform the intensive visual calculations required by multi-function bundled applications. In another example, as a third-generation processor and Six times stronger can be used in the disclosed embodiments.

[0043] Any processing device disclosed herein can be configured to perform certain functions. Configuring a processing device (such as any described EyeQ processor or other controller or microprocessor) to perform certain functions can include programming computer-executable instructions and making these instructions available to the processing device during its operation for execution. In some embodiments, configuring the processing device can include programming the processing device directly with architecture instructions. In other embodiments, configuring the processing device can include storing the executable instructions in a memory accessible to the processing device during operation. For example, the processing device can access the memory during operation to obtain and execute the stored instructions.

[0044] although Figure 1 Two separate processing devices are depicted as being included in processing unit 110, but more or fewer processing devices may be used. For example, in some embodiments, a single processing device may be used to perform the tasks of application processor 180 and image processor 190. In other embodiments, these tasks may be performed by more than two processing devices.

[0045] The processing unit 110 may include various types of devices. For example, the processing unit 110 may include various devices such as a controller, an image preprocessor, a central processing unit (CPU), auxiliary circuits, a digital signal processor, an integrated circuit, memory, or any other type of device for image processing and analysis. The image preprocessor may include a video processor for capturing, digitizing, and processing images from the image sensor. The CPU may include any number of microcontrollers or microprocessors. The auxiliary circuits may include any number of circuits known in the art, including caches, power supplies, clocks, and input / output circuits. The memory may store software that, when executed by the processor, controls the operation of the system. The memory may include a database and image processing software. The memory may include any number of random access memories, read-only memories, flash memories, disk drives, optical storage, tape storage, removable storage, and other types of storage. In one embodiment, the memory may be separate from the processing unit 110. In another embodiment, the memory may be integrated into the processing unit 110.

[0046] Each memory 140, 150 may include software instructions that, when executed by a processor (e.g., application processor 180 and / or image processor 190), may control the operation of various aspects of system 100. These memory units may include various databases and image processing software. The memory units may include random access memory, read-only memory, flash memory, disk drives, optical storage, tape storage, removable storage, and / or any other type of storage. In some embodiments, the memory units 140, 150 may be separate from the application processor 180 and / or image processor 190. In other embodiments, these memory units may be integrated into the application processor 180 and / or image processor 190.

[0047] Position sensor 130 may include any type of device suitable for determining a location associated with at least one component of system 100. In some embodiments, position sensor 130 may include a GPS receiver. Such a receiver may determine user location and velocity by processing signals broadcast by global positioning system satellites. Position information from position sensor 130 may be made available to application processor 180 and / or image processor 190.

[0048] The user interface 170 may include any device suitable for providing information to or receiving input from one or more users of the system 100. In some embodiments, the user interface 170 may include a user input device, including, for example, a touch screen, a microphone, a keyboard, a pointing device, a tracking wheel, a camera, knobs, buttons, etc. Using such input devices, a user can provide information input or commands to the system 100 by typing instructions or information, providing voice commands, selecting menu options on a screen using buttons, a pointer, or eye tracking capabilities, or by any other suitable technique for communicating information to the system 100.

[0049] The user interface 170 may be equipped with one or more processing devices configured to provide and receive information to and from a user, and to process this information for use by, for example, the application processor 180. In some embodiments, such processing devices may execute instructions to recognize and track eye movements, receive and interpret voice commands, recognize and interpret touches and / or gestures made on a touch screen, respond to keyboard input or menu selections, etc. In some embodiments, the user interface 170 may include a display, a speaker, a haptic device, and / or any other device for providing output information to a user.

[0050] Map database 160 may comprise any type of database for storing map data useful to system 100. In some embodiments, map database 160 may include data relating to the locations of various items within a reference coordinate system, including roads, water features, geographic features, commercial areas, points of interest, restaurants, gas stations, and the like. Map database 160 may store not only the locations of these items, but also descriptors associated with these items, including, for example, names associated with any stored features. In some embodiments, map database 160 may be physically located with other components of system 100. Alternatively or additionally, map database 160 or a portion thereof may be remotely located relative to other components of system 100 (e.g., processing unit 110). In such embodiments, information from map database 160 may be downloaded via a wired or wireless data connection to a network (e.g., via a cellular network and / or the Internet, etc.).

[0051] Image capture devices 122, 124, and 126 may each include any type of device suitable for capturing at least one image from the environment. Furthermore, any number of image capture devices may be used to acquire images for input to the image processor. Some embodiments may include only a single image capture device, while other embodiments may include two, three, or even four, or more image capture devices. Figure 2B-2E Image capture devices 122, 124, and 126 are further described.

[0052] The system 100 or its various components may be incorporated into a variety of different platforms. In some embodiments, the system 100 may be included on a vehicle 200, such as Figure 2A For example, the vehicle 200 may be equipped with a processing unit 110 and a Figure 1 While in some embodiments, the vehicle 200 may be equipped with only a single image capture device (e.g., a camera), in other embodiments, such as a combination of Figure 2B-2E As discussed above, multiple image capture devices may be used. For example, Figure 2A Either of the image capture devices 122 and 124 of the vehicle 200 shown in FIG. 2 may be part of an ADAS (Advanced Driver Assistance System) imaging set.

[0053] The image capture device included as part of the image acquisition unit 120 on the vehicle 200 may be placed at any suitable location. Figure 2A-2E and Figure 3A-3C As shown, the image capture device 122 can be located near the rearview mirror. This location can provide a line of sight similar to the line of sight of the driver of the vehicle 200, which can help determine what is visible to the driver and what is not visible to the driver. The image capture device 122 can be positioned anywhere near the rearview mirror, but placing the image capture device 122 on the driver's side of the rearview mirror can further help obtain an image representative of the driver's field of view and / or line of sight.

[0054] Other locations for the image capture devices of image acquisition unit 120 may also be used. For example, image capture device 124 may be located on or in the bumper of vehicle 200. This location is particularly suitable for image capture devices with a wide field of view. The line of sight of an image capture device located in the bumper may differ from the driver's line of sight, and therefore, the bumper image capture device and the driver may not always see the same objects. Image capture devices (e.g., image capture devices 122, 124, and 126) may also be located in other locations. For example, the image capture device may be located on or in one or both of the sideview mirrors of vehicle 200, on the roof of vehicle 200, on the hood of vehicle 200, on the trunk of vehicle 200, on the side of vehicle 200, mounted on any window of vehicle 200, positioned behind any window of vehicle 200, positioned in front of any window of vehicle 200, mounted in or near a light figure on the front and / or back of vehicle 200, and the like.

[0055] In addition to the image capture device, the vehicle 200 may also include various other components of the system 100. For example, the processing unit 110 may be included on the vehicle 200, integrated with the vehicle's engine control unit (ECU), or separate. The vehicle 200 may also be equipped with a location sensor 130, such as a GPS receiver, and may also include a map database 160 and memory units 140 and 150.

[0056] Figure 2A is a diagrammatic side view representation of an example vehicle imaging system in accordance with the disclosed embodiments. Figure 2B yes Figure 2A A diagrammatic top view illustration of the embodiment shown in FIG. Figure 2B As shown, the disclosed embodiments may include a vehicle 200 including a system 100 in its body having a first image capture device 122 located near a rearview mirror of the vehicle 200 and / or near a driver, a second image capture device 124 located on or in a bumper area (e.g., one of the bumper areas 210) of the vehicle 200, and a processing unit 110.

[0057] like Figure 2C As shown, both image capture devices 122 and 124 may be located near the rearview mirror of vehicle 200 and / or near the driver. Figure 2B and Figure 2C Two image capture devices 122 and 124 are shown, it should be understood that other embodiments may include more than two image capture devices. Figure 2D and Figure 2E In the embodiment shown in , a first image capture device 122 , a second image capture device 124 , and a third image capture device 126 are included in the system 100 of a vehicle 200 .

[0058] like Figure 2D As shown, image capture device 122 may be located near a rearview mirror of vehicle 200 and / or near the driver, and image capture devices 124 and 126 may be located on or in a bumper area (e.g., one of bumper areas 210) of vehicle 200. Figure 2E As shown, image capture devices 122, 124, and 126 may be located near rearview mirrors and / or near the driver's seat of vehicle 200. The disclosed embodiments are not limited to any particular number and configuration of image capture devices, and the image capture devices may be located in any suitable location within or on vehicle 200.

[0059] It should be understood that the disclosed embodiments are not limited to vehicles and can be applied in other scenarios. It should also be understood that the disclosed embodiments are not limited to a specific type of vehicle 200 and can be applicable to all types of vehicles, including cars, trucks, trailers, and other types of vehicles.

[0060] The first image capture device 122 may include any suitable type of image capture device. The image capture device 122 may include an optical axis. In one example, the image capture device 122 may include an Aptina M9V024W VGA sensor with a global shutter. In other embodiments, the image capture device 122 may provide a resolution of 1280×960 pixels and may include a rolling shutter. The image capture device 122 may include various optical elements. In some embodiments, one or more lenses may be included, for example, to provide a desired focal length and field of view for the image capture device. In some embodiments, the image capture device 122 may be associated with a 6 mm lens or a 12 mm lens. In some embodiments, as Figure 2D As shown, the image capture device 122 can be configured to capture images with a desired field of view (FOV) 202. For example, the image capture device 122 can be configured to have a conventional FOV, such as in the range of 40 degrees to 56 degrees, including a 46 degree FOV, a 50 degree FOV, a 52 degree FOV, or a larger FOV. Alternatively, the image capture device 122 can be configured to have a narrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or a 36 degree FOV. In addition, the image capture device 122 can be configured to have a wide FOV in the range of 100 to 180 degrees. In some embodiments, the image capture device 122 can include a wide-angle bumper camera or a camera with a FOV of up to 180 degrees.

[0061] The first image capture device 122 can capture a plurality of first images of a scene associated with the vehicle 200. Each of the plurality of first images can be captured as a series of image scan lines, which can be captured using a rolling shutter. Each scan line can include a plurality of pixels.

[0062] The first image capture device 122 may have a scan rate associated with the acquisition of each of the first series of image scan lines. The scan rate may refer to the scan rate at which the image sensor may acquire image data associated with each pixel included in a particular scan line.

[0063] Image capture devices 122, 124, and 126 may include any suitable type and number of image sensors, including, for example, CCD sensors or CMOS sensors. In one embodiment, CMOS image sensors may be employed with a rolling shutter so that each pixel in a row is read one at a time, and scanning of the rows continues on a row-by-row basis until the entire image frame has been captured. In some embodiments, the rows may be captured sequentially from top to bottom relative to the frame.

[0064] The use of a rolling shutter may cause pixels in different rows to be exposed and captured at different times, which may cause distortions and other image artifacts in the captured image frame. On the other hand, when image capture device 122 is configured to operate with a global or synchronized shutter, all pixels may be exposed for the same amount of time and during a common exposure period. As a result, the image data in a frame collected from a system employing a global shutter represents a snapshot of the entire FOV (such as FOV 202) at a particular time. In contrast, in a rolling shutter application, each row in the frame is exposed and data is captured at a different time. As a result, moving objects may appear distorted in an image capture device with a rolling shutter. This phenomenon will be described in more detail below.

[0065] Second image capture device 124 and third image capture device 126 can be any type of image capture device. Similar to first image capture device 122, each of image capture devices 124 and 126 can include an optical axis. In one embodiment, each of image capture devices 124 and 126 can include an Aptina M9V024 WVGA sensor with a global shutter. Alternatively, each of image capture devices 124 and 126 can include a rolling shutter. Similar to image capture device 122, image capture devices 124 and 126 can be configured to include various lenses and optical elements. In some embodiments, the lenses associated with image capture devices 124 and 126 can provide a FOV (such as FOVs 204 and 206) that is equal to or narrower than the FOV associated with image capture device 122 (such as FOV 202). For example, image capture devices 124 and 126 can have a FOV of 40 degrees, 30 degrees, 26 degrees, 23 degrees, 20 degrees, or less.

[0066] Image capture devices 124 and 126 can acquire a plurality of second and third images of a scene associated with vehicle 200. Each of the plurality of second and third images can be acquired as a second and third series of image scan lines, which can be captured using a rolling shutter. Each scan line or row can have a plurality of pixels. Image capture devices 124 and 126 can have a second and third scan rate associated with the acquisition of each image scan line included in the second and third series.

[0067] Each image capture device 122, 124, and 126 can be placed at any suitable location and orientation relative to vehicle 200. The relative positions of image capture devices 122, 124, and 126 can be selected to facilitate fusing information acquired from the image capture devices. For example, in some embodiments, the FOV associated with image capture device 124 (such as FOV 204) may partially or completely overlap with the FOV associated with image capture device 122 (e.g., FOV 202) and the FOV associated with image capture device 126 (e.g., FOV 206).

[0068] Image capture devices 122, 124, and 126 may be positioned at any suitable relative heights on vehicle 200. In one example, there may be height differences between image capture devices 122, 124, and 126 that may provide sufficient parallax information to enable stereo analysis. Figure 2A As shown, the two image capture devices 122 and 124 are at different heights. For example, there may also be lateral displacement differences between the image capture devices 122, 124, and 126, providing additional parallax information for the stereo analysis of the processing unit 110. Figure 2C and Figure 2D As shown, the difference in lateral displacement can be expressed by d x In some embodiments, there may be a forward or backward displacement (e.g., a range displacement) between image capture devices 122, 124, and 126. For example, image capture device 122 may be positioned 0.5 to 2 meters or more behind image capture device 124 and / or image capture device 126. This type of displacement may enable one of the image capture devices to cover a potential blind spot of the other(s).

[0069] Image capture device 122 may have any suitable resolution capability (e.g., the number of pixels associated with the image sensor), and the resolution of the image sensor(s) associated with image capture device 122 may be higher, lower, or the same as the resolution of the image sensor(s) associated with image capture devices 124 and 126. In some embodiments, the image sensor(s) associated with image capture device 122 and / or image capture devices 124 and 126 may have a resolution of 640×480, 1024×768, 1280×960, or any other suitable resolution.

[0070] The frame rate (e.g., the rate at which an image capture device acquires a set of pixel data for one image frame and then proceeds to capture pixel data associated with the next image frame) can be controllable. The frame rate associated with image capture device 122 can be higher, lower, or the same as the frame rates associated with image capture devices 124 and 126. The frame rates associated with image capture devices 122, 124, and 126 can depend on various factors that may affect the timing of the frame rates. For example, one or more of image capture devices 122, 124, and 126 can include a selectable pixel delay period that is applied before or after acquiring image data associated with one or more pixels of the image sensors in image capture devices 122, 124, and / or 126. Typically, image data corresponding to each pixel can be acquired according to the clock rate used for the device (e.g., one pixel per clock cycle). Additionally, in embodiments including a rolling shutter, one or more of image capture devices 122, 124, and 126 may include a selectable horizontal blanking period that is applied before or after acquiring image data associated with a row of pixels of an image sensor in image capture device 122, 124, and / or 126. Furthermore, one or more images of image capture devices 122, 124, and 126 may include a selectable vertical blanking period that is applied before or after acquiring image data associated with an image frame of image capture devices 122, 124, and 126.

[0071] These timing controls can enable synchronization of the frame rates associated with image capture devices 122, 124, and 126, even if the line rate rates of each are different. Furthermore, as will be discussed in more detail below, these selectable timing controls, along with other factors (e.g., image sensor resolution, maximum line rate, etc.), can enable synchronization of image capture from areas where the FOV of image capture device 122 overlaps with one or more of the FOVs of image capture devices 124 and 126, even if the field of view of image capture device 122 is different from the FOVs of image capture devices 124 and 126.

[0072] The frame rate timing in image capture devices 122, 124, and 126 may depend on the resolution of the associated image sensors. For example, assuming that the line scan rates of the two devices are similar, if one device includes an image sensor with a resolution of 640×480 and the other device includes an image sensor with a resolution of 1280×960, more time will be required to acquire a frame of image data from the sensor with the higher resolution.

[0073] Another factor that may affect the timing of image data acquisition in image capture devices 122, 124, and 126 is the maximum line scan rate. For example, a certain minimum amount of time will be required to acquire a line of image data from the image sensors included in image capture devices 122, 124, and 126. Assuming no pixel delay period is added, this minimum amount of time for acquiring a line of image data will be related to the maximum line scan rate for the particular device. Devices that provide higher maximum line scan rates have the potential to provide higher frame rates than devices with lower maximum line scan rates. In some embodiments, one or more of image capture devices 124 and 126 may have a maximum line scan rate that is higher than the maximum line scan rate associated with image capture device 122. In some embodiments, the maximum line scan rate of image capture devices 124 and / or 126 may be 1.25, 1.5, 1.75, or 2 times, or more, the maximum line scan rate of image capture device 122.

[0074] In another embodiment, image capture devices 122, 124, and 126 may have the same maximum line scan rate, but image capture device 122 may operate at a scan rate that is less than or equal to its maximum scan rate. The system may be configured such that one or more of image capture devices 124 and 126 operate at a line scan rate that is equal to the line scan rate of image capture device 122. In other examples, the system may be configured such that the line scan rate of image capture device 124 and / or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times, or more, greater than the line scan rate of image capture device 122.

[0075] In some embodiments, image capture devices 122, 124, and 126 may be asymmetric. That is, they may include cameras with different fields of view (FOVs) and focal lengths. For example, the fields of view of image capture devices 122, 124, and 126 may include any desired area relative to the environment of vehicle 200. In some embodiments, one or more of image capture devices 122, 124, and 126 may be configured to acquire image data from the environment in front of vehicle 200, behind vehicle 200, to the sides of vehicle 200, or a combination thereof.

[0076] Furthermore, the focal length associated with each image capture device 122, 124, and / or 126 can be selectable (e.g., by including an appropriate lens, etc.) so that each device captures images of objects at a desired range of distances relative to the vehicle 200. For example, in some embodiments, the image capture devices 122, 124, and 126 can capture images of close-up objects within a few meters from the vehicle. The image capture devices 122, 124, and 126 can also be configured to capture images of objects at greater ranges from the vehicle (e.g., 25 meters, 50 meters, 100 meters, 150 meters, or more). In addition, the focal lengths of image capture devices 122, 124, and 126 may be selected so that one image capture device (e.g., image capture device 122) may capture images of objects that are relatively close to the vehicle (e.g., within 10 meters or within 20 meters), while other image capture devices (e.g., image capture devices 124 and 126) may capture images of objects that are farther away from the vehicle 200 (e.g., greater than 20 meters, 50 meters, 100 meters, 150 meters, etc.).

[0077] According to some embodiments, the FOV of one or more image capture devices 122, 124, and 126 may have a wide angle. For example, having a FOV of 140 degrees may be advantageous, particularly for image capture devices 122, 124, and 126 that may be used to capture images of an area near vehicle 200. For example, image capture device 122 may be used to capture images of an area to the right or left of vehicle 200, and in such embodiments, it may be desirable for image capture device 122 to have a wide FOV (e.g., at least 140 degrees).

[0078] The FOV associated with each image capture device 122, 124, and 126 may depend on the respective focal length. For example, as the focal length increases, the corresponding field of view decreases.

[0079] Image capture devices 122, 124, and 126 can be configured to have any suitable field of view. In one specific example, image capture device 122 can have a horizontal FOV of 46 degrees, image capture device 124 can have a horizontal FOV of 23 degrees, and image capture device 126 can have a horizontal FOV between 23 degrees and 46 degrees. In another example, image capture device 122 can have a horizontal FOV of 52 degrees, image capture device 124 can have a horizontal FOV of 26 degrees, and image capture device 126 can have a horizontal FOV between 26 degrees and 52 degrees. In some embodiments, the ratio of the FOV of image capture device 122 to the FOV of image capture device 124 and / or image capture device 126 can vary from 1.5 to 2.0. In other embodiments, the ratio can vary between 1.25 and 2.25.

[0080] System 100 can be configured such that the field of view of image capture device 122 at least partially or completely overlaps the field of view of image capture device 124 and / or image capture device 126. In some embodiments, system 100 can be configured such that the fields of view of image capture devices 124 and 126, for example, fall within (e.g., are narrower than) the field of view of image capture device 122 and share a common center with the field of view of image capture device 122. In other embodiments, image capture devices 122, 124, and 126 can capture adjacent FOVs or can have partial overlap in their FOVs. In some embodiments, the fields of view of image capture devices 122, 124, and 126 can be aligned such that the center of narrower FOV image capture devices 124 and / or 126 can be located in the lower half of the field of view of wider FOV device 122.

[0081] Figure 2F is a diagrammatic representation of an example vehicle control system according to the disclosed embodiments. Figure 2F As indicated, the vehicle 200 may include a throttle system 220, a brake system 230, and a steering system 240. The system 100 may provide input (e.g., control signals) to one or more of the throttle system 220, the brake system 230, and the steering system 240 via one or more data links (e.g., any wired and / or wireless links for transmitting data). For example, based on an analysis of images acquired by the image capture devices 122, 124, and / or 126, the system 100 may provide control signals to one or more of the throttle system 220, the brake system 230, and the steering system 240 to navigate the vehicle 200 (e.g., by causing acceleration, steering, lane changes, etc.). In addition, the system 100 may receive input from one or more of the throttle system 220, the brake system 230, and the steering system 240 indicating an operating condition of the vehicle 200 (e.g., speed, whether the vehicle 200 is braking and / or steering, etc.). The following is in conjunction with Figure 4-Figure 7 Provide further details.

[0082] like Figure 3AAs shown, vehicle 200 may also include a user interface 170 for interacting with the driver or passengers of vehicle 200. For example, user interface 170 in a vehicle application may include a touch screen 320, a knob 330, a button 340, and a microphone 350. The driver or passenger of vehicle 200 may also interact with system 100 using a handle (e.g., located on or near the steering column of vehicle 200, including, for example, a turn signal handle), a button (e.g., located on the steering wheel of vehicle 200), or the like. In some embodiments, microphone 350 may be positioned adjacent to rearview mirror 310. Similarly, in some embodiments, image capture device 122 may be positioned near rearview mirror 310. In some embodiments, user interface 170 may also include one or more speakers 360 (e.g., speakers of a vehicle audio system). For example, system 100 may provide various notifications (e.g., alarms) via speakers 360.

[0083] Figure 3B-3D is an illustration of an example camera mount 370 configured to be located behind a rearview mirror (e.g., rearview mirror 310) and against a vehicle windshield in accordance with the disclosed embodiments. Figure 3B As shown, camera mount 370 may include image capture devices 122, 124, and 126. Image capture devices 124 and 126 may be located behind a sun visor 380, wherein sun visor 380 may be flush with the vehicle windshield and include a film and / or a composite of anti-reflective materials. For example, sun visor 380 may be positioned so that it is aligned with the vehicle's windshield having a matching bevel. In some embodiments, each of image capture devices 122, 124, and 126 may be located behind sun visor 380, such as at Figure 3D The disclosed embodiments are not limited to any particular configuration of image capture devices 122 , 124 , and 126 , camera mount 370 , and light shield 380 . Figure 3C yes Figure 3B An illustration of camera mount 370 is shown from a front perspective.

[0084] As will be appreciated by those skilled in the art having the benefit of this disclosure, numerous variations and / or modifications may be made to the aforementioned disclosed embodiments. For example, not all components are necessary for the operation of system 100. Furthermore, any component may be located in any suitable part of system 100, and the components may be rearranged into various configurations while still providing the functionality of the disclosed embodiments. Therefore, the aforementioned configurations are exemplary, and regardless of the configurations discussed above, system 100 may provide a wide range of functionality for analyzing the surroundings of vehicle 200 and navigating vehicle 200 in response to that analysis.

[0085] As discussed in greater detail below and in accordance with various disclosed embodiments, system 100 can provide various features related to autonomous driving and / or driver assistance technologies. For example, system 100 can analyze image data, location data (e.g., GPS location information), map data, speed data, and / or data from sensors included in vehicle 200. System 100 can collect data for analysis from, for example, image acquisition unit 120, location sensor 130, and other sensors. Furthermore, system 100 can analyze the collected data to determine whether vehicle 200 should take a certain action, and then automatically take the determined action without human intervention. For example, while vehicle 200 is navigating without human intervention, system 100 can automatically control braking, acceleration, and / or steering of vehicle 200 (e.g., by sending control signals to one or more of throttle system 220, braking system 230, and steering system 240). Furthermore, system 100 can analyze the collected data and, based on the analysis of the collected data, issue warnings and / or alerts to vehicle occupants. Additional details regarding various embodiments provided by system 100 are provided below.

[0086] Forward-facing multi-imaging system

[0087] As discussed above, system 100 can provide driver assistance functionality using a multi-camera system. A multi-camera system can use one or more cameras facing the front of the vehicle. In other embodiments, the multi-camera system can include one or more cameras facing the sides or rear of the vehicle. In one embodiment, for example, system 100 can use a dual-camera imaging system, wherein a first camera and a second camera (e.g., image capture devices 122 and 124) can be located in front of and / or on the sides of a vehicle (e.g., vehicle 200). The first camera can have a field of view that is larger than, smaller than, or partially overlaps with the field of view of the second camera. In addition, the first camera can be connected to a first image processor to perform monocular image analysis on the image provided by the first camera, and the second camera can be connected to a second image processor to perform monocular image analysis on the image provided by the second camera. The outputs (e.g., processed information) of the first and second image processors can be combined. In some embodiments, the second image processor can receive images from both the first and second cameras to perform stereo analysis. In another embodiment, system 100 can use a three-camera imaging system, wherein each camera has a different field of view. Thus, such a system can make decisions based on information derived from objects located at different distances in front of and to the sides of the vehicle. References to monocular image analysis can refer to instances where image analysis is performed based on images captured from a single viewpoint (e.g., from a single camera). Stereoscopic image analysis can refer to instances where image analysis is performed based on two or more images captured using one or more variations of image capture parameters. For example, captured images suitable for performing stereoscopic image analysis can include images captured from two or more different positions, from different fields of view, using different focal lengths, with parallax information, and the like.

[0088] For example, in one embodiment, system 100 may implement a three-camera configuration using image capture devices 122-126. In this configuration, image capture device 122 may provide a narrow field of view (e.g., 34 degrees, or other values ​​selected from a range of approximately 20 degrees to 45 degrees, etc.), image capture device 124 may provide a wide field of view (e.g., 150 degrees, or other values ​​selected from a range of approximately 100 degrees to approximately 180 degrees), and image capture device 126 may provide an intermediate field of view (e.g., 46 degrees, or other values ​​selected from a range of approximately 35 degrees to approximately 60 degrees). In some embodiments, image capture device 126 may serve as a primary or main camera. Image capture devices 122-126 may be located behind rearview mirror 310 and arranged substantially side-by-side (e.g., 6 centimeters apart). Furthermore, in some embodiments, as discussed above, one or more of image capture devices 122-126 may be mounted behind sun visor 380 flush with the windshield of vehicle 200. Such shielding may act to reduce the effect of any reflections from the interior of the vehicle on the image capture devices 122 - 126 .

[0089] In another embodiment, as above combined Figure 3B and 3C As discussed, the wide field of view camera (e.g., image capture device 124 in the above example) can be mounted lower than the narrow field of view camera and the main field of view camera (e.g., image capture devices 122 and 126 in the above example). This configuration can provide a clear line of sight from the wide field of view camera. To reduce reflections, the camera can be mounted closer to the windshield of vehicle 200, and a polarizer can be included on the camera to dampen reflected light.

[0090] A three-camera system can provide certain performance characteristics. For example, some embodiments may include the ability to verify the detection of an object by one camera based on the detection results from another camera. In the three-camera configuration discussed above, the processing unit 110 may include, for example, three processing devices (e.g., three EyeQ series processor chips as discussed above), where each processing device is dedicated to processing images captured by one or more of the image capture devices 122-126.

[0091] In a three-camera system, the first processing device can receive images from both the main camera and the narrow field of view camera and perform vision processing on the narrow FOV camera to, for example, detect other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Furthermore, the first processing device can calculate the disparity of pixels between the images from the main camera and the narrow camera and create a 3D reconstruction of the vehicle 200's environment. The first processing device can then combine this 3D reconstruction with 3D map data or with 3D information calculated based on information from another camera.

[0092] The second processing device can receive images from the primary camera and perform visual processing to detect other vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. Additionally, the second processing device can calculate camera displacement and, based on this displacement, calculate the disparity of pixels between consecutive images and create a 3D reconstruction of the scene (e.g., structure from motion). The second processing device can send the structure from motion-based 3D reconstruction to the first processing device for combination with the stereoscopic 3D image.

[0093] The third processing device may receive images from the wide FOV camera and process the images to detect vehicles, pedestrians, lane markings, traffic signs, traffic lights, and other road objects. The third processing device may also execute additional processing instructions to analyze the images to identify moving objects in the images, such as vehicles changing lanes, pedestrians, etc.

[0094] In some embodiments, having streams of image-based information captured and processed independently can provide an opportunity to provide redundancy in the system. Such redundancy can include, for example, using a first image capture device and images processed from that device to verify and / or supplement information obtained by capturing and processing image information from at least a second image capture device.

[0095] In some embodiments, system 100 may use two image capture devices (e.g., image capture devices 122 and 124) to provide navigation assistance for vehicle 200, and a third image capture device (e.g., image capture device 126) to provide redundancy and validate the analysis of data received from the other two image capture devices. For example, in this configuration, image capture devices 122 and 124 may provide images for stereo analysis by system 100 to navigate vehicle 200, while image capture device 126 may provide images for monocular analysis by system 100 to provide redundancy and validate information obtained based on images captured from image capture devices 122 and / or 124. That is, image capture device 126 (and the corresponding processing device) may be considered to provide a redundant subsystem for providing a check on the analysis obtained from image capture devices 122 and 124 (e.g., to provide an automatic emergency braking (AEB) system).

[0096] Those skilled in the art will recognize that the above-described camera configurations, camera placements, number of cameras, camera positions, etc. are merely examples. These components and other components described with respect to the overall system can be assembled and used in a variety of different configurations without departing from the scope of the disclosed embodiments. Further details regarding the use of a multi-camera system to provide driver assistance and / or autonomous vehicle functionality are as follows.

[0097] Figure 4 is an example functional block diagram of memory 140 and / or 150 that may be stored / programmed with instructions for performing one or more operations in accordance with the disclosed embodiments. Although reference is made below to memory 140, those skilled in the art will recognize that instructions may be stored in memory 140 and / or 150.

[0098] like Figure 4 As shown, memory 140 may store a monocular image analysis module 402, a stereoscopic image analysis module 404, a velocity and acceleration module 406, and a navigation response module 408. The disclosed embodiments are not limited to any particular configuration of memory 140. Furthermore, application processor 180 and / or image processor 190 may execute instructions stored in any of modules 402-408 included in memory 140. Those skilled in the art will appreciate that in the following discussion, references to processing unit 110 may refer individually or collectively to application processor 180 and image processor 190. Thus, the steps of any of the following processes may be performed by one or more processing devices.

[0099] In one embodiment, the monocular image analysis module 402 may store instructions (such as computer vision software) that, when executed by the processing unit 110, perform a monocular image analysis of a set of images acquired by one of the image capture devices 122, 124, and 126. In some embodiments, the processing unit 110 may combine information from the set of images with additional sensory information (e.g., information from radar) to perform the monocular image analysis. Figures 5A-5D As described, the monocular image analysis module 402 may include instructions for detecting a set of features within the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, and any other features associated with the vehicle's environment. Based on this analysis, the system 100 (e.g., via the processing unit 110) may cause one or more navigation responses in the vehicle 200, such as steering, lane changes, changes in acceleration, etc., as discussed below in conjunction with the navigation response module 408.

[0100] In one embodiment, the stereo image analysis module 404 may store instructions (such as computer vision software) that, when executed by the processing unit 110, perform stereo image analysis on a first set of images and a second set of images acquired by a combination of image capture devices selected from any of the image capture devices 122, 124, and 126. In some embodiments, the processing unit 110 may combine information from the first set of images and the second set of images with additional sensing information (e.g., information from radar) to perform stereo image analysis. For example, the stereo image analysis module 404 may include instructions for performing stereo image analysis based on a first set of images acquired by the image capture device 124 and a second set of images acquired by the image capture device 126. As described below in conjunction with Figure 6 As described, the stereo image analysis module 404 may include instructions for detecting a set of features within the first set of images and the second set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, etc. Based on this analysis, the processing unit 110 may cause one or more navigation responses in the vehicle 200, such as steering, lane changes, changes in acceleration, etc., as discussed below in conjunction with the navigation response module 408.

[0101] In one embodiment, the speed and acceleration module 406 may store software configured to analyze data received from one or more computing and electromechanical devices in the vehicle 200 configured to cause changes in the speed and / or acceleration of the vehicle 200. For example, the processing unit 110 may execute instructions associated with the speed and acceleration module 406 to calculate a target speed for the vehicle 200 based on data resulting from the execution of the monocular image analysis module 402 and / or the stereo image analysis module 404. Such data may include, for example, target position, speed, and / or acceleration, the position and / or speed of the vehicle 200 relative to nearby vehicles, pedestrians, or road objects, position information of the vehicle 200 relative to lane markings on the road, etc. Furthermore, the processing unit 110 may calculate the target speed for the vehicle 200 based on sensory input (e.g., information from radar) and input from other systems of the vehicle 200, such as the throttle system 220, the braking system 230, and / or the steering system 240. Based on the calculated target speed, the processing unit 110 can send electronic signals to the throttle system 220, braking system 230 and / or steering system 240 of the vehicle 200 to trigger a change in speed and / or acceleration, such as by physically depressing the brakes or releasing the accelerator of the vehicle 200.

[0102] In one embodiment, the navigation response module 408 may store software that is executable by the processing unit 110 to determine a desired navigation response based on data derived from the execution of the monocular image analysis module 402 and / or the stereo image analysis module 404. Such data may include position and velocity information associated with nearby vehicles, pedestrians, and road objects, target position information for the vehicle 200, and the like. Additionally, in some embodiments, the navigation response may be based (in part or in whole) on map data, a predetermined position of the vehicle 200, and / or a relative velocity or acceleration between the vehicle 200 and one or more objects detected from the execution of the monocular image analysis module 402 and / or the stereo image analysis module 404. The navigation response module 408 may also determine the desired navigation response based on sensory input (e.g., information from radar) and input from other systems of the vehicle 200, such as the throttle system 220, the braking system 230, and the steering system 240 of the vehicle 200. Based on the desired navigation response, processing unit 110 may send electronic signals to throttle system 220, brake system 230, and steering system 240 of vehicle 200 to trigger the desired navigation response, such as by turning the steering wheel of vehicle 200 to achieve a predetermined angle of rotation. In some embodiments, processing unit 110 may use the output of navigation response module 408 (e.g., the desired navigation response) as input to execution of velocity and acceleration module 406 for calculating the change in velocity of vehicle 200.

[0103] Figure 5A is a flow chart illustrating an example process 500A for causing one or more navigation responses based on monocular image analysis according to the disclosed embodiments. In step 510, the processing unit 110 may receive a plurality of images via the data interface 128 between the processing unit 110 and the image acquisition unit 120. For example, a camera (such as the image capture device 122 having the field of view 202) included in the image acquisition unit 120 may capture a plurality of images of an area in front of the vehicle 200 (e.g., or to the side or rear of the vehicle) and send them to the processing unit 110 via a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.). In step 520, the processing unit 110 may execute the monocular image analysis module 402 to analyze the plurality of images, as described below in conjunction with Figures 5B-5D By performing this analysis, processing unit 110 may detect a set of features within the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, etc.

[0104] In step 520, the processing unit 110 may also execute the monocular image analysis module 402 to detect various road hazards, such as parts of truck tires, fallen road signs, loose cargo, small animals, etc. Road hazards may vary in structure, shape, size, and color, which may make the detection of such hazards more challenging. In some embodiments, the processing unit 110 may execute the monocular image analysis module 402 to perform multi-frame analysis on the multiple images to detect road hazards. For example, the processing unit 110 may estimate the camera motion between consecutive image frames and calculate the disparity in pixels between frames to construct a 3D map of the road. The processing unit 110 may then use the 3D map to detect the road surface and the hazards present on the road surface.

[0105] In step 530, the processing unit 110 may execute the navigation response module 408 to respond based on the analysis performed in step 520 and the above combination. Figure 4 The described techniques cause one or more navigation responses. Navigation responses may include, for example, steering, lane changes, acceleration changes, etc. In some embodiments, processing unit 110 may use data obtained from the execution of velocity and acceleration module 406 to cause one or more navigation responses. In addition, multiple navigation responses may occur simultaneously, sequentially, or in any combination thereof. For example, processing unit 110 may cause vehicle 200 to change lanes and then accelerate by, for example, sequentially sending control signals to steering system 240 and throttle system 220 of vehicle 200. Alternatively, processing unit 110 may cause vehicle 200 to brake and change lanes simultaneously by, for example, simultaneously sending control signals to braking system 230 and steering system 240 of vehicle 200.

[0106] Figure 5B is a flow chart illustrating an example process 500B for detecting one or more vehicles and / or pedestrians in a set of images according to the disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to implement process 500B. In step 540, processing unit 110 may determine a set of candidate objects representing possible vehicles and / or pedestrians. For example, processing unit 110 may scan one or more images, compare the images to one or more predetermined patterns, and identify possible locations within each image that may contain objects of interest (e.g., vehicles, pedestrians, or parts thereof). The predetermined patterns may be designed in such a way as to achieve a high "false hit" rate and a low "miss" rate. For example, processing unit 110 may use a low threshold similar to the predetermined pattern to identify a candidate object as a possible vehicle or pedestrian. Doing so may allow processing unit 110 to reduce the likelihood of missing (e.g., failing to identify) a candidate object representing a vehicle or pedestrian.

[0107] In step 542, processing unit 110 may filter the set of candidate objects based on classification criteria to exclude certain candidates (e.g., irrelevant or less relevant objects). Such criteria may be derived from various attributes associated with object types stored in a database (e.g., a database stored in memory 140). Attributes may include object shape, size, texture, location (e.g., relative to vehicle 200), etc. Thus, processing unit 110 may use one or more sets of criteria to reject false candidates from the set of candidate objects.

[0108] In step 544, processing unit 110 may analyze multiple frames of imagery to determine whether an object in the set of candidate objects represents a vehicle and / or a pedestrian. For example, processing unit 110 may track detected candidate objects across consecutive frames and accumulate frame-by-frame data associated with the detected objects (e.g., size, position relative to vehicle 200, etc.). In addition, processing unit 110 may estimate parameters of the detected object and compare the frame-by-frame position data of the object with the predicted position.

[0109] In step 546, processing unit 110 may construct a set of measurements for the detected objects. Such measurements may include, for example, position, velocity, and acceleration values ​​associated with the detected objects (relative to vehicle 200). In some embodiments, processing unit 110 may construct the measurements based on an estimation technique using a series of time-based observations (such as a Kalman filter or linear quadratic estimation (LQE)), and / or based on modeling data available for different object types (e.g., cars, trucks, pedestrians, bicycles, road signs, etc.). The Kalman filter may be based on a measure of the scale of the object, where the scale measure is proportional to the time to collision (e.g., the amount of time it takes for vehicle 200 to reach the object). Thus, by executing steps 540-546, processing unit 110 may identify vehicles and pedestrians that appear within the set of captured images and obtain information associated with the vehicles and pedestrians (e.g., position, velocity, size). Based on this identification and the obtained information, processing unit 110 may cause one or more navigation responses in vehicle 200, as described above in conjunction with Figure 5A described.

[0110] In step 548, the processing unit 110 may perform an optical flow analysis on the one or more images to reduce the likelihood of detecting "false hits" and missing candidate objects representing vehicles or pedestrians. Optical flow analysis may refer to analyzing motion patterns relative to the vehicle 200, for example, in one or more images associated with other vehicles and pedestrians, and which is distinguished from road motion. The processing unit 110 may calculate the motion of a candidate object by observing the different positions of the object across multiple image frames captured at different times. The processing unit 110 may use the position and time values ​​as input to a mathematical model for calculating the motion of the candidate object. Thus, optical flow analysis may provide another method for detecting vehicles and pedestrians near the vehicle 200. The processing unit 110 may perform the optical flow analysis in conjunction with steps 540-546 to provide redundancy in detecting vehicles and pedestrians and improve the reliability of the system 100.

[0111] Figure 5C is a flow chart illustrating an example process 500C for detecting road markings and / or lane geometry in a set of images according to the disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to implement process 500C. In step 550, processing unit 110 may detect a set of objects by scanning one or more images. In order to detect segments of lane markings, lane geometry, and other relevant road markings, processing unit 110 may filter the set of objects to exclude those determined to be irrelevant (e.g., small potholes, small rocks, etc.). In step 552, processing unit 110 may group together the segments detected in step 550 that belong to the same road marking or lane marking. Based on the grouping, processing unit 110 may generate a model, such as a mathematical model, representing the detected segments.

[0112] In step 554, the processing unit 110 may construct a set of measurements associated with the detected segment. In some embodiments, the processing unit 110 may create a projection of the detected segment from the image plane onto a real-world plane. The projection may be represented using a cubic polynomial having coefficients corresponding to physical properties such as the position, slope, curvature, and curvature derivatives of the detected road. In generating the projection, the processing unit 110 may take into account variations in the road surface, as well as the pitch and roll rates associated with the vehicle 200. In addition, the processing unit 110 may model the road elevation by analyzing positional and motion cues that appear on the road surface. Furthermore, the processing unit 110 may estimate the pitch and roll rates associated with the vehicle 200 by tracking a set of feature points in one or more images.

[0113] In step 556, processing unit 110 may perform a multi-frame analysis by, for example, tracking the detected segments across consecutive image frames and accumulating frame-by-frame data associated with the detected segments. As processing unit 110 performs multi-frame analysis, the set of measurements constructed in step 554 may become more reliable and associated with increasingly higher confidence levels. Thus, by executing steps 550-556, processing unit 110 may identify road markings present in the set of captured images and derive lane geometry information. Based on this identification and the derived information, processing unit 110 may cause one or more navigation responses in vehicle 200, as described above in conjunction with Figure 5A described.

[0114] In step 558, processing unit 110 may consider additional information sources to further generate a safety model of vehicle 200 in the context of its surrounding environment. Processing unit 110 may use this safety model to define scenarios in which system 100 can safely perform autonomous control of vehicle 200. To generate this safety model, in some embodiments, processing unit 110 may consider the position and motion of other vehicles, detected road edges and barriers, and / or a general road shape description extracted from map data (such as data from map database 160). By considering additional information sources, processing unit 110 can provide redundancy for detecting road markings and lane geometry and increase the reliability of system 100.

[0115] Figure 5D is a flow chart illustrating an example process 500D for detecting traffic lights in a set of images according to the disclosed embodiments. Processing unit 110 may execute monocular image analysis module 402 to implement process 500D. In step 560, processing unit 110 may scan the set of images and identify objects appearing in the images at locations that may contain traffic lights. For example, processing unit 110 may filter the identified objects to construct a set of candidate objects, excluding those that are unlikely to correspond to traffic lights. Filtering may be performed based on various attributes associated with traffic lights, such as shape, size, texture, and position (e.g., relative to vehicle 200). These attributes may be based on multiple examples of traffic lights and traffic control signals and stored in a database. In some embodiments, processing unit 110 may perform multi-frame analysis on the set of candidate objects that reflect possible traffic lights. For example, processing unit 110 may track candidate objects across consecutive image frames, estimate the real-world positions of the candidate objects, and filter out moving objects (which are unlikely to be traffic lights). In some embodiments, processing unit 110 may perform color analysis on the candidate objects and identify the relative positions of the detected colors within the possible traffic lights.

[0116] In step 562, processing unit 110 may analyze the geometry of the intersection. This analysis may be based on any combination of: (i) the number of lanes detected on either side of vehicle 200, (ii) detected markings on the road (e.g., arrows), and (iii) a description of the intersection extracted from map data (e.g., data from map database 160). Processing unit 110 may use information derived from execution of monocular analysis module 402 for this analysis. Furthermore, processing unit 110 may determine the correspondence between the traffic lights detected in step 560 and the lanes present near vehicle 200.

[0117] In step 564, as the vehicle 200 approaches the intersection, the processing unit 110 may update the confidence level associated with the analyzed intersection geometry and the detected traffic lights. For example, the number of traffic lights estimated to be present at the intersection compared to the number of traffic lights actually present at the intersection may affect the confidence level. Therefore, based on the confidence level, the processing unit 110 may delegate control to the driver of the vehicle 200 in order to improve safety conditions. By executing steps 560-564, the processing unit 110 may identify the traffic lights that appear within the set of captured images and analyze the intersection geometry information. Based on this identification and analysis, the processing unit 110 may cause one or more navigation responses in the vehicle 200, as described above in conjunction with Figure 5A described.

[0118] Figure 5E FIG2 is a flow chart illustrating an example process 500E for inducing one or more navigation responses in a vehicle based on a vehicle path according to the disclosed embodiments. In step 570, the processing unit 110 may construct an initial vehicle path associated with the vehicle 200. The vehicle path may be represented by a set of points expressed in coordinates (x, z), and the distance d between two points in the set of points may be i May fall within the range of 1 to 5 meters. In one embodiment, the processing unit 110 may construct an initial vehicle path using two polynomials, such as left and right road polynomials. The processing unit 110 may calculate the geometric midpoint between the two polynomials and offset each point included in the resulting vehicle path by a predetermined offset (e.g., a smart lane offset), if any (a zero offset may correspond to driving in the middle of the lane). The offset may be in a direction perpendicular to the line segment between any two points in the vehicle path. In another embodiment, the processing unit 110 may use a polynomial and an estimated lane width to offset each point of the vehicle path by half the estimated lane width plus a predetermined offset (e.g., a smart lane offset).

[0119] In step 572, the processing unit 110 may update the vehicle path constructed in step 570. The processing unit 110 may reconstruct the vehicle path constructed in step 570 using a higher resolution so that the distance d between two points in the set of points representing the vehicle path is k Smaller than the above distance d i For example, the distance d k The processing unit 110 may reconstruct the vehicle path using a parabolic spline algorithm, which may produce a cumulative distance vector S corresponding to the total length of the vehicle path (ie, based on the set of points representing the vehicle path).

[0120] In step 574, the processing unit 110 may determine a look-ahead point (expressed in coordinates (x i ,z i )). The processing unit 110 can extract a look-ahead point from the accumulated distance vector S, and the look-ahead point can be associated with a look-ahead distance and a look-ahead time. The look-ahead distance, which can have a lower limit ranging from 10 meters to 20 meters, can be calculated as the product of the speed of the vehicle 200 and the look-ahead time. For example, as the speed of the vehicle 200 decreases, the look-ahead distance can also decrease (e.g., until it reaches the lower limit). The look-ahead time, which can range from 0.5 to 1.5 seconds, can be inversely proportional to the gain of one or more control loops associated with causing a navigation response in the vehicle 200, such as a heading error tracking control loop. For example, the gain of the heading error tracking control loop can depend on the bandwidth of the yaw rate loop, the steering actuator loop, the vehicle lateral dynamics, etc. Therefore, the higher the gain of the heading error tracking control loop, the lower the look-ahead time.

[0121] In step 576, the processing unit 110 may determine the heading error and the yaw rate command based on the look-ahead point determined in step 574. The processing unit 110 may calculate the arc tangent of the look-ahead point, such as arctan(x i / z i ) to determine the heading error. Processing unit 110 may determine the yaw rate command as the product of the heading error and the high-level control gain. If the look-ahead distance is not at the lower limit, the high-level control gain may be equal to: (2 / look-ahead time). Otherwise, the high-level control gain may be equal to: (2×vehicle 200 speed / look-ahead distance).

[0122] Figure 5FFIG2 is a flow chart illustrating an example process 500F for determining whether a leading vehicle is changing lanes according to the disclosed embodiments. In step 580, the processing unit 110 may determine navigation information associated with a leading vehicle (e.g., a vehicle traveling in front of the vehicle 200). For example, the processing unit 110 may use the above combined Figure 5A and Figure 5B The described techniques can be used to determine the position, velocity (e.g., direction and speed), and / or acceleration of the vehicle ahead. The processing unit 110 can also use the above combined Figure 5E The described techniques determine one or more road polynomials, look-ahead points (associated with vehicle 200 ), and / or snail trails (eg, a set of points describing a path taken by a preceding vehicle).

[0123] In step 582, processing unit 110 may analyze the navigation information determined in step 580. In one embodiment, processing unit 110 may calculate the distance between the tracking trajectory and the road polynomial (e.g., along the trajectory). If the change in this distance along the trajectory exceeds a predetermined threshold (e.g., 0.1 to 0.2 meters on a straight road, 0.3 to 0.4 meters on a moderately curving road, and 0.5 to 0.6 meters on a road with a sharp curve), processing unit 110 may determine that the leading vehicle is likely changing lanes. In the event that multiple vehicles are detected traveling ahead of vehicle 200, processing unit 110 may compare the tracking trajectory associated with each vehicle. Based on this comparison, processing unit 110 may determine that a vehicle whose tracking trajectory does not match the tracking trajectory of the other vehicles is likely changing lanes. Processing unit 110 may additionally compare the curvature of the tracking trajectory (associated with the leading vehicle) with the expected curvature of the road segment in which the leading vehicle is traveling. The expected curvature may be extracted from map data (e.g., data from map database 160), from road polynomials, from tracked trajectories of other vehicles, from prior knowledge about the road, etc. If the difference between the curvature of the tracked trajectory and the expected curvature of the road segment exceeds a predetermined threshold, processing unit 110 may determine that the leading vehicle is likely changing lanes.

[0124] In another embodiment, the processing unit 110 may compare the instantaneous position of the preceding vehicle with a look-ahead point (associated with the vehicle 200) over a specific time period (e.g., 0.5 to 1.5 seconds). If the distance between the instantaneous position of the preceding vehicle and the look-ahead point changes during the specific time period, and the cumulative sum of the changes exceeds a predetermined threshold (e.g., 0.3 to 0.4 meters on a straight road, 0.7 to 0.8 meters on a moderately curved road, and 1.3 to 1.7 meters on a road with a sharp curve), the processing unit 110 may determine that the preceding vehicle is likely changing lanes. In another embodiment, the processing unit 110 may analyze the geometry of the tracking trajectory by comparing the lateral distance traveled along the tracking trajectory with the expected curvature of the tracking path. The expected radius of curvature may be determined according to the formula: (δ z 2 +δ x 2 ) / 2 / (δ x ), where δ x represents the lateral distance traveled, and δ z The longitudinal distance traveled is represented by the curvature. If the difference between the lateral distance traveled and the expected curvature exceeds a predetermined threshold (e.g., 500 to 700 meters), the processing unit 110 can determine that the leading vehicle is likely to be changing lanes. In another embodiment, the processing unit 110 can analyze the position of the leading vehicle. If the position of the leading vehicle obscures the road polynomial (e.g., the leading vehicle is overlaid on top of the road polynomial), the processing unit 110 can determine that the leading vehicle is likely to be changing lanes. In the event that the position of the leading vehicle is such that another vehicle is detected in front of the leading vehicle and the tracking trajectories of the two vehicles are not parallel, the processing unit 110 can determine that the (closer) leading vehicle is likely to be changing lanes.

[0125] In step 584, processing unit 110 may determine whether the leading vehicle 200 is changing lanes based on the analysis performed in step 582. For example, processing unit 110 may make this determination based on a weighted average of the individual analyses performed in step 582. In this approach, for example, a determination by processing unit 110 that the leading vehicle is likely changing lanes based on a particular type of analysis may be assigned a value of "1" (and a "0" to indicate a determination that the leading vehicle is unlikely to be changing lanes). Different analyses performed in step 582 may be assigned different weights, and the disclosed embodiments are not limited to any particular combination of analyses and weights.

[0126] Figure 6is a flow chart illustrating an example process 600 for eliciting one or more navigation responses based on stereo image analysis according to the disclosed embodiments. In step 610, the processing unit 110 may receive the first and second pluralities of images via the data interface 128. For example, a camera included in the image acquisition unit 120 (such as the image capture devices 122 and 124 having fields of view 202 and 204) may capture the first and second pluralities of images of the area in front of the vehicle 200 and transmit them to the processing unit 110 via a digital connection (e.g., USB, wireless, Bluetooth, etc.). In some embodiments, the processing unit 110 may receive the first and second pluralities of images via two or more data interfaces. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0127] In step 620, the processing unit 110 may execute the stereo image analysis module 404 to perform stereo image analysis on the first and second plurality of images to create a 3D map of the road ahead of the vehicle and detect features within the image, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, etc. The stereo image analysis may be performed in a manner similar to the above combined Figures 5A-5D The processing unit 110 may execute the stereo image analysis module 404 to detect candidate objects (e.g., vehicles, pedestrians, road signs, traffic lights, road hazards, etc.) within the first and second pluralities of images, filter out a subset of the candidate objects based on various criteria, and perform multi-frame analysis, construct measurements, and determine confidence levels for the remaining candidate objects. In performing the above steps, the processing unit 110 may consider information from both the first and second pluralities of images, rather than from a single set of images. For example, the processing unit 110 may analyze differences in pixel-level data (or other subsets of data from the two streams of captured images) for candidate objects appearing in both the first and second pluralities of images. As another example, the processing unit 110 may estimate the position and / or velocity of a candidate object (e.g., relative to the vehicle 200) by observing that the object appears in one of the multiple images but not in the other, or other differences that may exist with respect to the objects appearing in the two image streams. For example, the position, velocity, and / or acceleration relative to the vehicle 200 may be determined based on features such as trajectory, position, movement characteristics, etc. associated with the object appearing in one or both image streams.

[0128] In step 630, the processing unit 110 may execute the navigation response module 408 to respond based on the analysis performed in step 620 and the above combination. Figure 4The described techniques may be used to cause one or more navigation responses in the vehicle 200. The navigation responses may include, for example, steering, lane changes, changes in acceleration, changes in speed, braking, etc. In some embodiments, the processing unit 110 may use data obtained from the execution of the speed and acceleration module 406 to cause the one or more navigation responses. Furthermore, multiple navigation responses may occur simultaneously, sequentially, or any combination thereof.

[0129] Figure 7 is a flow chart illustrating an example process 700 for eliciting one or more navigation responses based on analysis of three sets of images, according to the disclosed embodiments. In step 710, processing unit 110 may receive first, second, and third pluralities of images via data interface 128. For example, cameras included in image acquisition unit 120 (such as image capture devices 122, 124, and 126 having fields of view 202, 204, and 206) may capture first, second, and third pluralities of images of areas in front of and / or to the sides of vehicle 200 and transmit them to processing unit 110 via a digital connection (e.g., USB, wireless, Bluetooth, etc.). In some embodiments, processing unit 110 may receive the first, second, and third pluralities of images via three or more data interfaces. For example, each of image capture devices 122, 124, 126 may have an associated data interface for transmitting data to processing unit 110. The disclosed embodiments are not limited to any particular data interface configuration or protocol.

[0130] In step 720, the processing unit 110 may analyze the first, second, and third plurality of images to detect features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, etc. This analysis may be performed similarly to the above combined Figures 5A-5D and Figure 6 For example, the processing unit 110 may perform a monocular image analysis on each of the first, second, and third plurality of images (e.g., via execution of the monocular image analysis module 402 and based on the above combination). Figures 5A-5D Alternatively, the processing unit 110 may perform stereoscopic image analysis on the first and second pluralities of images, the second and third pluralities of images, and / or the first and third pluralities of images (e.g., via execution of the stereoscopic image analysis module 404 and based on the above combination). Figure 64 and 3 pluralities of images). The processed information corresponding to the analysis of the first, second, and / or third pluralities of images may be combined. In some embodiments, the processing unit 110 may perform a combination of monocular and stereoscopic image analysis. For example, the processing unit 110 may perform monocular image analysis on the first pluralities of images (e.g., via execution by the monocular image analysis module 402) and perform stereoscopic image analysis on the second and third pluralities of images (e.g., via execution by the stereoscopic image analysis module 404). The configuration of the image capture devices 122, 124, and 126—including their respective positions and fields of view 202, 204, and 206—may affect the type of analysis performed on the first, second, and third pluralities of images. The disclosed embodiments are not limited to a particular configuration of the image capture devices 122, 124, and 126 or the type of analysis performed on the first, second, and third pluralities of images.

[0131] In some embodiments, processing unit 110 may perform testing on system 100 based on the images acquired and analyzed in steps 710 and 720. Such testing may provide an indicator of the overall performance of system 100 for certain configurations of image acquisition devices 122, 124, and 126. For example, processing unit 110 may determine the proportion of "false hits" (e.g., instances in which system 100 incorrectly determines the presence of a vehicle or pedestrian) and "misses."

[0132] In step 730, the processing unit 110 may cause one or more navigation responses in the vehicle 200 based on information obtained from two of the first, second, and third pluralities of images. The selection of two of the first, second, and third pluralities of images may depend on various factors, such as the number, type, and size of objects detected in each of the plurality of images. The processing unit 110 may also make the selection based on image quality and resolution, the effective field of view reflected in the image, the number of frames captured, the degree to which one or more objects of interest actually appear in the frames (e.g., the percentage of frames in which the object appears, the proportion of the object appearing in each such frame), etc.

[0133] In some embodiments, processing unit 110 may select information derived from two of the first, second, and third pluralities of images by determining how consistent the information derived from one image source is with information derived from other image sources. For example, processing unit 110 may combine processed information derived from each of image capture devices 122, 124, and 126 (whether through monocular analysis, stereoscopic analysis, or any combination thereof) and determine visual indicators (e.g., lane markings, detected vehicles and their positions and / or paths, detected traffic lights, etc.) that are consistent across each captured image from image capture devices 122, 124, and 126. Processing unit 110 may also exclude information that is inconsistent across the captured images (e.g., vehicles changing lanes, lane models indicating that a vehicle is too close to vehicle 200, etc.). Thus, processing unit 110 may select information derived from two of the first, second, and third pluralities of images based on the determination of consistent and inconsistent information.

[0134] The navigation response may include, for example, steering, lane change, braking, acceleration change, etc. The processing unit 110 may perform the following actions based on the analysis performed in step 720 and the above combination. Figure 4 The described techniques cause one or more navigation responses. Processing unit 110 may also cause one or more navigation responses using data derived from execution of velocity and acceleration module 406. In some embodiments, processing unit 110 may cause one or more navigation responses based on the relative position, relative velocity, and / or relative acceleration between vehicle 200 and an object detected within any of the first, second, and third pluralities of images. Multiple navigation responses may occur simultaneously, sequentially, or any combination thereof.

[0135] Curb detection and pedestrian hazard assessment

[0136] As described herein, system 100 may include technical features and capabilities that allow for the detection of certain elements, which help system 100 stay on the road and avoid collisions. In one example, system 100 is configured to detect road boundaries defining a road and / or pedestrians near the road. Knowledge of the physical road boundaries allows system 100 to warn the driver or control the vehicle in a manner that avoids veer-off-the-road and / or collisions with objects or pedestrians. For example, identifying road boundaries helps provide physical protection for pedestrians on sidewalks near the road by demarcating areas where pedestrians may be present.

[0137] In an exemplary embodiment, the system 100 can be configured to detect curbs as road boundaries. In most cases, curbs are physical boundaries that separate road surface from non-road surface (e.g., sidewalks, paths, medians, buildings, etc.). Preventing vehicles from crossing curbs helps keep vehicles on the road and pedestrians on the sidewalks safe. However, pedestrians located on the road surface are at a higher risk of being struck by moving vehicles. Therefore, the ability to classify pedestrians as on the curb and off the curb, as well as the estimation of vehicle and pedestrian trajectories, provides the system 100 with the ability to assess the danger posed by vehicles to pedestrians. The disclosed embodiments include curb detection and pedestrian hazard assessment features to provide these and other functions to the system 100.

[0138] To implement curb detection and pedestrian hazard assessment functionality, the processing unit 110 may receive images captured by at least one of the image capture devices 122, 124, and 126 and perform one or more image analysis processes. For example, the processing unit 110 may perform a curb recognition process to identify curbs in the captured images and a pedestrian recognition process to determine the position of pedestrians relative to the road surface.

[0139] In some embodiments, the memory 140 and / or 150 may store instructions programmed to provide curb detection and / or pedestrian hazard assessment functionality when executed by the processing device. Figure 8 As shown, memory 140 and / or 150 may store a curb recognition module 810, a curb registration module 820, a pedestrian recognition module 830, and a database 840. Curb recognition module 810 may store instructions for identifying elements of an image corresponding to a curb. Curb registration module 820 may store instructions for modeling curbs and / or determining attributes of each identified curb. Pedestrian recognition module 830 may store instructions for detecting and classifying pedestrians in one or more captured images. Database 840 may be configured to store data associated with curb detection and / or pedestrian hazard assessment functions. In addition, curb recognition module 810, curb registration module 820, and pedestrian recognition module 830 may store instructions that can be executed by one or more processors (e.g., processing unit 110) individually or in various combinations with each other. For example, the curb recognition module 810 , the curb registration module 820 , and the pedestrian recognition module 830 can be configured to interact with each other and / or with other modules of the system 100 to perform functions in accordance with the disclosed embodiments.

[0140] Database 840 may include one or more memory devices that store information and are accessed and / or managed by a computing device such as processing unit 110. In some embodiments, database 840 may be located in memory 140 or 150, such as Figure 8150 and 150. In other embodiments, database 840 can be remotely located from memory 140 or 150 and can be accessed by other components of system 100 (e.g., processing unit 120) via one or more wireless connections (e.g., a wireless network). Although one database 840 is shown, it should be understood that several independent and / or interconnected databases can comprise database 840. Database 840 can include a computing component (e.g., a database management system, a database server, etc.) configured to receive and process requests for data stored in a memory device associated with database 840 and provide data from database 840 (e.g., to processing unit 110).

[0141] In some embodiments, database 840 may be configured to store data associated with providing curb detection and / or pedestrian hazard assessment functionality. For example, database 840 may store data, such as images, portions of images, maps, algorithms, sets of values, etc., that may allow processing unit 110 to identify information detected in an image. For example, database 840 may store information that allows system 100 to identify curbs and / or pedestrians, such as stored images of curbs and / or images of pedestrians.

[0142] Figure 9 An exemplary image 900 captured by one of the image capture devices 122, 124, 126 is depicted. Image 900 includes an environment that may generally represent a setting visible when a vehicle is traveling on a road. For example, image 900 includes a road 910 defined between opposing curbs 920, and a plurality of pedestrians 930. In an exemplary embodiment, system 100 may be configured to recognize and / or distinguish various features of image 900. Knowledge of these features allows system 100 to make decisions that help facilitate safe operation of the vehicle. For example, recognition of curbs 920 provides information that helps keep the vehicle within the physical boundaries of road 910 and away from pedestrians 930 on the sidewalk. Furthermore, recognition of curbs 920 allows system 100 to recognize pedestrians on the surface of road 910, and therefore identify potential pedestrian hazards, which may require further action to avoid a collision.

[0143] The entirety of image 900 may represent the field of view of one or more image capture devices 122, 124, 126. In some embodiments, system 100 may be configured to identify a region of interest (ROI) 950 within the FOV. In an exemplary embodiment, system 100 may be a detection system configured to process a selected ROI 950 and / or the entire field of view associated with image 900 (and / or multiple images of which image 900 is one) to identify curb 920.

[0144] In one embodiment, the system 100 (e.g., the curb recognition module 810) can be configured to detect linear image edges as curb edge line candidates. The curb recognition module 810 can analyze the image to identify one or more edge lines of the curb based on the identified curb edge line candidates. For example, the curb recognition module 810 can identify the bottom edge of the curb by analyzing the edge line candidates to determine which, if any, includes features that match features of the bottom edge of the curb, which can be the transition line between the curb and the road surface in the image.

[0145] In some embodiments, the system 100 (e.g., the curb registration module 820) can be configured to determine whether a curb edge line candidate is part of a curb (e.g., the candidate will be correctly identified as an edge line of a curb) by determining whether the motion field of pixels near the curb edge line candidate is consistent with a three-dimensional structure having a step of appropriate height. For example, the system 100 can model the curb as a sharp step function and use multiple captured images (e.g., previous and current images) to calculate what properties of the step will best model the motion field of pixels between captured image frames. Figure 9 An example of an identified curb 920A is shown near a nearest pedestrian 930A.

[0146] In an exemplary embodiment, the curb recognition module 810 may identify curb edge line candidates using image analysis methods. For example, the system 100 may use a method utilizing the Hough transform, which is an image processing technique that can be used to identify lines in an image. In another example, the system 100 may use the Canny edge detection technique, which utilizes image gradient analysis to identify edges present in an image. Other image analysis methods may be used, such as methods that group image pixels by intensity, contrast, or other parameters to identify lines and / or curves present in an image.

[0147] Curb edges, including curb bottom edges, typically appear as elongated linear edges in the intensity pattern of an image, thereby allowing their identification using one of the image analysis methods described above. However, this is also true for non-curb objects and road markings that contain long linear features that appear in an image. Thus, elongated linear edges found in an image are not necessarily curb edges. Curb registration module 820 can perform a curb detection process to analyze curb edge line candidates to identify curb edge lines (and therefore, curbs).

[0148] Figure 10AAn exemplary image 1000 is depicted, including linear edges 1010 identified by the curb identification module 810 as potential edges of a curb 1020. Some of the linear edges 1010 may correspond to edge lines of the curb 1020 (e.g., the top and / or bottom edges of the curb), while other linear edges 1010 may correspond to non-curb objects, such as cracks, seams, markings, etc. The curb registration module 820 may analyze the image 1000 and the linear edges 1010 to determine the location of the curb 1020 and / or model the curb 1020. Figure 10B An image 1000 is depicted with a curb 1020 having been identified and modeled in accordance with the disclosed embodiments.

[0149] The curb registration module 820 may be configured to store and allow for Figure 10B Instructions associated with one or more algorithms for identifying and modeling the curb 1020 are shown. For example, the curb registration module 820 may include a curb analysis algorithm (CAA) that allows the system 100 to identify curbs within multiple images in an image and / or video sequence. In an exemplary embodiment, the processing unit 110 may use the CAA to determine whether a curb edge line candidate corresponds to a curb edge line (e.g., a curb bottom edge or a curb top edge).

[0150] Figure 11 An example of a modeled step function 1100 that can be used to model a curb corresponding to a real curb 1105 is depicted, which includes at least some parameters that can be used by the system 100 when performing CAA. Figure 11 As shown, the system 110 can analyze the region of interest 1110 across multiple captured image frames to model the step function 1100 without considering the mask region 1120. This can allow the processing unit 110 to focus only on the pixels surrounding the curb edge line candidate. In an exemplary embodiment, to determine whether a curb edge line candidate corresponds to a curb edge line, the height of the step function 1100 (i.e., the height of the associated modeled curb) (hereinafter referred to as "Z curb ”). For example, Z can be calculated for a given curb edge line candidate detected in one image of a video sequence, a previous frame, and known, assumed, or estimated parameters (e.g., camera calibration and ego-motion parameters). curb .

[0151] In one example, the curb registration module 820 can utilize a parameterized image warping function and error minimization function E(p) to calculate Z for a given curb edge line candidate curb . Image warping function It can be a piecewise homography warp whereby each homography operates on a different portion of the region of interest 1110 to match pixels in the three-dimensional space between the captured frames. For example, the image warp function Can be defined as:

[0152]

[0153] like Figure 11 As shown, each homography matrix H1, H2, and H3 may correspond to a portion of the region of interest 1120. In an exemplary embodiment, H1 may represent an area inside the bottom edge of the curb 1105 (e.g., the road surface), H2 may represent an area between the bottom edge of the curb 1105 and the top edge of the curb 1105 (e.g., the height of the curb), and H3 may represent an area extending away from the top of the curb 1105 and the road surface (e.g., the sidewalk), all within the region of interest 1120. The system 100 may use an image warping function To follow such as the current image I a and the previous image I b pixels between the plurality of images to determine a step function associated with a curb edge line candidate.

[0154] In an exemplary embodiment, each homography matrix H1, H2, and H3 may be calculated based on a homography matrix determination formula, such as:

[0155]

[0156] In general, the parameters defining each homography matrix may be known, assumed, or estimated based on information associated with the vehicle 200 and the image capture devices 122, 124, 126. For example, K may be an image capture device parameter that may account for focal length, image center, and / or other camera settings, and may be defined as:

[0157]

[0158] The inter-frame ego-motion parameters can be obtained using:

[0159] t ab =(ΔX, ΔY, ΔZ) T

[0160] Δθ ab =(Δα, Δβ, Δγ) T

[0161] Rab=Rz(Δγ)Ry(Δβ)Rx(Δα) can be in frame I a Frame I in the camera coordinate systema and frame I a The camera rotation matrix between is composed of pitch (x), yaw (y), and roll (z) rotations (for pitch angle α, yaw angle β, and roll angle γ relative to the road plane).

[0162] About the surface normal n i :

[0163]

[0164] n2=-u×n1

[0165] In these formulas, u = (X2–X1) / ||(X2-X1)||, u is a unit vector in the direction of the bottom edge of the curb, and R calib =Rz(γ)Ry(β)Rx(α), R calib is the road calibration matrix.

[0166] About the distance d to the three planes i :

[0167] d1=Z cam

[0168]

[0169] d3=Z cam -Z curb

[0170] In the above formula, the image coordinates projected onto the road plane can be used and The world coordinates X1 and X2 correspond to and The image coordinates of the points on the upper half of the step function 1100 are and A line may be defined that separates regions H2 and H3 within the region of interest 1110. These points may thus be defined as:

[0171]

[0172] X 3,4 =X 1,2 +Z curb n1

[0173]

[0174] In these formulas, x can be used to represent the three-dimensional homogenous image coordinates of the corresponding two-dimensional image point x.

[0175] The above formula can be used to determine the image distortion function The homography matrices H1, H2 and H3 of the function. Due to the curb height Z of the step function 1100 curb is an unknown parameter, the image distortion function Iterative solution can be performed until the appropriate Z is determined curb . Appropriate Z curb may correspond to an image warping formula within a satisfactory error when warping the coordinates of the region of interest 1120 between captured image frames. This can be determined using the error minimization function E(p), where E(p) is defined as:

[0176]

[0177] The error minimization function E(p) can be used to minimize the difference between the warping of the current frame and the previous frame using known information, assumptions, and / or estimated information. For example, when using the error minimization function E(p), the system 100 can use the brightness constant assumption. The error minimization function E(p) allows one captured image frame to be compared to another captured image frame by warping the coordinates of one frame to the coordinates of the other frame. Minimizing the error of the warping allows the system 100 to provide Z curb A more accurate calculation of (e.g., since a low error value may indicate that the differences between the captured image frames are accurately accounted for).

[0178] For each curb edge line candidate, the system 100 may use an image warping function to identify the parameter vector p. Depending on whether the other parameters are known, assumed, or estimated (e.g., additional degrees of freedom can be built in to allow some parameters to be unknown), the parameter vector p includes at least Z curb Based on the parameter vector p, the system 100 can determine the curb height Z within the error threshold based on the error minimization function E(p) curb This Z curb The value may represent the value of the height of the step function modeled to a given curb edge line candidate. The system 100 may calculate the Z curb Value and expected Z curb The Z value is compared (for example, to the average height of curbs that can be expected to be found on the road) to determine the calculated Z curb Is the value within the threshold difference? If the calculated Z curb If the value is within a threshold difference, the system 100 may determine that the curb edge line candidate corresponds to a curb edge line.

[0179] The system 100 can repeat the process of using CAA (which can include the formulas described above) to determine whether one or more identified curb edge line candidates correspond to a curb edge line. The system 100 can model each curb edge line candidate as both a curb bottom edge and a curb top edge to determine whether the curb edge line candidate matches either edge of the curb. Based on these processes, the system 100 can determine the location of one or more curbs within an image or video sequence of images.

[0180] After identifying the curb, the pedestrian recognition module 830 may perform one or more processes to identify a pedestrian relative to the curb. For example, the pedestrian recognition module 830 may evaluate whether the identified pedestrian is a potential hazard. As the vehicle travels on the road, the system 100 may use information related to the curb and pedestrian to provide warnings to the driver and / or control the vehicle.

[0181] Figure 12 1 is a flow chart illustrating an example process 1200 for identifying curbs within an image and / or video sequence. In an exemplary embodiment, system 100 may perform process 1200 to determine the location of curbs, allowing system 100 to determine the boundaries of the road surface. Knowing the boundaries of the road surface allows system 100 to facilitate safe operation of the vehicle by helping to maintain the vehicle on the road surface and avoid colliding with objects and / or pedestrians. Additionally, knowing the boundaries of the road surface may allow system 100 to assess whether any pedestrians or other objects are on the road surface and, therefore, whether some action should be taken to prevent a collision in response to the hazard.

[0182] In step 1202, the processing unit 110 may receive a plurality of images of the vehicle environment. For example, one or more image capture devices 122, 124, 126 may acquire a plurality of images of the area in front of the vehicle 200. The area in front of the vehicle may include various features, such as Figure 9 Those depicted.

[0183] In step 1204, processing unit 110 may identify curb edge line candidates. For example, processing unit 110 (e.g., curb identification module 810) may use an algorithm or formula to identify linear edges within the captured image. These linear edges may represent curb edge candidates, which may be further processed to determine whether they correctly represent the edges of a curb present in the image or video sequence. In one example, processing unit 110 may use an edge detection method, such as using a Hough transform or fitting lines to long connected components of a Canny edge response, to identify curb edge line candidates.

[0184] In step 1206, the processing unit 110 can analyze each curb edge line candidate. For example, the processing unit 110 (e.g., the curb registration module 820) can use an algorithm or formula to iteratively process the curb edge line candidates to determine whether any of them correspond to a curb edge line. For example, the processing unit can use CAA to model each curb edge line candidate as a step function and determine whether the corresponding height of the step function corresponds to the expected height of the real curb.

[0185] In step 1208, the processing unit 110 may identify at least one of the curb edge line candidates as a curb edge line. For example, the processing unit 110 (e.g., the curb registration module 820) may select and store a curb edge line candidate determined to be a curb edge line, or otherwise identify the edge line candidate as a curb edge line. The curb edge line may be identified as a top curb edge or a bottom curb edge.

[0186] Furthermore, processing unit 110 can identify areas corresponding to road surface and non-road surface based on the identified curb edge line. For example, processing unit 110 can use the determined curb edge line to identify the curb throughout the entire image and / or video sequence, and in each image throughout the entire image and / or video sequence, can identify a first area as road surface and a second area as non-road surface. In this manner, processing unit 110 can identify the physical boundaries of the road on which vehicle 200 is traveling. In some embodiments, processing unit 110 can store location and / or coordinate information associated with the identified curb edge line or curb.

[0187] In step 1210, processing unit 110 may use the identified curb edge line or curb to control vehicle 200. For example, processing unit 110 may provide position and / or coordinate information to a vehicle control module, which makes decisions regarding the speed, direction, acceleration, etc. of vehicle 200. In some cases, processing unit 110 may use the modeled curb to assess whether a hazard is present that requires consideration. For example, processing unit 110 may use the modeled curb to assess pedestrian hazards, as described in more detail below.

[0188] Figure 13 1 is a flow chart illustrating an example process 1300 for assessing the hazard presented by a pedestrian or other object that may be located within a roadway. In an exemplary embodiment, the system 100 may perform the process 1300 to determine the location of one or more pedestrians within the field of view of the image capture devices 122, 124, and 126. For example, the system 100 may determine that the one or more pedestrians are located on the roadway surface (e.g., and therefore not on a non-road surface) and determine one or more actions that should be taken to prevent a collision with the pedestrians.

[0189] In step 1310, processing unit 110 may identify pedestrians within an image and / or image of a video sequence. For example, processing unit 110 (e.g., pedestrian identification module 830) may process one or more images to identify objects of interest within the images. Processing unit 110 may further analyze the objects of interest to determine whether they correspond to pedestrians (or other objects that may be considered pedestrians, such as animals, cyclists, etc.). In one example, the processing unit may compare the image with stored images to match features of the image with information known to correspond to pedestrians (e.g., a graphic representation of a person's shape). In another example, the processing unit may compare multiple images of a video sequence to identify objects that move in a manner consistent with pedestrians.

[0190] In step 1320, processing unit 110 may classify the pedestrian. For example, processing unit 110 (e.g., pedestrian identification module 830) may classify the pedestrian as either on-pavement or off-pavement. In one example, processing unit 110 may compare the position and / or trajectory of the identified pedestrian with the position of an identified and modeled curb (e.g., the curb modeled using process 1200). For example, processing unit 110 may determine whether the pedestrian is within an area determined to be on-pavement (e.g., inside the bottom edge of the curb) or within an area determined to be off-pavement (e.g., outside the top edge of the curb).

[0191] In step 1330, processing unit 110 may evaluate whether the pedestrian presents a hazard to vehicle 200. In one example, processing unit 110 may determine that the pedestrian on the road surface is a hazard. In another example, processing unit 110 may compare the trajectory of vehicle 200 with the trajectory of the identified pedestrian on the road surface to determine whether there is a possibility of a collision between the vehicle and the pedestrian.

[0192] In step 1340, processing unit 110 may warn the driver and / or control vehicle 200 to prevent a collision. In one example, processing unit 110 may warn the driver of a pedestrian on the road surface each time the pedestrian is classified as being on the road surface. In another example, the driver is warned only when processing unit 110 determines that a collision with the pedestrian is possible (e.g., above a risk threshold, such as based on the predicted closest distance between vehicle 200 and the pedestrian at a certain point in time). In some embodiments, processing unit 110 may use the assessment of pedestrian hazard to automatically control the driving of vehicle 200. For example, processing unit 110 may modify the path (e.g., steering direction), speed, acceleration, etc. of vehicle 200 to avoid a collision with a pedestrian.

[0193] System 100 may use processes 1200 and / or 1300 to identify features of the environment of vehicle 200 and use these features to operate / control vehicle 200. In particular, as discussed in the example above, system 100 may identify road boundaries and determine whether there are any pedestrian hazards based on the position of pedestrians relative to the road boundaries. However, it should be understood that these are examples and other similar processes may be implemented. For example, in addition to curbs, other road boundaries may be identified, such as guardrails, medians, shoulders, etc., and other hazards other than pedestrians, such as other vehicles, debris, road defects, etc., may be identified and assessed based on their positioning and / or location relative to the road boundaries.

[0194] The foregoing description has been presented for illustrative purposes. It is not exhaustive and is not limited to the precise forms of the disclosed embodiments. Modifications and adaptations will be apparent to those skilled in the art from consideration of the description and practice of the disclosed embodiments. Additionally, while aspects of the disclosed embodiments are described as being stored in a memory, those skilled in the art will appreciate that these aspects may also be stored on other types of computer-readable media, such as secondary storage devices, e.g., hard disks or CDROMs, or other forms of RAM or ROM, USB media, DVDs, Blu-rays, or other optical drive media.

[0195] Computer programs based on the written description and disclosed methods are within the skill of experienced developers. Various programs or program modules can be created using any technology known to those skilled in the art or can be designed in conjunction with existing software. For example, program segments or program modules can be designed in or through the .NET Framework, .NET Compact Framework (and related languages ​​such as Visual Basic, C, etc.), Java, C++, Objective-C, HTML, HTML / AJAX combinations, XML, or HTML containing Java applets.

[0196] In addition, although illustrative embodiments have been described herein, those skilled in the art will recognize the scope of any and all embodiments based on this disclosure with equivalent elements, modifications, omissions, combinations, adaptations and / or changes (e.g., throughout aspects of the various embodiments). The limitations in the claims are to be interpreted broadly based on the language employed in the claims and are not limited to the examples described in this specification or during the prosecution of this application. The examples are to be understood to be non-exclusive. In addition, the steps of the disclosed methods may be modified in any way, including by reordering steps and / or inserting or deleting steps. Therefore, it is intended that the description and examples be regarded as illustrative only, with the true scope and spirit being represented by the full scope of the following claims and their equivalents.

Claims

1. A detection system, comprising: At least one processing device programmed to: receiving, via a data interface, a plurality of images of the area output from at least one camera; determining a plurality of road edge line candidates from and within the plurality of images of the region; as well as Based on the road edge line candidates, identifying areas corresponding to road surfaces in the plurality of images and areas corresponding to non-road surfaces in the plurality of images, wherein the at least one processing device is further programmed to: In response to determining that a motion field of pixels near a target road edge line candidate among the plurality of road edge line candidates is consistent with a three-dimensional structure of steps having a threshold height, the target road edge line candidate is determined to be an edge line of a curb.

2. The detection system of claim 1 , wherein the at least one processing device is further programmed to: identifying a pedestrian in one or more of the plurality of images; assessing whether the pedestrian presents a hazard; and An action to be taken to avoid a collision with the pedestrian is determined based on the evaluation. 3 . The detection system of claim 2 , wherein evaluating whether the pedestrian presents a hazard comprises classifying the pedestrian based on the identified area and at least one of the location or trajectory of the pedestrian. 4 . The detection system of claim 3 , wherein classifying the pedestrian comprises classifying the pedestrian as being on a road surface or off a road surface. 5 . The detection system of claim 2 , wherein evaluating whether the pedestrian presents a hazard comprises comparing the pedestrian's trajectory to a vehicle's trajectory.

6. The detection system of claim 2, wherein the determined action to be taken to avoid the collision comprises providing a warning to a driver.

7. The detection system of claim 2, wherein the determined action to be taken to avoid the collision comprises modifying at least one of a speed, acceleration, or direction of the vehicle.

8. The detection system of claim 1 , wherein the at least one processing device is further programmed to: identifying an object in one or more of the plurality of images; assessing whether the subject presents a risk; and An action to be taken to avoid a collision with the object is determined based on the evaluation.

9. The detection system of claim 8, wherein evaluating whether the object presents a hazard comprises classifying the object based on the identified area and at least one of the location or trajectory of the object.

10. The detection system of claim 9, wherein classifying the object comprises classifying the object as being on-road or off-road.

11. A vehicle comprising: vehicle body; as well as At least one processing device programmed to: receiving, via a data interface, a plurality of images of the area output from at least one camera; determining a plurality of road edge line candidates from and within the plurality of images of the region; as well as Based on the road edge line candidates, identifying areas corresponding to road surfaces in the plurality of images and areas corresponding to non-road surfaces in the plurality of images, wherein the at least one processing device is further programmed to: In response to determining that a motion field of pixels near a target road edge line candidate among the plurality of road edge line candidates is consistent with a three-dimensional structure of steps having a threshold height, the target road edge line candidate is determined to be an edge line of a curb.

12. The vehicle of claim 11 , wherein the at least one processing device is further programmed to: identifying a pedestrian in one or more of the plurality of images; assessing whether the pedestrian presents a hazard; and An action to be taken to avoid a collision with the pedestrian is determined based on the evaluation.

13. The vehicle of claim 12, wherein assessing whether the pedestrian presents a hazard comprises classifying the pedestrian based on the identified area and at least one of the location or trajectory of the pedestrian.

14. The vehicle of claim 13, wherein classifying the pedestrian comprises classifying the pedestrian as on-road or off-road.

15. The vehicle of claim 12, wherein assessing whether the pedestrian presents a hazard comprises comparing the pedestrian's trajectory to a vehicle's trajectory.

16. The vehicle of claim 12, wherein the determined action to take to avoid the collision includes providing a warning to a driver.

17. The vehicle of claim 12, wherein the determined action to take to avoid the collision comprises modifying at least one of a speed, acceleration, or direction of the vehicle.

18. The vehicle of claim 11 , wherein the at least one processing device is further programmed to: identifying an object in one or more of the plurality of images; assessing whether the subject presents a risk; and An action to be taken to avoid a collision with the object is determined based on the evaluation.

19. The vehicle of claim 18, wherein assessing whether the object presents a hazard comprises classifying the object based on the identified area and at least one of the location or trajectory of the object, and classifying the object comprises classifying the object as on-road or off-road.

20. A method for risk assessment, the method comprising the following operations performed by one or more processors: receiving, via a data interface, a plurality of images of the area output from at least one camera; determining a plurality of road edge line candidates from and within the plurality of images of the region; as well as Based on the road edge line candidates, identifying areas corresponding to road surfaces in the plurality of images and areas corresponding to non-road surfaces in the plurality of images, The method further comprises the following operations performed by the one or more processors: In response to determining that a motion field of pixels near a target road edge line candidate among the plurality of road edge line candidates is consistent with a three-dimensional structure of steps having a threshold height, the target road edge line candidate is determined to be an edge line of a curb.

Images