Systems and methods for braking a vehicle based on a detected object

Systems and methods are provided for detecting an object and causing a vehicle to brake based on the detection. In one implementation, an object detecting and braking system for a vehicle includes at least one image capture device configured to acquire a plurality of images of an area including an object in front of the vehicle. The system includes at least one processing device programmed to perform a first image analysis to determine a first estimated time-to-collision of the vehicle with the object, and to perform a second image analysis to determine a second estimated time-to-collision of the vehicle with the object. The processing device is also programmed to calculate a difference between the first estimated time-to-collision and the second estimated time-to-collision, to determine that the difference does not exceed a predetermined threshold, and to cause the vehicle to brake based on the determination that the difference does not exceed the predetermined threshold.

BACKGROUND

I. Technical Field

The present disclosure relates generally to autonomous vehicle navigation and, more specifically, to systems and methods that use cameras to detect an object in front of the vehicle and that apply brakes based on the detected object.

II. Background Information

As technology continues to advance, the goal of a fully autonomous vehicle that is capable of navigating on roadways is on the horizon. Primarily, an autonomous vehicle may be able to identify its environment and navigate without input from a human operator. Autonomous vehicles may also take into account a variety of factors and make appropriate decisions based on those factors to safely and accurately reach an intended destination. For example, various objects—such as other vehicles and pedestrians—are encountered when a vehicle typically travels a roadway. Autonomous driving systems may recognize these objects in a vehicle's environment and take appropriate and timely action to avoid collisions. Additionally, autonomous driving systems may identify other indicators—such as traffic signals, traffic signs, and lane markings—that regulate vehicle movement (e.g., when the vehicle must stop and may go, a speed at which the vehicle must not exceed, where the vehicle must be positioned on the roadway, etc.). Autonomous driving systems may need to determine when a vehicle should change lanes, turn at intersections, change roadways, etc. As is evident from these examples, many factors may need to be addressed in order to provide an autonomous vehicle that is capable of navigating safely and accurately.

SUMMARY

Embodiments consistent with the present disclosure provide systems and methods for autonomous vehicle navigation. The disclosed embodiments may use cameras to provide autonomous vehicle navigation features. For example, consistent with the disclosed embodiments, the disclosed systems may include one, two, or more cameras that monitor the environment of a vehicle and cause a navigational response based on an analysis of images captured by one or more of the cameras.

Consistent with a disclosed embodiment, an object detecting and braking system for a vehicle is provided. The system may include at least one image capture device configured to acquire a plurality of images of an area including an object in front of the vehicle. The system may include a data interface. The system may include at least one processing device programmed to receive the plurality of images via the data interface, perform a first image analysis based on at least a first image and a second image to determine a first estimated time-to-collision of the vehicle with the object, and perform a second image analysis based on at least a third image and a fourth image to determine a second estimated time-to-collision of the vehicle with the object. The processing device may be programmed to calculate a difference between the first estimated time-to-collision and the second estimated time-to-collision, determine that the difference does not exceed a predetermined threshold, and cause the vehicle to brake based on the determination that the difference does not exceed the predetermined threshold.

Consistent with another disclosed embodiment, a vehicle is provided. The vehicle may include a body. The vehicle may include at least one image capture device configured to acquire a plurality of images of an area including an object in front of the vehicle. The system may include a data interface. The system may include at least one processing device programmed to receive the plurality of images via the data interface, perform a first image analysis based on at least a first image and a second image to determine a first estimated time-to-collision of the vehicle with the object, and perform a second image analysis based on at least a third image and a fourth image to determine a second estimated time-to-collision of the vehicle with the object. The processing device may be programmed to calculate a difference between the first estimated time-to-collision and the second estimated time-to-collision, determine that the difference does not exceed a predetermined threshold, and cause the vehicle to brake based on the determination that the difference does not exceed the predetermined threshold.

Consistent with yet another disclosed embodiment, a method for detecting an object and braking a vehicle is provided. The method may include acquiring, via at least one image capture device, a plurality of images of an area including an object in front of the vehicle. The method may include receiving, via a processing device, the plurality of images, performing, via the processing device, a first image analysis based on at least a first image and a second image to determine a first estimated time-to-collision of the vehicle with the object, and performing, via the processing device, a second image analysis based on at least a third image and a fourth image to determine a second estimated time-to-collision of the vehicle with the object. The method may include calculating, via the processing device, a difference between the first estimated time-to-collision and the second estimated time-to-collision, determining, via the processing device, that the difference does not exceed a predetermined threshold, and causing, via the processing device, the vehicle to brake based on the determination that the difference does not exceed the predetermined threshold.

Consistent with another disclosed embodiment, a method is provided for assessing an overall system failure rate associated with a braking decisioning system for a vehicle. The method includes determining, using a processing device, a first failure rate associated with a textural analysis based sub-system. The textural analysis based sub-system is configured to make a decision to brake based on a change in texture between at least two images of an area in front of a vehicle. The method further includes determining, using the processing device, a second failure rate associated with a structural analysis based sub-system. The structural analysis based sub-system is configured to make a decision to brake based on optical flow information derived from at least two images of the area in front of the vehicle. The method also includes determining, using the processing device, the overall system failure rate based on the first failure rate and the second failure rate.

Consistent with other disclosed embodiments, non-transitory computer-readable storage media may store program instructions, which are executed by at least one processing device and perform any of the methods described herein.

DETAILED DESCRIPTION

FIG. 1is a block diagram representation of a system100consistent with the exemplary disclosed embodiments. System100may include various components depending on the requirements of a particular implementation. In some embodiments, system100may include a processing unit110, an image acquisition unit120, a position sensor130, one or more memory units140,150, a map database160, and a user interface170. Processing unit110may include one or more processing devices. In some embodiments, processing unit110may include an applications processor180, an image processor190, or any other suitable processing device. Similarly, image acquisition unit120may include any number of image acquisition devices and components depending on the requirements of a particular application. In some embodiments, image acquisition unit120may include one or more image capture devices (e.g., cameras), such as image capture device122, image capture device124, and image capture device126. System100may also include a data interface128communicatively connecting processing device110to image acquisition device120. For example, data interface128may include any wired and/or wireless link or links for transmitting image data acquired by image accusation device120to processing unit110.

Both applications processor180and image processor190may include various types of processing devices. For example, either or both of applications processor180and image processor190may include a microprocessor, preprocessors (such as an image preprocessor), graphics processors, a central processing unit (CPU), support circuits, digital signal processors, integrated circuits, memory, or any other types of devices suitable for running applications and for image processing and analysis. In some embodiments, applications processor180and/or image processor190may include any type of single or multi-core processor, mobile device microcontroller, central processing unit, etc. Various processing devices may be used, including, for example, processors available from manufacturers such as Intel®, AMD®, etc. and may include various architectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, applications processor180and/or image processor190may include any of the EyeQ series of processor chips available from Mobileye®. These processor designs each 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 out capabilities. In one example, the EyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2® architecture consists of two floating point, hyper-thread 32-bit RISC CPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), three Vector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller, 128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bit Video output controllers, 16 channels DMA and several peripherals. The MIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the second MIPS34K CPU and the multi-channel DMA as well as the other peripherals. The five VCEs, three VMP® and the MIPS34K CPU can perform intensive vision computations required by multi-function bundle applications. In another example, the EyeQ3®, which is a third generation processor and is six times more powerful that the EyeQ2®, may be used in the disclosed embodiments.

Any of the processing devices disclosed herein may be configured to perform certain functions. Configuring a processing device, such as any of the described EyeQ processors or other controller or microprocessor, to perform certain functions may include programming of computer executable instructions and making those instructions available to the processing device for execution during operation of the processing device. In some embodiments, configuring a processing device may include programming the processing device directly with architectural instructions. In other embodiments, configuring a processing device may include storing executable instructions on a memory that is accessible to the processing device during operation. For example, the processing device may access the memory to obtain and execute the stored instructions during operation.

WhileFIG. 1depicts two separate processing devices included in processing unit110, more or fewer processing devices may be used. For example, in some embodiments, a single processing device may be used to accomplish the tasks of applications processor180and image processor190. In other embodiments, these tasks may be performed by more than two processing devices.

Processing unit110may comprise various types of devices. For example, processing unit110may include various devices, such as a controller, an image preprocessor, a central processing unit (CPU), support circuits, digital signal processors, integrated circuits, memory, or any other types of devices for image processing and analysis. The image preprocessor may include a video processor for capturing, digitizing and processing the imagery from the image sensors. The CPU may comprise any number of microcontrollers or microprocessors. The support circuits may be any number of circuits generally well known in the art, including cache, power supply, clock 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 databases and image processing software. The memory may comprise 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 instance, the memory may be separate from the processing unit110. In another instance, the memory may be integrated into the processing unit110.

Each memory140,150may include software instructions that when executed by a processor (e.g., applications processor180and/or image processor190), may control operation of various aspects of system100. 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 types of storage. In some embodiments, memory units140,150may be separate from the applications processor180and/or image processor190. In other embodiments, these memory units may be integrated into applications processor180and/or image processor190.

Position sensor130may include any type of device suitable for determining a location associated with at least one component of system100. In some embodiments, position sensor130may include a GPS receiver. Such receivers can determine a user position and velocity by processing signals broadcasted by global positioning system satellites. Position information from position sensor130may be made available to applications processor180and/or image processor190.

User interface170may include any device suitable for providing information to or for receiving inputs from one or more users of system100. In some embodiments, user interface170may include user input devices, including, for example, a touchscreen, microphone, keyboard, pointer devices, track wheels, cameras, knobs, buttons, etc. With such input devices, a user may be able to provide information inputs or commands to system100by typing instructions or information, providing voice commands, selecting menu options on a screen using buttons, pointers, or eye-tracking capabilities, or through any other suitable techniques for communicating information to system100.

User interface170may be equipped with one or more processing devices configured to provide and receive information to or from a user and process that information for use by, for example, applications processor180. In some embodiments, such processing devices may execute instructions for recognizing and tracking eye movements, receiving and interpreting voice commands, recognizing and interpreting touches and/or gestures made on a touchscreen, responding to keyboard entries or menu selections, etc. In some embodiments, user interface170may include a display, speaker, tactile device, and/or any other devices for providing output information to a user.

Map database160may include any type of database for storing map data useful to system100. In some embodiments, map database160may include data relating to the position, in a reference coordinate system, of various items, including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, etc. Map database160may store not only the locations of such items, but also descriptors relating to those items, including, for example, names associated with any of the stored features. In some embodiments, map database160may be physically located with other components of system100. Alternatively or additionally, map database160or a portion thereof may be located remotely with respect to other components of system100(e.g., processing unit110). In such embodiments, information from map database160may be downloaded over a wired or wireless data connection to a network (e.g., over a cellular network and/or the Internet, etc.).

Image capture devices122,124, and126may each include any type of device suitable for capturing at least one image from an environment. Moreover, 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. Image capture devices122,124, and126will be further described with reference toFIGS. 2B-2E, below.

System100, or various components thereof, may be incorporated into various different platforms. In some embodiments, system100may be included on a vehicle200, as shown inFIG. 2A. For example, vehicle200may be equipped with a processing unit110and any of the other components of system100, as described above relative toFIG. 1. While in some embodiments vehicle200may be equipped with only a single image capture device (e.g., camera), in other embodiments, such as those discussed in connection withFIGS. 2B-2E, multiple image capture devices may be used. For example, either of image capture devices122and124of vehicle200, as shown inFIG. 2A, may be part of an ADAS (Advanced Driver Assistance Systems) imaging set.

The image capture devices included on vehicle200as part of the image acquisition unit120may be positioned at any suitable location. In some embodiments, as shown inFIGS. 2A-2E, and 3A-3C, image capture device122may be located in the vicinity of the rearview mirror. This position may provide a line of sight similar to that of the driver of vehicle200, which may aid in determining what is and is not visible to the driver. Image capture device122may be positioned at any location near the rearview mirror, but placing image capture device122on the driver side of the mirror may further aid in obtaining images representative of the driver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit120may also be used. For example, image capture device124may be located on or in a bumper of vehicle200. Such a location may be especially suitable for image capture devices having a wide field of view. The line of sight of bumper located. Image capture devices can be different from that of the driver and, therefore, the bumper image capture device and driver may not always see the same objects. The image capture devices (e.g., image capture devices122,124, and126) may also be located in other locations. For example, the image capture devices may be located on or in one or both of the side mirrors of vehicle200, on the roof of vehicle200, on the hood of vehicle200, on the trunk of vehicle200, on the sides of vehicle200, mounted on, positioned behind, or positioned in front of any of the windows of vehicle200, and mounted in or near the figures on the front and/or back of vehicle200, etc.

In addition to image capture devices, vehicle200may include various other components of system100. For example, processing unit110may be included on vehicle200either integrated with or separate from an engine control unit (ECU) of the vehicle. Vehicle200may also be equipped with a position sensor130, such as a GPS receiver and may also include a map database160and memory units140and150.

FIG. 2Ais a diagrammatic side view representation of an exemplary vehicle imaging system consistent with the disclosed embodiments.FIG. 2Bis a diagrammatic top view illustration of the embodiment shown inFIG. 2A. As illustrated inFIG. 2B, the disclosed embodiments may include a vehicle200including in its body a system100with a first image capture device122positioned in the vicinity of the rearview mirror and/or near the driver of vehicle200, a second image capture device124positioned on or in a bumper region (e.g., one of bumper regions210) of vehicle200, and a processing unit110.

As illustrated inFIG. 2C, image capture devices122and124may both be positioned in the vicinity of the rearview mirror and/or near the driver of vehicle200. Additionally, while two image capture devices122and124are shown inFIGS. 2B and 2C, it should be understood that other embodiments may include more than two image capture devices. For example, in the embodiments shown inFIGS. 2D and 2E, first, second, and third image capture devices122,124, and126, are included in the system100of vehicle200.

As illustrated inFIG. 2D, image capture device122may be positioned in the vicinity of the rearview mirror and/or near the driver of vehicle200, and image capture devices124and126may be positioned on or in a bumper region (e.g., one of bumper regions210) of vehicle200. And as shown inFIG. 2E, image capture devices122,124, and126may be positioned in the vicinity of the rearview mirror and/or near the driver seat of vehicle200. The disclosed embodiments are not limited to any particular number and configuration of the image capture devices, and the image capture devices may be positioned in any appropriate location within and/or on vehicle200.

It is to be understood that the disclosed embodiments are not limited to vehicles and could be applied in other contexts. It is also to be understood that disclosed embodiments are not limited to a particular type of vehicle201) and may be applicable to all types of vehicles including automobiles, trucks, trailers, and other types of vehicles.

The first image capture device122may include any suitable type of image capture device. Image capture device122may include an optical axis. In one instance, the image capture device122may include an Aptina M9V024 WVGA sensor with a global shutter. In other embodiments, image capture device122may provide a resolution of 1280×960 pixels and may include a rolling shutter. Image capture device122may 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, image capture device122may be associated with a 6 mm lens or a 12 mm lens. In some embodiments, image capture device122may be configured to capture images having a desired field-of-view (FOV)202, as illustrated inFIG. 2D. For example, image capture device122may be configured to have a regular FOV, such as within a range of 40 degrees to 56 degrees, including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater. Alternatively, image capture device122may be configured to have a narrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or 36 degree FOV. In addition, image capture device122may be configured to have a wide FOV in the range of 100 to 180 degrees. In some embodiments, image capture device122may include a wide angle bumper camera or one with up to a 180 degree FOV.

The first image capture device122may acquire a plurality of first images relative to a scene associated with the vehicle200. Each of the plurality of first images may be acquired as a series of image scan lines, which may be captured using a rolling shutter. Each scan line may include a plurality of pixels.

The first image capture device122may have a scan rate associated with acquisition of each of the first series of image scan lines. The scan rate may refer to a rate at which an image sensor can acquire image data associated with each pixel included in a particular scan line.

Image capture devices122,124, and126may contain any suitable type and number of image sensors, including CCD sensors or CMOS sensors, for example. In one embodiment, a CMOS image sensor may be employed along with a rolling shutter, such that each pixel in a row is read one at a time, and scanning of the rows proceeds on a row-by-row basis until an entire image frame has been captured. In some embodiments, the rows may be captured sequentially from top to bottom relative to the frame.

The use of a rolling shutter may result in pixels in different rows being exposed and captured at different times, which may cause skew and other image artifacts in the captured image frame. On the other hand, when the image capture device122is configured to operate with a global or synchronous shutter, all of the 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 FOV202) at a particular time. In contrast, in a rolling shutter application, each row in a frame is exposed and data is capture at different times. Thus, moving objects may appear distorted in an image capture device having a rolling shutter. This phenomenon will be described in greater detail below.

The second image capture device124and the third image capturing device126may be any type of image capture device. Like the first image capture device122, each of image capture devices124and126may include an optical axis. In one embodiment, each of image capture devices124and126may include an Aptina M9V024 WVGA sensor with a global shutter. Alternatively, each of image capture devices124and126may include a rolling shutter. Like image capture device122, image capture devices124and126may be configured to include various lenses and optical elements. In some embodiments, lenses associated with image capture devices124and126may provide FOVs (such as FOVs204and206) that are the same as, or narrower than, a FOV (such as FOV202) associated with image capture device122. For example, image capture devices124and126may have FOVs of 40 degrees, 30 degrees, 26 degrees, 23 degrees, 20 degrees, or less.

Image capture devices124and126may acquire a plurality of second and third images relative to a scene associated with the vehicle200. Each of the plurality of second and third images may be acquired as a second and third series of image scan lines, which may be captured using a rolling shutter. Each scan line or row may have a plurality of pixels. Image capture devices124and126may have second and third scan rates associated with acquisition of each of image scan lines included in the second and third series.

Each image capture device122,124, and126may be positioned at any suitable position and orientation relative to vehicle200. The relative positioning of the image capture devices122,124, and126may be selected to aid in fusing together the information acquired from the image capture devices. For example, in some embodiments, a FOV (such as FOV204) associated with image capture device124may overlap partially or fully with a FOV (such as FOV202) associated with image capture device122and a FOV (such as FOV206) associated with image capture device126.

Image capture devices122,124, and126may be located on vehicle200at any suitable relative heights. In one instance, there may be a height difference between the image capture devices122,124, and126, which may provide sufficient parallax information to enable stereo analysis. For example, as shown inFIG. 2A, the two image capture devices122and124are at different heights. There may also be a lateral displacement difference between image capture devices122,124, and126, giving additional parallax information for stereo analysis by processing unit110, for example. The difference in the lateral displacement may be denoted by dx, as shown inFIGS. 2C and 2D. In some embodiments, fore or aft displacement (e.g., range displacement) may exist between image capture devices122,124, and126. For example, image capture device122may be located 0.5 to 2 meters or more behind image capture device124and/or image capture device126. This type of displacement may enable one of the image capture devices to cover potential blind spots of the other image capture device(s).

Image capture devices122may have any suitable resolution capability (e.g., number of pixels associated with the image sensor), and the resolution of the image sensor(s) associated with the image capture device122may be higher, lower, or the same as the resolution of the image sensor(s) associated with image capture devices124and126. In some embodiments, the image sensor(s) associated with image capture device122and/or image capture devices124and126may have a resolution of 640×480, 1024×768, 1280×960, or any other suitable resolution.

The frame rate (e.g., the rate at which an image capture device acquires a set of pixel data of one image frame before moving on to capture pixel data associated with the next image frame) may be controllable. The frame rate associated with image capture device122may be higher, lower, or the same as the frame rate associated with image capture devices124and126. The frame rate associated with image capture devices122,124, and126may depend on a variety of factors that may affect the timing of the frame rate. For example, one or more of image capture devices122,124, and126may include a selectable pixel delay period imposed before or after acquisition of image data associated with one or more pixels of an image sensor in image capture device122,124, and/or126. Generally, image data corresponding to each pixel may be acquired according to a clock rate for the device (e.g., one pixel per clock cycle). Additionally, in embodiments including a rolling shutter, one or more of image capture devices122,124, and126may include a selectable horizontal blanking period imposed before or after acquisition of image data associated with a row of pixels of an image sensor in image capture device122,124, and/or126. Further, one or more of image capture devices122,124, and/or126may include a selectable vertical blanking period imposed before or after acquisition of image data associated with an image frame of image capture device122,124, and126.

These timing controls may enable synchronization of frame rates associated with image capture devices122,124, and126, even where the line scan rates of each are different. Additionally, as will be discussed in greater detail below, these selectable timing controls, among other factors (e.g., image sensor resolution, maximum line scan rates, etc.) may enable synchronization of image capture from an area where the FOV of image capture device122overlaps with one or more FOVs of image capture devices124and126, even where the field of view of image capture device122is different from the FOVs of image capture devices124and126.

Frame rate timing in image capture device122,124, and126may depend on the resolution of the associated image sensors. For example, assuming similar line scan rates for both devices, if one device includes an image sensor having a resolution of 640×480 and another device includes an image sensor with a resolution of 1280×960, then more time will be required to acquire a frame of image data from the sensor having the higher resolution.

Another factor that may affect the timing of image data acquisition in image capture devices122,124, and126is the maximum line scan rate. For example, acquisition of a row of image data from an image sensor included in image capture device122,124, and126will require some minimum amount of time. Assuming no pixel delay periods are added, this minimum amount of time for acquisition of a row of image data will be related to the maximum line scan rate fir a particular device. Devices that offer 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 devices124and126may have a maximum line scan rate that is higher than a maximum line scan rate associated with image capture device122. In some embodiments, the maximum line scan rate of image capture device124and/or126may be 1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate of image capture device122.

In another embodiment, image capture devices122,124, and126may have the same maximum line scan rate, but image capture device122may be operated at a scan rate less than or equal to its maximum scan rate. The system may be configured such that one or more of image capture devices124and126operate at a line scan rate that is equal to the line scan rate of image capture device122. In other instances, the system may be configured such that the line scan rate of image capture device124and/or image capture device126may be 1.25, 1.5, 1.75, or 2 times or more than the line scan rate of image capture device122.

In some embodiments, image capture devices122,124, and126may be asymmetric. That is, they may include cameras having different fields of view (FOV) and focal lengths. The fields of view of image capture devices122,124, and126may include any desired area relative to an environment of vehicle200, for example. In some embodiments, one or more of image capture devices122,124, and126may be configured to acquire image data from an environment in front of vehicle200, behind vehicle200, to the sides of vehicle200, or combinations thereof.

Further, the focal length associated with each image capture device122,124, and/or126may be selectable (e.g., by inclusion of appropriate lenses etc.) such that each device acquires images of objects at a desired distance range relative to vehicle200. For example, in some embodiments image capture devices122,124, and126may acquire images of close-up objects within a few meters from the vehicle. Image capture devices122,124, and126may also be configured to acquire images of objects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100 m, 150 m, or more). Further, the focal lengths of image capture devices122,124, and126may be selected such that one image capture device (e.g., image capture device122) can acquire images of objects relatively close to the vehicle (e.g., within 10 m or within 20 m) while the other image capture devices (e.g., image capture devices124and126) can acquire images of more distant objects (e.g., greater than 20 m, 50 m, 100 m, 150 m, etc.) from vehicle200.

According to some embodiments, the FOV of one or more image capture devices122,124, and126may have a wide angle. For example, it may be advantageous to have a FOV of 140 degrees, especially for image capture devices122,124, and126that may be used to capture images of the area in the vicinity of vehicle200. For example, image capture device122may be used to capture images of the area to the right or left of vehicle200and, in such embodiments, it may be desirable for image capture device122to have a wide FOV (e.g., at least 140 degrees).

The field of view associated with each of image capture devices122,124, and126may depend on the respective focal lengths. For example, as the focal length increases, the corresponding field of view decreases.

Image capture devices122,124, and126may be configured to have any suitable fields of view. In one particular example, image capture device122may have a horizontal FOV of 46 degrees, image capture device124may have a horizontal FOV of 23 degrees, and image capture device126may have a horizontal FOV in between 23 and 46 degrees. In another instance, image capture device122may have a horizontal FOV of 52 degrees, image capture device124may have a horizontal FOV of 26 degrees, and image capture device126may have a horizontal FOV in between 26 and 52 degrees. In some embodiments, a ratio of the FOV of image capture device122to the FOVs of image capture device124and/or image capture device126may vary from 1.5 to 2.0. In other embodiments, this ratio may vary between 1.25 and 2.25.

System100may be configured so that a field of view of image capture device122overlaps, at least partially or fully, with a field of view of image capture device124and/or image capture device126. In some embodiments, system100may be configured such that the fields of view of image capture devices124and126, for example, fall within (e.g., are narrower than) and share a common center with the field of view of image capture device122. In other embodiments, the image capture devices122,124, and126may capture adjacent FOVs or may have partial overlap in their FOVs. In some embodiments, the fields of view of image capture devices122,124, and126may be aligned such that a center of the narrower FOV image capture devices124and/or126may be located in a lower half of the field of view of the wider FOV device122.

FIG. 2Fis a diagrammatic representation of exemplary vehicle control systems, consistent with the disclosed embodiments. As indicated inFIG. 2F, vehicle200may include throttling system220, braking system230, and steering system240. System100may provide inputs (e.g., control signals) to one or more of throttling system220, braking system230, and steering system240over one or more data links (e.g., any wired and/or wireless link or links for transmitting data). For example, based on analysis of images acquired by image capture devices122,124, and/or126, system100may provide control signals to one or more of throttling system220, braking system230, and steering system240to navigate vehicle200(e.g., by causing an acceleration, a turn, a lane shift, etc.). Further, system100may receive inputs from one or more of throttling system220, braking system230, and steering system24indicating operating conditions of vehicle200(e.g., speed, whether vehicle200is braking and/or turning, etc.). Further details are provided in connection withFIGS. 4-7, below.

As shown inFIG. 3A, vehicle200may also include a user interface170for interacting with a driver or a passenger of vehicle200. For example, user interface170in a vehicle application may include a touch screen320, knobs330, buttons340, and a microphone350. A driver or passenger of vehicle200may also use handles (e.g., located on or near the steering column of vehicle200including, for example, turn signal handles), buttons (e.g., located on the steering wheel of vehicle200), and the like, to interact with system100. In some embodiments, microphone350may be positioned adjacent to a rearview mirror310. Similarly, in some embodiments, image capture device122may be located near rearview mirror310. In some embodiments, user interface170may also include one or more speakers360(e.g., speakers of a vehicle audio system). For example, system100may provide various notifications (e.g., alerts) via speakers360.

FIGS. 3B-3Dare illustrations of an exemplary camera mount370configured to be positioned behind a rearview mirror (e.g., rearview mirror310) and against a vehicle windshield, consistent with disclosed embodiments. As shown inFIG. 3B, camera mount370may include image capture devices122,124, and126. Image capture devices124and126may be positioned behind a glare shield380, which may be flush against the vehicle windshield and include a composition of film and/or anti-reflective materials. For example, glare shield380may be positioned such that it aligns against a vehicle windshield having a matching slope. In some embodiments, each of image capture devices122,124, and126may be positioned behind glare shield380, as depicted, for example, inFIG. 3D. The disclosed embodiments are not limited to any particular configuration of image capture devices122,124, and126, camera mount370, and glare shield380.FIG. 3Cis an illustration of camera mount370shown inFIG. 3Bfrom a front perspective.

As will be appreciated by a person skilled in the art having the benefit of this disclosure, numerous variations and/or modifications may be made to the foregoing disclosed embodiments. For example, not all components are essential for the operation of system100. Further, any component may be located in any appropriate part of system100and the components may be rearranged into a variety of configurations while providing the functionality of the disclosed embodiments. Therefore, the foregoing configurations are examples and, regardless of the configurations discussed above, system100can provide a wide range of functionality to analyze the surroundings of vehicle200and navigate vehicle200in response to the analysis.

As discussed below in further detail and consistent with various disclosed embodiments, system100may provide a variety of features related to autonomous driving and/or driver assist technology. For example, system100may analyze image data, position data (e.g., GPS location information), map data, speed data, and/or data from sensors included in vehicle200. System100may collect the data for analysis from, for example, image acquisition unit120, position sensor130, and other sensors. Further, system100may analyze the collected data to determine whether or not vehicle200should take a certain action, and then automatically take the determined action without human intervention. For example, when vehicle200navigates without human intervention, system100may automatically control the braking, acceleration, and/or steering of vehicle200(e.g., by sending control signals to one or more of throttling system220, braking system230, and steering system240). Further, system100may analyze the collected data and issue warnings and/or alerts to vehicle occupants based on the analysis of the collected data. Additional details regarding the various embodiments that are provided by system100are provided below.

As discussed above, system100may provide drive assist functionality that uses a multi-camera system. The multi-camera system may use one or more cameras facing in the forward direction of a vehicle, in other embodiments, the multi-camera system may include one or more cameras facing to the side of a vehicle or to the rear of the vehicle. In one embodiment, for example, system100may use a two-camera imaging system, where a first camera and a second camera (e.g., image capture devices122and124) may be positioned at the front and/or the sides of a vehicle (e.g., vehicle200). The first camera may have a field of view that is greater than, less than, or partially overlapping with, the field of view of the second camera. In addition, the first camera may be connected to a first image processor to perform monocular image analysis of images provided by the first camera, and the second camera may be connected to a second image processor to perform monocular image analysis of images provided by the second camera. The outputs (e.g., processed information) of the first and second image processors may be combined. In some embodiments, the second image processor may receive images from both the first camera and second camera to perform stereo analysis. In another embodiment, system100may use a three-camera imaging system where each of the cameras has a different field of view. Such a system may therefore, make decisions based on information derived from objects located at varying distances both forward and to the sides of the vehicle. References to monocular image analysis may refer to instances where image analysis is performed based on images captured from a single point of view (e.g., from a single camera). Stereo image analysis may refer to instances where image analysis is performed based on two or more images captured with one or more variations of an image capture parameter. For example, captured images suitable for performing stereo image analysis may include images captured: from two or more different positions, from different fields of view, using different focal lengths, along with parallax information, etc.

For example, in one embodiment, system100may implement a three camera configuration using image capture devices122-126. In such a configuration, image capture device122may provide a narrow field of view (e.g., 34 degrees, or other values selected from a range of about 20 to 45 degrees, etc.), image capture device124may provide a wide field of view (e.g., 150 degrees or other values selected from a range of about 100 to about 180 degrees), and image capture device126may provide an intermediate field of view (e.g., 46 degrees or other values selected from a range of about 35 to about 60 degrees). In some embodiments, image capture device126may act as a main or primary camera. Image capture devices122-126may be positioned behind rearview mirror310and positioned substantially side-by-side (e.g., 6 cm apart). Further, in some embodiments, as discussed above, one or more of image capture devices122-126may be mounted behind glare shield380that is flush with the windshield of vehicle200. Such shielding may act to minimize the impact of any reflections from inside the car on image capture devices122-126.

In another embodiment, as discussed above in connection withFIGS. 3B and 3C, the wide field of view camera (e.g., image capture device124in the above example) may be mounted lower than the narrow and main field of view cameras (e.g., image devices122and126in the above example). This configuration may provide a free line of sight from the wide field of view camera. To reduce reflections, the cameras may be mounted close to the windshield of vehicle200, and may include polarizers on the cameras to damp reflected light.

A three camera system may provide certain performance characteristics. For example, some embodiments may include an ability to validate the detection of objects by one camera based on detection results from another camera, in the three camera configuration discussed above, processing unit110may include, for example, three processing devices (e.g., three EyeQ series of processor chips, as discussed above), with each processing device dedicated to processing images captured by one or more of image capture devices122-126.

In a three camera system, a first processing device may receive images from both the main camera and the narrow field of view camera, and perform vision processing of the narrow FOV camera to, for example, detect other vehicles, pedestrians, lane marks, traffic signs, traffic lights, and other road objects. Further, the first processing device may calculate a disparity of pixels between the images from the main camera and the narrow camera and create a 3D reconstruction of the environment of vehicle200. The first processing device may then combine the 3D reconstruction with 3D map data or with 3D information calculated based on information from another camera.

The second processing device may receive images from main camera and perform vision processing to detect other vehicles, pedestrians, lane marks, traffic signs, traffic lights, and other road objects. Additionally, the second processing device may calculate a camera displacement and, based on the displacement, calculate a disparity of pixels between successive images and create a 3D reconstruction of the scene (e.g., a structure from motion). The second processing device may send the structure from motion based 3D reconstruction to the first processing device to be combined with the stereo 3D images.

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

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

In some embodiments, system100may use two image capture devices (e.g., image capture devices122and124) in providing navigation assistance for vehicle200and use a third image capture device (e.g., image capture device126) to provide redundancy and validate the analysis of data received from the other two image capture devices. For example, in such a configuration, image capture devices122and124may provide images for stereo analysis by system100for navigating vehicle200, while image capture device126may provide images for monocular analysis by system100to provide redundancy and validation of information obtained based on images captured from image capture device122and/or image capture device124. That is, image capture device126(and a corresponding processing device) may be considered to provide a redundant sub-system for providing a check on the analysis derived from image capture devices122and124(e.g., to provide an automatic emergency braking (AEB) system).

One of skill in the art will recognize that the above camera configurations, camera placements, number of cameras, camera locations, etc., are examples only. These components and others described relative to the overall system may be assembled and used in a variety of different configurations without departing from the scope of the disclosed embodiments. Further details regarding usage of a multi-camera system to provide driver assist and/or autonomous vehicle functionality follow below.

FIG. 4is an exemplary functional block diagram of memory140and/or150, which may be stored/programmed with instructions for performing one or more operations consistent with the disclosed embodiments. Although the following refers to memory140, one of skill in the art will recognize that instructions may be stored in memory140and/or150.

As shown inFIG. 4, memory140may store a monocular image analysis module402, a stereo image analysis module404, a velocity and acceleration module406, and a navigational response module408. The disclosed embodiments are not limited to any particular configuration of memory140. Further, application processor180and/or image processor190may execute the instructions stored in any of modules402-408included in memory140. One of skill in the art will understand that references in the following discussions to processing unit110may refer to application processor180and image processor190individually or collectively. Accordingly, steps of any of the following processes may be performed by one or more processing devices.

In one embodiment, monocular image analysis module402may store instructions (such as computer vision software) which, when executed by processing unit110, performs monocular image analysis of a set of images acquired by one of image capture devices122,124, and126. In some embodiments, processing unit110may combine information from a set of images with additional sensory information (e.g., information from radar) to perform the monocular image analysis. As described in connection withFIGS. 5A-5Dbelow, monocular image analysis module402may 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 feature associated with an environment of a vehicle. Based on the analysis, system100(e.g., via processing unit110) may cause one or more navigational responses in vehicle200, such as a turn, a lane shift, a change in acceleration, and the like, as discussed below in connection with navigational response module408.

In one embodiment, stereo image analysis module404may store instructions (such as computer vision software) which, when executed by processing unit110, performs stereo image analysis of first and second sets of images acquired by a combination of image capture devices selected from any of image capture devices122,124, and126. In some embodiments, processing unit110may combine information from the first and second sets of images with additional sensory information (e.g., information from radar) to perform the stereo image analysis. For example, stereo image analysis module404may include instructions for performing stereo image analysis based on a first set of images acquired by image capture device124and a second set of images acquired by image capture device126. As described in connection withFIG. 6below, stereo image analysis module404may include instructions for detecting a set of features within the first and second sets of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, hazardous objects, and the like. Based on the analysis, processing unit110may cause one or more navigational responses in vehicle200, such as a turn, a lane shift, a change in acceleration, and the like, as discussed below in connection with navigational response module408.

In one embodiment, velocity and acceleration module406may store software configured to analyze data received from one or more computing and electromechanical devices in vehicle200that are configured to cause a change in velocity and/or acceleration of vehicle200. For example, processing unit110may execute instructions associated with velocity and acceleration module406to calculate a target speed for vehicle200based on data derived from execution of monocular image analysis module402and/or stereo image analysis module404. Such data may include, for example, a target position, velocity, and/or acceleration, the position and/or speed of vehicle200relative to a nearby vehicle, pedestrian, or road object, position information for vehicle200relative to lane markings of the road, and the like. In addition, processing unit110may calculate a target speed for vehicle200based on sensory input (e.g., information from radar) and input from other systems of vehicle200, such as throttling system220, braking system230, and/or steering system240of vehicle200. Based on the calculated target speed, processing unit110may transmit electronic signals to throttling system220, braking system230, and/or steering system240of vehicle200to trigger a change in velocity and/or acceleration by, for example, physically depressing the brake or easing up off the accelerator of vehicle200.

In one embodiment, navigational response module408may store software executable by processing unit110to determine a desired navigational response based on data derived from execution of monocular image analysis module402and/or stereo image analysis module404. Such data may include position and speed information associated with nearby vehicles, pedestrians, and road objects, target position information for vehicle200, and the like. Additionally, in some embodiments, the navigational response may be based (partially or fully) on map data, a predetermined position of vehicle200, and/or a relative velocity or a relative acceleration between vehicle200and one or more objects detected from execution of monocular image analysis module402and/or stereo image analysis module404. Navigational response module408may also determine a desired navigational response based on sensory input (e.g., information from radar) and inputs from other systems of vehicle200, such as throttling system220, braking system230, and steering system240of vehicle200. Based on the desired navigational response, processing unit110may transmit electronic signals to throttling system220, braking system230, and steering system240of vehicle200to trigger a desired navigational response by, for example, turning the steering wheel of vehicle200to achieve a rotation of a predetermined angle, in some embodiments, processing unit110may use the output of navigational response module408(e.g., the desired navigational response) as an input to execution of velocity and acceleration module406for calculating a change in speed of vehicle200.

FIG. 5Ais a flowchart showing an exemplary process500A for causing one or more navigational responses based on monocular image analysis, consistent with disclosed embodiments. At step510, processing unit110may receive a plurality of images via data interface128between processing unit110and image acquisition unit120. For instance, a camera included in image acquisition unit120(such as image capture device122having field of view202) may capture a plurality of images of an area forward of vehicle200(or to the sides or rear of a vehicle, for example) and transmit them over a data connection (e.g., digital, wired, USB, wireless, Bluetooth, etc.) to processing unit110. Processing unit110may execute monocular image analysis module402to analyze the plurality of images at step520, as described in further detail in connection withFIGS. 5B-5Dbelow. By performing the analysis, processing unit110may detect a set of features within the set of images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, and the like.

Processing unit110may also execute monocular image analysis module402to detect various road hazards at step520, such as, for example, parts of a truck tire, fallen road signs, loose cargo, small animals, and the like. Road hazards may vary in structure, shape, size, and color, which may make detection of such hazards more challenging. In some embodiments, processing unit110may execute monocular image analysis module402to perform multi-frame analysis on the plurality of images to detect road hazards. For example, processing unit110may estimate camera motion between consecutive image frames and calculate the disparities in pixels between the frames to construct a 3D-map of the road. Processing unit110may then use the 3D-map to detect the road surface, as well as hazards existing above the road surface.

At step530, processing unit110may execute navigational response module408to cause one or more navigational responses in vehicle200based on the analysis performed at step520and the techniques as described above in connection withFIG. 4. Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, and the like. In some embodiments, processing unit110may use data derived from execution of velocity and acceleration module406to cause the one or more navigational responses. Additionally, multiple navigational responses may occur simultaneously, in sequence, or any combination thereof. For instance, processing unit110may cause vehicle200to shift one lane over and then accelerate by, for example, sequentially transmitting control signals to steering system240and throttling system220of vehicle200. Alternatively, processing unit110may cause vehicle200to brake while at the same time shifting lanes by, for example, simultaneously transmitting control signal to braking system230and steering system240of vehicle200.

FIG. 5Bis a flowchart showing an exemplary process500B for detecting one or more vehicles and/or pedestrians in a set of images, consistent with disclosed embodiments. Processing unit110may execute monocular image analysis module402to implement process500B. At step540, processing unit110may determine a set of candidate objects representing possible vehicles and/or pedestrians. For example, processing unit110may scan one or more images, compare the images to one or more predetermined patterns, and identify within each image possible locations that may contain objects of interest (e.g., vehicles, pedestrians, or portions thereof). The predetermined patterns may be designed in such a way to achieve a high rate of “false hits” and a low rate of “misses.” For example, processing unit110may use a low threshold of similarity to predetermined patterns for identifying candidate objects as possible vehicles or pedestrians. Doing so may allow processing unit110to reduce the probability of missing (e.g., not identifying) a candidate object representing a vehicle or pedestrian.

At step542, processing unit110may filter the set of candidate objects to exclude certain candidates (e.g., irrelevant or less relevant objects) based on classification criteria. Such criteria may be derived from various properties associated with object types stored in a database (e.g., a database stored in memory140). Properties may include object shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Thus, processing unit110may use one or more sets of criteria to reject false candidates from the set of candidate objects.

At step544, processing unit110may analyze multiple frames of images to determine whether objects in the set of candidate objects represent vehicles and/or pedestrians. For example, processing unit110may track a detected candidate object across consecutive frames and accumulate frame-by-frame data associated with the detected object (e.g., size, position relative to vehicle200, etc.). Additionally, processing unit110may estimate parameters for the detected object and compare the object's frame-by-frame position data to a predicted position.

At step546, processing unit110may construct a set of measurements for the detected objects. Such measurements may include, for example, position, velocity, and acceleration values (relative to vehicle200) associated with the detected objects. In some embodiments, processing unit110may construct the measurements based on estimation techniques using a series of time-based observations such as Kalman filters or linear quadratic estimation (LQE), and/or based on available modeling data for different object types (e.g., cars, trucks, pedestrians, bicycles, road signs, etc.). The Kalman filters may be based on a measurement of an object's scale, where the scale measurement is proportional to a time to collision (e.g., the amount of time for vehicle200to reach the object). Thus, by performing steps540-546, processing unit110may identify vehicles and pedestrians appearing within the set of captured images and derive information (e.g., position, speed, size) associated with the vehicles and pedestrians. Based on the identification and the derived information, processing unit110may cause one or more navigational responses in vehicle200, as described in connection withFIG. 5A, above.

At step548, processing unit110may perform an optical flow analysis of one or more images to reduce the probabilities of detecting a “false hit” and missing a candidate object that represents a vehicle or pedestrian. The optical flow analysis may refer to, for example, analyzing motion patterns relative to vehicle200in the one or more images associated with other vehicles and pedestrians, and that are distinct from road surface motion. Processing unit110may calculate the motion of candidate objects by observing the different positions of the objects across multiple image frames, which are captured at different times. Processing unit110may use the position and time values as inputs into mathematical models for calculating the motion of the candidate objects. Thus, optical flow analysis may provide another method of detecting vehicles and pedestrians that are nearby vehicle200. Processing unit110may perform optical flow analysis in combination with steps540-546to provide redundancy for detecting vehicles and pedestrians and increase the reliability of system100.

FIG. 5Cis a flowchart showing an exemplary process500C for detecting road marks and/or lane geometry information in a set of images, consistent with disclosed embodiments. Processing unit110may execute monocular image analysis module402to implement process500C. At step550, processing unit110may detect a set of objects by scanning one or more images. To detect segments of lane markings, lane geometry information, and other pertinent road marks, processing unit110may filter the set of objects to exclude those determined to be irrelevant (e.g., minor potholes, small rocks, etc.). At step552, processing unit110may group together the segments detected in step550belonging to the same road mark or lane mark. Based on the grouping, processing unit110may develop a model to represent the detected segments, such as a mathematical model.

At step554, processing unit110may construct a set of measurements associated with the detected segments. In some embodiments, processing unit110may create a projection of the detected segments from the image plane onto the real-world plane. The projection may be characterized using a 3rd-degree polynomial having coefficients corresponding to physical properties such as the position, slope, curvature, and curvature derivative of the detected road. In generating the projection, processing unit110may take into account changes in the road surface, as well as pitch and roll rates associated with vehicle200. In addition, processing unit110may model the road elevation by analyzing position and motion cues present on the road surface. Further, processing unit110may estimate the pitch and roll rates associated with vehicle200by tracking a set of feature points in the one or more images.

At step556, processing unit110may perform multi-frame analysis by, for example, tracking the detected segments across consecutive image frames and accumulating frame-by-frame data associated with detected segments. As processing unit110performs multi-frame analysis, the set of measurements constructed at step554may become more reliable and associated with an increasingly higher confidence level. Thus, by performing steps550-556, processing unit110may identify road marks appearing within the set of captured images and derive lane geometry information. Based on the identification and the derived information, processing unit110may cause one or more navigational responses in vehicle200, as described in connection withFIG. 5A, above.

At step558, processing unit110may consider additional sources of information to further develop a safety model for vehicle200in the context of its surroundings. Processing unit110may use the safety model to define a context in which system100may execute autonomous control of vehicle200in a safe manner. To develop the safety model, in some embodiments, processing unit110may consider the position and motion of other vehicles, the detected road edges and barriers, and/or general road shape descriptions extracted from map data (such as data from map database160). By considering additional sources of information, processing unit110may provide redundancy for detecting road marks and lane geometry and increase the reliability of system100.

FIG. 5Dis a flowchart showing an exemplary process500D for detecting traffic lights in a set of images, consistent with disclosed embodiments. Processing unit110may execute monocular image analysis module402to implement process500D. At step560, processing unit110may scan the set of images and identify objects appearing at locations in the images likely to contain traffic lights. For example, processing unit110may filter the identified objects to construct a set of candidate objects, excluding those objects unlikely to correspond to traffic lights. The filtering may be done based on various properties associated with traffic lights, such as shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Such properties may be based on multiple examples of traffic lights and traffic control signals and stored in a database. In some embodiments, processing unit110may perform multi-frame analysis on the set of candidate objects reflecting possible traffic lights. For example, processing unit110may track the candidate objects across consecutive image frames, estimate the real world position of the candidate objects, and filter out those objects that are moving (which are unlikely to be traffic lights). In some embodiments, processing unit110may perform color analysis on the candidate objects and identify the relative position of the detected colors appearing inside possible traffic lights.

At step562, processing unit110may analyze the geometry of a junction. The analysis may be based on any combination of: (i) the number of lanes detected on either side of vehicle200, (ii) markings (such as arrow marks) detected on the road, and (iii) descriptions of the junction extracted from map data (such as data from map database160). Processing unit110may conduct the analysis using information derived from execution of monocular analysis module402. In addition, processing unit110may determine a correspondence between the traffic lights detected at step560and the lanes appearing near vehicle200.

As vehicle200approaches the junction, at step564, processing unit110may update the confidence level associated with the analyzed junction geometry and the detected traffic lights. For instance, the number of traffic lights estimated to appear at the junction as compared with the number actually appearing at the junction may impact the confidence level. Thus, based on the confidence level, processing unit110may delegate control to the driver of vehicle200in order to improve safety conditions. By performing steps560-564processing unit110may identify traffic lights appearing within the set of captured images and analyze junction geometry information. Based on the identification and the analysis, processing unit110may cause one or more navigational responses in vehicle200, as described in connection withFIG. 5A, above.

FIG. 5Eis a flowchart showing an exemplary process500E for causing one or more navigational responses in vehicle200based on a vehicle path, consistent with the disclosed embodiments. At step570, processing unit110may construct an initial vehicle path associated with vehicle200. The vehicle path may be represented using a set of points expressed in coordinates (x, z), and the distance dibetween two points in the set of points may fall in the range of 1 to 5 meters. In one embodiment, processing unit110may construct the initial vehicle path using two polynomials, such as left and right road polynomials. Processing unit110may calculate the geometric midpoint between the two polynomials and offset each point included in the resultant vehicle path by a predetermined offset (e.g., a smart lane offset), if any (an offset of zero may correspond to travel in the middle of a lane). The offset may be in a direction perpendicular to a segment between any two points in the vehicle path. In another embodiment, processing unit110may use one 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).

At step572, processing unit110may update the vehicle path constructed at step570. Processing unit110may reconstruct the vehicle path constructed at step570using a higher resolution, such that the distance dkbetween two points in the set of points representing the vehicle path is less than the distance didescribed above. For example, the distance dkmay fall in the range of 0.1 to 0.3 meters. Processing unit110may reconstruct the vehicle path using a parabolic spline algorithm, which may yield a cumulative distance vector S corresponding to the total length of the vehicle path (i.e., based on the set of points representing the vehicle path).

At step574, processing unit110may determine a look-ahead point (expressed in coordinates as (xl, zl)) based on the updated vehicle path constructed at step572. Processing unit110may extract the look-ahead point from the cumulative distance vector S, and the look-ahead point may be associated with a look-ahead distance and look-ahead time. The look-ahead distance, which may have a lower bound ranging from 10 to 20 meters, may be calculated as the product of the speed of vehicle200and the look-ahead time. For example, as the speed of vehicle200decreases, the look-ahead distance may also decrease (e.g., until it reaches the lower bound). The look-ahead time, which may range from 0.5 to 1.5 seconds, may be inversely proportional to the gain of one or more control loops associated with causing a navigational response in vehicle200, such as the heading error tracking control loop. For example, the gain of the heading error tracking control loop may depend on the bandwidth of a yaw rate loop, a steering actuator loop, ear lateral dynamics, and the like. Thus, the higher the gain of the heading error tracking control loop, the lower the look-ahead time.

At step576, processing unit110may determine a heading error and yaw rate command based on the look-ahead point determined at step574. Processing unit110may determine the heading error by calculating the arctangent of the look-ahead point, e.g., arctan(xl/zl). Processing unit110may determine the yaw rate command as the product of the heading error and a high-level control gain. The high-level control gain may be equal to: (2/look-ahead time), if the look-ahead distance is not at the lower bound. Otherwise, the high-level control gain may be equal to: (2*speed of vehicle200/look-ahead distance).

FIG. 5Fis a flowchart showing an exemplary process500F for determining whether a leading vehicle is changing lanes, consistent with the disclosed embodiments. At step580, processing unit110may determine navigation information associated with a leading vehicle (e.g., a vehicle traveling ahead of vehicle200). For example, processing unit110may determine the position, velocity (e.g., direction and speed), and/or acceleration of the leading vehicle, using the techniques described in connection withFIGS. 5A and 5B, above. Processing unit110may also determine one or more road polynomials, a look-ahead point (associated with vehicle200), and/or a snail trail (e.g., a set of points describing a path taken by the leading vehicle), using the techniques described in connection withFIG. 5E, above.

At step582, processing unit110may analyze the navigation information determined at step580. In one embodiment, processing unit110may calculate the distance between a snail trail and a road polynomial (e.g., along the trail). If the variance of this distance along the trail exceeds a predetermined threshold (for example, 0.1 to 0.2 meters on a straight road, 0.3 to 0.4 meters on a moderately curvy road, and 0.5 to 0.6 meters on a road with sharp curves), processing unit110may determine that the leading vehicle is likely changing lanes. In the case where multiple vehicles are detected traveling ahead of vehicle200, processing unit110may compare the snail trails associated with each vehicle. Based on the comparison, processing unit110may determine that a vehicle whose snail trail does not match with the snail trails of the other vehicles is likely changing lanes. Processing unit110may additionally compare the curvature of the snail trail (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 database160), from road polynomials, from other vehicles' snail trails, from prior knowledge about the road, and the like. If the difference in curvature of the snail trail and the expected curvature of the road segment exceeds a predetermined threshold, processing unit110may determine that the leading vehicle is likely changing lanes.

In another embodiment, processing unit110may compare the leading vehicle's instantaneous position with the look-ahead point (associated with vehicle200) over a specific period of time (e.g., 0.5 to 1.5 seconds). If the distance between the leading vehicle's instantaneous position and the look-ahead point varies during the specific period of time, and the cumulative sum of variation exceeds a predetermined threshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8 meters on a moderately curvy road, and 1.3 to 1.7 meters on a road with sharp curves), processing unit110may determine that the leading vehicle is likely changing lanes. In another embodiment, processing unit110may analyze the geometry of the snail trail by comparing the lateral distance traveled along the trail with the expected curvature of the snail trail. The expected radius of curvature may be determined according to the calculation: (δz2+δx2)/2/(δx), where δxrepresents the lateral distance traveled and δzrepresents the longitudinal distance traveled. If the difference between the lateral distance traveled and the expected curvature exceeds a predetermined threshold (e.g., 500 to 700 meters), processing unit110may determine that the leading vehicle is likely changing lanes. In another embodiment, processing unit110may analyze the position of the leading vehicle. If the position of the leading vehicle obscures a road polynomial (e.g., the leading vehicle is overlaid on top of the road polynomial), then processing unit110may determine that the leading vehicle is likely changing lanes. In the case where the position of the leading vehicle is such that, another vehicle is detected ahead of the leading vehicle and the snail trails of the two vehicles are not parallel, processing unit110may determine that the (closer) leading vehicle is likely changing lanes.

At step584, processing unit110may determine whether or not leading vehicle200is changing lanes based on the analysis performed at step582. For example, processing unit110may make the determination based on a weighted average of the individual analyses performed at step582. Under such a scheme, for example, a decision by processing unit110that the leading vehicle is likely changing lanes based on a particular type of analysis may be assigned a value of “1” (and “0” to represent a determination that the leading vehicle is not likely changing lanes). Different analyses performed at step582may be assigned different weights, and the disclosed embodiments are not limited to any particular combination of analyses and weights.

FIG. 6is a flowchart showing an exemplary process600for causing one or more navigational responses based on stereo image analysis, consistent with disclosed embodiments. At step610, processing unit110may receive a first and second plurality of images via data interface128. For example, cameras included in image acquisition unit120(such as image capture devices122and124having fields of view202and204) may capture a first and second plurality of images of an area forward of vehicle200and transmit them over a digital connection (e.g., USB, wireless, Bluetooth, etc) to processing unit110. In some embodiments, processing unit110may receive the first and second plurality of images via two or more data interfaces. The disclosed embodiments are not limited to any particular data interface configurations or protocols.

At step620, processing unit110may execute stereo image analysis module404to perform stereo image analysis of the first and second plurality of images to create a 3D map of the road in front of the vehicle and detect features within the images, such as lane markings, vehicles, pedestrians, road signs, highway exit ramps, traffic lights, road hazards, and the like. Stereo image analysis may be performed in a manner similar to the steps described in connection withFIGS. 5A-5D, above. For example, processing unit110may execute stereo image analysis module404to detect candidate objects (e.g., vehicles, pedestrians, road marks, traffic lights, road hazards, etc.) within the first and second plurality of images, filter out a subset of the candidate objects based on various criteria, and perform multi-frame analysis, construct measurements, and determine a confidence level for the remaining candidate objects. In performing the steps above, processing unit110may consider information from both the first and second plurality of images, rather than information from one set of images alone. For example, processing unit110may analyze the differences in pixel-level data (or other data subsets from among the two streams of captured images) for a candidate object appearing in both the first and second plurality of images. As another example, processing unit110may estimate a position and/or velocity of a candidate object (e.g., relative to vehicle200) by observing that the object appears in one of the plurality of images but not the other or relative to other differences that may exist relative to objects appearing if the two image streams. For example, position, velocity, and/or acceleration relative to vehicle200may be determined based on trajectories, positions, movement characteristics, etc. of features associated with an object appearing in one or both of the image streams.

At step630, processing unit110may execute navigational response module408to cause one or more navigational responses in vehicle200based on the analysis performed at step620and the techniques as described above in connection withFIG. 4. Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, a change in velocity, braking, and the like, in some embodiments, processing unit110may use data derived from execution of velocity and acceleration module406to cause the one or more navigational responses. Additionally, multiple navigational responses may occur simultaneously, in sequence, or any combination thereof.

FIG. 7is a flowchart showing an exemplary process700for causing one or more navigational responses based on an analysis of three sets of images, consistent with disclosed embodiments. At step710, processing unit110may receive a first, second, and third plurality of images via data interface128. For instance, cameras included in image acquisition unit120(such as image capture devices122,124, and126having fields of view202,204, and206) may capture a first, second, and third plurality of images of an area forward and/or to the side of vehicle200and transmit them over a digital connection (e.g., USB, wireless, Bluetooth, etc.) to processing unit110. In some embodiments, processing unit110may receive the first, second, and third plurality of images via three or more data interfaces. For example, each of image capture devices122,124,126may have an associated data interface for communicating data to processing unit110. The disclosed embodiments are not limited to any particular data interface configurations or protocols.

At step720, processing unit110may 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, and the like. The analysis may be performed in a manner similar to the steps described in connection withFIGS. 5A-5D and 6, above. For instance, processing unit110may perform monocular image analysis (e.g., via execution of monocular image analysis module402and based on the steps described in connection withFIGS. 5A-5D, above) on each of the first, second, and third plurality of images. Alternatively, processing unit110may perform stereo image analysis (e.g., via execution of stereo image analysis module404and based on the steps described in connection withFIG. 6, above) on the first and second plurality of images, the second and third plurality of images, and/or the first and third plurality of images. The processed information corresponding to the analysis of the first, second, and/or third plurality of images may be combined. In some embodiments, processing unit110may perform a combination of monocular and stereo image analyses. For example, processing unit110may perform monocular image analysis (e.g., via execution of monocular image analysis module402) on the first plurality of images and stereo image analysis (e.g., via execution of stereo image analysis module404) on the second and third plurality of images. The configuration of image capture devices122,124, and126—including their respective locations and fields of view202,204, and206—may influence the types of analyses conducted on the first, second, and third plurality of images. The disclosed embodiments are not limited to a particular configuration of image capture devices122,124, and126, or the types of analyses conducted on the first, second, and third plurality of images.

In some embodiments, processing unit110may perform testing on system100based on the images acquired and analyzed at steps710and720. Such testing may provide an indicator of the overall performance of system100for certain configurations of image capture devices122,124, and126. For example, processing unit110may determine the proportion of “false hits” (e.g., cases where system100incorrectly determined the presence of a vehicle or pedestrian) and “misses.”

At step730, processing unit110may cause one or more navigational responses in vehicle200based on information derived from two of the first, second, and third plurality of images. Selection of two of the first, second, and third plurality of images may depend on various factors, such as, for example, the number, types, and sizes of objects detected in each of the plurality of images. Processing unit110may also make the selection based on image quality and resolution, the effective field of view reflected in the images, the number of captured frames, the extent to which one or more objects of interest actually appear in the frames (e.g., the percentage of frames in which an object appears, the proportion of the object that appears in each such frame, etc.), and the like.

In some embodiments, processing unit110may select information derived from two of the first, second, and third plurality of images by determining the extent to which information derived from one image source is consistent with information derived from other image sources. For example, processing unit110may combine the processed information derived from each of image capture devices122,124, and126(whether by monocular analysis, stereo analysis, or any combination of the two) and determine visual indicators (e.g., lane markings, a detected vehicle and its location and/or path, a detected traffic light, etc.) that are consistent across the images captured from each of image capture devices122,124, and126. Processing unit110may also exclude information that is inconsistent across the captured images (e.g., a vehicle changing lanes, a lane model indicating a vehicle that is too close to vehicle200, etc.). Thus, processing unit110may select information derived from two of the first, second, and third plurality of images based on the determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift, a change in acceleration, and the like. Processing unit110may cause the one or more navigational responses based on the analysis performed at step720and the techniques as described above in connection withFIG. 4. Processing unit110may also use data derived from execution of velocity and acceleration module406to cause the one or more navigational responses. In some embodiments, processing unit110may cause the one or more navigational responses based on a relative position, relative velocity, and/or relative acceleration between vehicle200and an object detected within any of the first, second, and third plurality of images. Multiple navigational responses may occur simultaneously, in sequence, or any combination thereof.

Object Detecting and Braking System

In some embodiments, vehicle200may include an object detecting and braking system configured to detect an object in front of vehicle200and cause vehicle200to brake, e.g., using braking system230, based on the detection of the object. For example, the object detecting and braking system may cause vehicle200to brake automatically, or autonomously, without or with limited driver intervention. The automatic or autonomous object detecting and braking system may or may not allow a driver to intervene to take over control of braking system230.

The object detecting and braking system may include a plurality of other systems discussed above. For example, the object detecting and braking system may include system100, braking system230, and one or more of image capture devices122,124, and126. The one or more of image capture devices122,124, and126may acquire one or more images of an environment or an area in front of vehicle200, which may include an object, such as another vehicle, a pedestrian, a road surface, etc. Processing unit110may process the images based on at least two independent analyses to determine at least two times-to-collision (TTCs) of vehicle200with the object. In some embodiments, the two TTCs of vehicle200with the object may be determined separately and independently of each other. A “time-to-collision” (TTC) may also be referred to as a “time-to-contact.” The time-to-collision is an estimated time that would lapse before vehicle200collides with another object. Processing unit110may determine that a difference between the two times-to-collision does not exceed a predetermined threshold. Based on this determination, processing unit110may cause vehicle200to brake using, e.g., braking system230. The automatic or autonomous braking may assist the driver to avoid collision with the object in front of vehicle200, thereby improving driving safety. By causing vehicle200to brake when the difference between the two TTCs does not exceed the predetermined threshold, false braking may be prevented or reduced.

In some embodiments, the object detecting and braking system may use two independent channels of information inherent to monocular video sensing. The first channel of information may be referred to as a “texture” or “appearance information” channel. System100may perform an analysis based on the texture or appearance information. In this channel, system100may perform pattern recognition over the pixels intensities in each image separately among different images (e.g., two sequential images or frames). System100may estimate the change of a footprint size (e.g., a dimension length of a feature of an object in the image) by comparing image data of the two sequential images. The second channel of information may be referred to as a “structural” or “optical flow information” channel. System100may perform an analysis based on the structural or optical flow information. For example, system100may process two or more images (e.g., sequentially acquired images or frames) to obtain an optical flow field. The optical flow field may provide information regarding the movement of trackable textures between the two or more images or frames. The analysis of the optical flow field may indicate a low time-to-collision upright object in the images. The two analyses may also be referred to as two modalities, methods, or models.

In some embodiments, the structure (or optical flow) based analysis may serve to validate a result of a texture based analysis. For example, the texture based analysis may be performed on a plurality of images (e.g., at least two images), and the structure based analysis may be performed based on the same two or more images used in the texture based analysis after the texture based analysis is performed. In such embodiments, the structure based analysis may validate a result of the texture based analysis. In some embodiments, the texture based analysis may be performed after the structure based analysis has been performed, and may validate a result of the structure based analysis.

In some embodiments, the texture based analysis may use a first group of at least two images, and the structure based analysis may use a second group of at least two images. The first group and the second group may be the same or different. In some embodiments, the first group of at least two images may be selected from odd numbered images among a plurality of images acquired by at least one of image capture devices122-126, and the second group of at least two images may be selected from even numbered images among the same plurality of images acquired by at least of image capture devices122-126.

In some embodiments, the first group of at least two images may be selected from a plurality of images that are acquired at a first time interval and the second group of at least two images may be selected from the same plurality of images that are acquired at a second time interval, which may be different from the first time interval. In some embodiments, a first image capture device (or a first image sensor) may be designated for capturing images for the texture based analysis, and a second image capture device (or a second image sensor) may be designated for capturing images for structure based analysis.

The texture based analysis and the structure based analysis may complement each other, and the combination of their results that may be used by processing unit110to make a braking decision. For example, the texture based analysis may result in a first estimate of time-to-collision, and the structure based analysis may also result in a second estimate of the time-to-collision. Processing unit110may cause braking system230to brake vehicle200when the difference between the first and second estimates is smaller than a threshold. One of the texture based analysis and the structure based analysis may provide a redundancy for the other, thereby improve accuracy of braking, for example, autonomous braking in emergency driving situation, by preventing or reducing the occurrence of false braking.

In some embodiments, when the structure based analysis serves as a validator of the texture based analysis, the texture based analysis may detect an object (e.g., a car or a pedestrian) in front of vehicle200, and may determine that the time-to-collision is low (e.g., lower than a predetermined threshold). The texture based analysis may reach this determination by calculating the time-to-collision based on a change of a footprint size through time in at least two sequentially acquired images. This determination from the texture based analysis may then trigger the structure based analysis. In some embodiments, a determination that the time-to-collision is low from the texture based analysis may serve as a precondition for releasing a braking decision (e.g., an autonomous emergency braking decision) to vehicle systems. In some embodiments, braking (e.g., autonomous emergency braking) may not be performed until the structure based analysis confirms the determination made via the texture based analysis.

In some embodiments, system100may require that the estimated time-to-collision (TTC) determined by the structure based analysis is identical to or substantially identical to (up to an error tolerance) to the estimated time-to-collision (TTC) determined by the texture based analysis. For example, as discussed below, processing unit10may determine that a difference between the two TTCs determined by the texture based analysis and the structure based analysis does not exceed a predetermined threshold, which may be the error tolerance. In some embodiments, system100may take into account the divergence of the object's focus of expansion from that of the static environment. If the focus of expansion of the object diverges (e.g., moves) away from the focus of expansion of the static environment, this may be a strong indication that the object is a low TTC upright object. An example is discussed in greater detail with respect toFIGS. 15A and 15B. This requirement is optional because it may only apply in the case where the target object has a velocity component that is perpendicular to the driving direction of vehicle200(e.g., target object traveling in a direction that is not parallel to the driving direction of vehicle200).

In some embodiments, processing unit110may use certain criteria for deciding whether structure based analysis supports texture based analysis in making a braking (e.g., autonomous emergency braking) decision. For example, such criteria may include a requirement that the optical flow fits a model of low time-to-collision upright object (LTU object), and not an open-road (or an open road surface).

FIG. 8is a diagrammatic representation of an exemplary vehicle200including an object detecting and braking system (or referred to as an emergency braking decisioning system). The object detecting and braking system may include system100and braking system230. The object detecting and braking system may also include at least one of image capture devices122and124(and126, which is not shown inFIG. 8). The object detecting and braking system may include processing unit110, which may be part of system100. At least one of image capture devices122and124may acquire one or a plurality of images of an area including an object in front of vehicle200. The object may be another vehicle, such as a vehicle800that may be traveling in front of vehicle200. As shown inFIG. 8, in some embodiments, vehicle800may be traveling in a direction that is parallel to a traveling direction of vehicle200. A parallel direction may include a situation in which vehicle800is traveling in the same direction as and in alignment or substantially in alignment with vehicle200(e.g., vehicle800traveling in front of vehicle200), as shown inFIG. 8. A parallel direction may also include a situation in which vehicle800is traveling in an opposite direction relative to vehicle200and in aliment with vehicle200(e.g., vehicle800moving toward vehicle200in a direction that is 180 degrees, or substantially 180 degrees, opposite to the traveling direction of vehicle200). A parallel direction may also include a situation in which vehicle800is not traveling in alignment with vehicle200, but is traveling in a direction parallel to the traveling direction of vehicle200(e.g., vehicle800traveling on a front side of vehicle200at an offset distance).

In some embodiments, the object may be a pedestrian who may be standing in the road, or walking or running across the road. The area included in the images may include other objects, such as a road surface, a house, a wall, a sign, etc., which may form a static environment. When an object is not traveling in a direction parallel to the traveling direction of vehicle200, the traveling direction of the object may transverse the traveling direction of vehicle200with an angle that is neither zero (or substantially zero) nor 180 degrees (or substantially 180 degrees). The images may include both moving and/or static objects.

Processing unit110may communicate with braking system230and at least one of image capture devices122and124. For example, processing unit110may receive images from at least one of image capture devices122and124, and may process the images using various methods or modalities to detect one or more times-to-collision of vehicle200with object800. Although two image capture devices122and124are shown inFIG. 8, the use of additional image capture devices (e.g., 3, 4, 5, etc., image capture devices) is consistent with the disclosed embodiments.

FIG. 9is a diagrammatic representation of an exemplary vehicle200including an object detecting and braking system that may be configured to detect an object not traveling in a direction parallel to the traveling direction of vehicle200. The object may be a vehicle900or a pedestrian (not shown). The object detecting and braking system may detect vehicle900traveling toward vehicle200based on one or more images acquired by at least one of image capture devices122and124. In addition, the object detecting and braking system may determine at least two times-to-collision of vehicle200with vehicle900based on the at least one or more images. When a difference between the at least two times-to-collision does not exceed the predetermined threshold, processing unit110may cause vehicle200to brake in order to prevent collision with vehicle900.

FIG. 10is an exemplary block diagram of memory140or150that may store instructions for performing one or more operations for detecting an object in front of vehicle200and causing vehicle200to brake based on the detection. As shown inFIG. 10, memory140or150may store one or more modules for performing the operations, as described herein. For example, memory140or150may store a first image analysis module1002, a second image analysis module1003, and a braking module1004. Application processor180and/or image processor190may execute the instructions stored in any of modules1002-1004included in memory140or150. One of skill in the art will understand that references in the following discussions to processing unit110may refer to application processor180and image processor190individually or collectively. Accordingly, steps of any of the following processes may be performed by one or more processing devices.

First image analysis module1002may store instructions which, when executed by processing unit110, may perform a first image analysis to calculate or generate a first estimated time-to-collision (TTC1) of vehicle200with an object (e.g., vehicle800or900). First image analysis module1002may process at least two images acquired by at least one of image capture devices122and124using, for example, an appearance based detection method, which may calculate TTC1based on a change in the appearance (e.g., a size) of the object in the at least two images. An exemplary first image analysis is discussed below in greater detail with respect toFIG. 12.

Second image analysis module1002may store instructions which, when executed by processing unit110, may perform a second image analysis to calculate or generate a second estimated time-to-collision (TTC2) of vehicle200with the object (e.g., vehicle800or900). Second image analysis module1003may process at least two images acquired by at least one of image capture devices122and124using, for example, an optical flow based method, which may calculate TTC2based on optical flow information derived from the at least two images. An exemplary second image analysis is discussed below in greater detail with respect toFIG. 13.

Braking module1004may store instructions which, when executed by processing unit110, may cause vehicle200to brake using braking system230. For example, when processing unit110determines that a difference between TTC1and TTC2does not exceed the predetermined threshold, braking module1004may cause braking system230to apply braking to reduce the speed of vehicle200or stop vehicle200, thereby preventing collision with the object.

FIG. 11is a flowchart showing an exemplary process or method1100for detecting an object in front of vehicle200and causing vehicle200to brake based on the detection. Method1100may include acquiring a plurality of images of an area in front of vehicle200(step1110). For example, at least one of image capture devices122and124may acquire one or more images of an area in front of vehicle200. The area may include an object, such as another vehicle or a pedestrian. The area may include other static objects, such as a sign, a house, a mountain, a road surface, etc., which may form a static environment.

Method1100may include performing the first image analysis based on at least a first image and a second image to generate a first time-to-collision (TTC1) (step1120). For example, processing unit110may perform the first image analysis based on at least the first image and the second image. More than two images may be used in the first image analysis to generate TTC1. In some embodiments, the first image analysis may include an appearance based detection method. The appearance based detection may detect TTC1based on a change in appearance (e.g., a change in size) of an object in the first image and the second image. The appearance based detection technique is further discussed below in connection withFIG. 12.

FIG. 12is a flowchart showing an exemplary first image analysis1200for determining the first estimated time-to-collision (TTC1). The first image analysis1200may include the appearance or texture based detection method (hence, the first image analysis may be referred to as the texture based analysis or textural analysis). The appearance based detection method may include determining a change in the appearance of an object or a feature of the object in at least two images (e.g., the first image and the second image), and may calculate TTC1based on the change in the appearance. In some embodiments, the change in the appearance may be a change in a size of the object (or the feature of the object).

In the exemplary first image analysis1200shown inFIG. 12, processing unit110may determine a first size of an object (or a feature of the object) appearing in the first image (step1210). Processing unit110may determine a second size of the object (or the feature of the object) appearing in the second image (step1220). The second size may be larger than the first size as vehicle200approaches the object. Processing unit110may determine the first size and the second size based on the dimensions (e.g., pixel sizes) of the object appearing in the first image and the second image. Processing unit110may determine a change in size (or relative scale) of the object (or the feature of the object) (step1230). For example, processing unit110may subtract the first size from the second size to determine a difference in size. In some embodiments, processing unit110may divide the difference in size by the first size to determine a relative difference in size. The change in size (or relative scale) may refer to the relative difference in size, which may be a percentage. Processing unit110may determine TTC1based on the change in size and a first predetermined time interval between times of acquisition of the first image and the second image (step1240). The first predetermined time interval may depend on settings of the camera (e.g., the camera included in the first image capture device122) that acquires the first image and the second image, and may be known once the camera settings are fixed. The first predetermined time interval may be any suitable interval, such as, for example, 0.05 second, 0.1 second, 0.2 second, etc. For example, the first predetermined time interval may be represented by dt1, the relative change in size may be represented by S0, and processing unit110may calculate TTC1from TTC1=dt1/S0.

Referring back toFIG. 11, method1100may include performing a second image analysis based on at least a third image and a fourth image to generate a second estimated time-to-collision (TTC2) (step1130). For example, processing unit110may derive optical flow information from the third image and the fourth image, and may calculate TTC2based on the optical flow information. The second image analysis may be referred to as the structure based analysis or structural analysis. In some embodiments, more than two images may be used in the second image analysis to derive the optical flow information.

FIG. 13is a flowchart showing an exemplary second image analysis1300for determining the second estimated time-to-collision (TTC2). The second image analysis1300may include obtaining or deriving an optical flow field from at least the third image and the fourth image (step1310). In some embodiments, processing unit110may derive the optical flow field from the third image and the fourth image, and/or additional images acquired by at least one of image capture devices122and124. The optical flow field may be a vector field in which each point may be represented by a vector showing a magnitude and a direction of movement from a first point in, e.g., the third image, to a corresponding second point in, e.g., the fourth image.

As shown inFIG. 13, the second image analysis1300may include determining a focus of expansion based on the optical flow field (step1320). The focus of expansion may be a converging point of vectors in the optical flow field, which is discussed below in greater detail with respect toFIG. 16. The second image analysis1300may also include determining a rate of expansion relative to the focus of expansion for each point in the optical flow field (step1330). For example, for each point in the optical flow field, processing unit110may calculate the rate of expansion based on a magnitude of movement of the point, and a distance from the point to the focus of expansion. For example, the magnitude of movement of any point may be represented by d, the distance from the point to the focus of expansion may be represented by r, and processing unit110may calculate the rate of expansion from Rexp=d/r. For each point in the optical flow field, the magnitude of movement r may be calculated from the magnitude of the vector associated with that point. The magnitude of movement r may indicate how much a first point in, e.g., the third image, has moved to reach a second point in, e.g., the fourth image.

As shown inFIG. 13, the second image analysis1300may include determining the second estimated time-to-collision (TTC2) based on the rate of expansion and a second predetermined time interval. The second predetermined time interval may be the difference between times of acquisition of the third image and the fourth image. The second predetermined time interval dt2may depend on the settings (e.g., the frame rate) of the camera, and may or may not be the same as the first predetermined time interval used in the first image analysis. The second predetermined time interval may be any suitable interval, such as, for example, 0.05 second, 0.1 second, 0.2 second, etc. In some embodiments, TTC2may be inversely proportional to the rate of expansion d/r, and may be directly proportional to the second predetermined time interval dt2. For example, processing unit110may calculate TTC2from TTC2=dt2/(d/r).

The first image analysis1200and second image analysis1300shown inFIGS. 12 and 13may be performed independently and separately. The first image and second image used in the first image analysis1200may be the same as or different from the third image and fourth image used in the second image analysis1300. In some embodiments, the first image may be the same as the third image, and the second image may be the same as the fourth image. In some embodiments, the first image may be different from the third image, and the second image may be different from the fourth image. In some embodiments, images of alternating sequences may be used for the first and second analyses. For example, from the same set of images, odd numbered images may be used for the first image analysis, and even numbered images may be used for the second image analysis. In some embodiments, at least one image capture device (e.g., image capture device122) may be configured to capture images (e.g., the first and second images) for use in the first image analysis to generate the first estimated time-to-collision. At least one other image capture device (e.g., image capture device124) may be configured to capture images (e.g., the third and fourth images) for use in the second image analysis to generate the second estimated time-to-collision. In some embodiments, when at least one image capture device is used for capturing a plurality of images for the first image analysis and the second image analysis, the at least one image capture device may include a first image sensor configured to capture images (e.g., the first and second images) for use in the first image analysis, and a second image sensor that is separate and independent of the first image sensor configured to capture images (e.g., the third and fourth images) for use in the second image analysis.

Referring back toFIG. 11, method1100may include calculating a difference between the first estimated time-to-collision (TTC1) and the second estimated time-to-collision (TTC2) (step1140). In some embodiments, processing unit110may subtract TTC1from TTC2to result in a difference. Processing unit110may determine whether the difference exceed a predetermined threshold (step1150). The predetermined threshold may be any suitable threshold, such as, for example, 0.05 second, 0.1 second, 0.2 second, etc. When processing unit110determines that the difference exceeds the predetermined threshold (Yes, step1150), processing unit110may repeat steps1110-1150. When processing unit110determines that the difference does not exceed the predetermined threshold (No, step1150), processing unit110may cause vehicle200to brake using, e.g., braking system230(step1160).

FIGS. 14A-14Cshow an image with an optical flow field and a distribution of the second estimated time-to-collision (TTC2) in the optical flow field.FIG. 14Ashows an image1400showing a vehicle1410traveling on a road1420. For illustrative purposes, other objects included in image1400are not shown.FIG. 14Bshows an optical flow field1430derived based on at least two images. For example, optical flow field1430may be derived from image1400and at least one second image (not shown) of vehicle1410acquired at the second predetermined time interval dt2apart from the time when image1400was acquired. A plurality of image points may be selected in the image1400or the at least one second image, and vectors connecting the corresponding points of these images may be generated by processing unit110. The vectors may form optical flow field1430, which is schematically shown inFIG. 14B, as a region surrounding vehicle1410.

Although optical flow field1430is shown within a region surrounding vehicle1410for illustrative purposes, optical flow field1430may be derived for a smaller region (e.g., focusing on the window of vehicle1430) or a greater region (e.g., the entire area shown in image1400). Each point in optical flow field1430may be associated with a vector represented inFIG. 14Bby an arrow having a length and pointing at a direction. The length of the arrow may represent a magnitude of the vector. For example, the magnitude of the vector may indicate a magnitude of movement of a first point in image1400to a second point in the at least one second image. The direction of the arrow may indicate the direction of movement between each pair of corresponding points in these two images.

After performing the second image analysis1300discussed above, processing unit110may determine the second estimated time-to-collision (TTC2) for each point in the optical flow field1430.FIG. 14Cshows an exemplary distribution of TTC2over the entire area shown in image1400. As shown inFIG. 14C, points in different regions may be associated with different values of TTC2. The different patterns and/or shades shown inFIG. 14Care shown to schematically illustrate different values of TTC2. For a region1440substantially focusing on the body of vehicle1410, all or most of the points within region1440may be associated with substantially the same value of TTC2. In other words, for an upright moving object like vehicle1410, all or most of the points on that upright object may have the same or substantially the same times-to-collision.

For road surfaces, however, all or most of the points may not have the same or substantially the same times-to-collision. For example, points in a first portion1450of road1420may be associated with one time-to-collision, and points in a second portion1460of road1420may be associated with another time-to-collision. These times-to-collision may be different, as shown with different patterns/shades inFIG. 14C. In some embodiments, there may be a discontinuity between the times-to-collision for the first portion1450and second portion1460of road1420(e.g., the changes between these times-to-collision may not be gradual). Although the first and second portions1450and1460appear as upright objects in a single image, the optical flow field generated from at least two images may reveal additional information (e.g., time-to-collision) that may distinguish a true upright object (which may or may not be moving) and a road surface, which is not upright. This aspect is further discussed with respect toFIGS. 17A-18B.

As shown inFIG. 14C, points in regions1470,1480, and1490may be associated with different times-to-collision, as indicated by the different patterns/shading shown in the figure. Regions1470and1480may each represent a portion of road1420, and the change in time-to-collision values in regions1470and1480and first portion1450may be gradual (e.g., continuous). For example, region1490may represent a portion of the sky, which may be associated with a time-to-collision that is different from those associated with regions1470and/or1480.

FIGS. 15A and 15Bshow exemplary focuses of expansion in an optical flow field.FIG. 15Ashows an object in an image traveling in the same direction (or substantially the same direction) as and in alignment (or substantially in alignment) with vehicle200(e.g., in the same line of movement in front of vehicle200, toward or away from vehicle200). As shown inFIG. 15A, a focus of expansion of the object may overlap completely or substantially with a focus of expansion of the static environment.FIG. 15Bshows an object not traveling in a direction parallel to the traveling direction of vehicle200, e.g., when the object is traveling laterally in a direction that transverses the traveling direction of vehicle200. As shown in FIG.15B, a focus of expansion of the object may not overlap the focus of expansion of the static environment.

For example,FIG. 15Ashows an environment1500of an area in front of vehicle200. Consistent with disclosed embodiments, an image capture device (e.g., one of image capture devices122-126) may acquire a first image1510of environment1500that includes an object located within the first image1510. The area surrounding the object may represent a static environment (e.g., roads, buildings, mountains, sky, etc.).FIG. 15Aalso shows a second image1520that an image capture device (e.g., one of image capture devices122-126) may acquire of the object after the second predetermined time interval dt2. Image1520may be acquired as vehicle200moves toward the object and, therefore, may be larger than image1510, indicating an expansion.

FIG. 15Afurther shows a focus of expansion1530. The focus of expansion1530may be a focus of expansion of the static environment, as indicated by two dashed lines converging to focus of expansion point1530. The location of focus of expansion1530of the static environment may depend on the camera, and may not change over time as the camera continuously acquires a plurality of images of the object or area for deriving optical flow information. When the object is traveling in the same (or substantially the same) direction as and in (or substantially in) alignment with vehicle200, e.g., in front of vehicle200and in the same or opposite traveling direction as vehicle200, the focus of expansion1530may also constitute a focus of expansion of the object. In other words, the focus of expansion of the object traveling in the same direction as and in alignment with vehicle200may completely or substantially overlap (or completely or substantially converge with) the focus of expansion of the static environment. Thus, processing unit110may detect one focus of expansion from the optical flow field derived from a plurality of images.FIG. 15Aalso shows, for illustrative purpose, the distance r from certain points to the focus of expansion.

FIG. 15Bshows an environment1540of an area in front of vehicle200, and a first image1550and a second image1560of an object (the object being included in the first image1550and second image1560) acquired by the camera one after another at the second predetermined time interval dt2. For example, as vehicle200moves toward the object, the image of the object may expand, e.g., from image1550to image1560.FIG. 15Bfurther shows a first focus of expansion1570that may be calculated from the optical flow field derived from image1550and image1560of the object. For illustrative purposes,FIG. 15Bshows the distances r from certain points to the first focus of expansion1570.FIG. 15Balso shows a second focus of expansion1580, which may be a focus of expansion of the static environment. The location of the second focus of expansion1580may depend on the camera, and may not change as the camera continuously acquires images of the object or area for deriving the optical flow information. In the example shown inFIG. 15B, the first focus of expansion1570may not overlap the second focus of expansion1580. The detection of two or at least two non overlapping focuses of expansion may indicate that there is at least one object in the image1540that is traveling laterally, e.g., in a direction that is not parallel to the traveling direction of vehicle200, or in a direction that transverses the traveling direction of vehicle200at an angle that is neither zero (or substantially zero) nor 180 degrees (or substantially 180 degrees).

FIG. 16shows an exemplary optical flow field and a focus of expansion. The following discussion ofFIG. 16illustrates how an optical flow field may be derived and how a focus of expansion may be determined. For example, vehicle1600is an object included in an image acquired by, e.g., image capture device122installed on vehicle200, and vehicle200is traveling toward vehicle1600. Image capture device122may sequentially and/or continuously acquire a plurality of images of an area in front of vehicle200as vehicle200approaches vehicle1600. Any two or more images may be selected from the plurality of images to derive an optical flow field. InFIG. 16, the first image is represented by vehicle1600shown in solid lines, and the second image is represented by vehicle1610shown in dashed lines. For a plurality of selected points on the first image, e.g., points (x1, y1), (x2, y2), and (x3, y3), processing unit110may determine their corresponding points, e.g., (u1, v1), (u2, v2), and (u3, v3) in the second image. Vectors may be determined for each pair of points (x1, y1) and (u1, v1), (x2, y2) and (u2, v2), and (x3, y3) and (u3, v3). The vectors may indicate a magnitude and direction of expansion for the points in the first image. The length of each vector (represented by a solid arrow inFIG. 16) may indicate the distance or magnitude of movement of from one point in the first image to a corresponding point in the second image, and the direction of the vector may indicate the expansion direction or direction of movement. The converging point1620of the vectors may represent the focus of expansion. In some embodiments, the location of the converging point1620may be calculated using a suitable mathematical model based on the coordinates of the vectors. For example, the location of converging point1620may be calculated from:
SS*(xi−FOEx)=ui(1)
SS*(yi−FOEy)=vi(2)

Where SS may be a constant, (xi, yi) may be coordinates of the i-th point within a first image, and (ui, vi) may be coordinates of the corresponding i-th point within the second image. FOExand FOEymay be coordinates representing the location of converging point1620(i.e., focus of expansion).

FIGS. 17A and 17Bshow an image of an object and a linear relationship between distances from points in an optical flow field to the focus of expansion and magnitudes of movement of the points.FIGS. 17A and 17Bshow that a linear relationship between distances from points to the focus of expansion and magnitudes of movement of the points may be used to determine that the object is an upright object (e.g., a low time-to-collision upright object).

FIG. 17Ashows an image including an object, i.e., a vehicle1700. The image of vehicle1700may include an image of two car windows1710and1720, which appear to be darker in the image of vehicle1700. A plurality of points1730may be selected, most of which may be from a region in the image of vehicle1700. An optical flow field may be derived from at least two images of vehicle1700acquired one after another with the second predetermined time interval dt2. Processing unit110may determine the magnitudes of movement d and the distances r from the points to the focus of expansion based on the optical flow field. Processing unit110may determine a relationship between the magnitudes d and distances r. In some embodiments, the relationship may be a linear relationship, as shown inFIG. 17B, indicating that d/r (e.g., slope of the plotted line) is the same for all or substantially all of the points in the optical flow field. As discussed above, d/r may be inversely proportional to the second estimated time-to-collision (TTC2). Thus, a linear relationship between d and r may indicate a constant or substantially constant value of d/r, which in turn may indicate a constant or substantially constant TTC2for all or substantially all of the points in the optical flow field. The constant or same TTC2may indicate that the object within the optical flow field is an upright object, or a low time-to-collision upright object.

To illustrate the linear relationship between magnitudes of movement d and distances r,FIG. 17Bshows a plot of the vertical components (y components) related to the magnitudes of movement d (e.g., the magnitude of movement in the y direction, “dy” inFIG. 17B) versus vertical components of distances r (e.g., the distance to the focus of expansion in the y direction, “y” inFIG. 17B). Although not shown inFIG. 17B, a plot of the horizontal components, e.g., magnitudes in the x direction and distances in the x direction, may also indicate a linear relationship, which in turn may indicate a constant or substantially constant time-to-collision for all or substantially all of the selected points in the optical flow field, addition, a plot of distances r and magnitudes d may also indicate a linear relationship, which in turn may indicate a constant (or same) or substantially constant (or same) time-to-collision for all or substantially all of the selected points in the optical flow field.

When a non-linear relationship between the magnitudes of movement d and the distances r to the focus of expansion is detected or determined, the non-linear relationship may indicate that the second estimated times-to-collision are different for different points included in the optical flow field, which in turn may indicate that the image may include a road surface, rather than an upright object. As discussed above, the second estimated times-to-collision may be determined based on the optical flow information derived from at least two images.FIGS. 18A and 18Bshow an image of an object and a non-linear relationship between distances r from points in an optical flow field to the focus of expansion and magnitudes d of movement of the points.FIG. 18Ashows an image1800of a house1810having two garages1820and1830with garage doors opened, and a driveway1840(e.g., a road surface) leading up to the garages1820and1830. Because the garage doors are opened, garages1820and1830may appear darker on the image, similar to the car windows1710and1720shown inFIG. 17A. A region1850of the image may be selected for image processing. A plurality of points may be selected within region1850, and at least two images including regions corresponding to region1850may be acquired to derive an optical flow field. Based on the optical flow field having a plurality of vectors, processing unit110may determine a focus of expansion, the distances r of the points to the focus of expansion, and the magnitudes d of movement of the points included in the optical flow field. Processing unit110may determine a relationship between the distances r and the magnitudes d. In the situation shown inFIG. 18A, processing unit110may determine that the relationship between the distances r and the magnitudes d is non-linear. The non-linear relationship between the distances r and the magnitudes d may indicate that the times-to-collision for the points are different for different points. This in turn may indicate that selected region1850includes an image of a road surface, rather than an upright object like vehicle1701) shown inFIG. 17A.

To illustrate the non-linear relationship between magnitudes of movement d and distances r,FIG. 18Bshows a plot of the vertical components (y components) related to the magnitudes d of movement (e.g., the magnitude of movement in the y direction, “dy” inFIG. 18B) versus vertical components of distances r (e.g., the distance to the focus of expansion in the y direction, “y” inFIG. 18B). Although not shown inFIG. 18B, a plot of the horizontal components, e.g., magnitudes in the x direction and distances in the x direction, may also indicate a nonlinear relationship, and hence, different times-to-collision for the selected points in the optical flow field. In addition, a plot of distances r and magnitudes d may also indicate a nonlinear relationship, and hence, different times-to-collision for the selected points in the optical flow field. Here, the image in the region1850may appear similar to the image of vehicle1700shown inFIG. 17Ain a single image. The optical flow field derived from a plurality of images acquired at the second predetermined time interval dt2may reveal additional information, e.g., time-to-collision information, about the objects included in the selected region. The time-to-collision information may be used to determine whether the object is an upright object or a road surface. Processing unit110may combine this determination with the calculated TTC2to determine whether or not to cause vehicle200to brake.

FIG. 19is a flowchart showing an exemplary process1900for triggering a warning based on one or more probabilities indicating that a time-to-collision is smaller than one or more thresholds. Instead of determining the time-to-collision as discussed above, processing unit110may determine, based on at least two images acquired by one or more image capture devices122,124, and126, a probability that the time-to-collision is smaller than a predetermined threshold. For example, in process1900, one or more image capture devices122-126may acquire a plurality of images of an area in front of vehicle200(step1910). In some embodiments, one image capture device having one camera may be used. In some embodiments, two or more image capture devices have two or more cameras may be used. For example, one camera may have a 45-degree field of view of the area in front of vehicle200, and another camera may have a 30-degree field of view.

In some embodiments, processing unit110may perform a third image analysis based on at least two images to determine a first probability (P1) that a time-to-collision (TTC) is smaller than a first predetermined threshold T1(i.e., TTC<T1) (step1920). The first predetermined threshold T1may be any suitable value, such as, for example, 0.1 second, 0.2 second, 0.5 second, etc. Processing unit110may determine a second probability (P2) that the time-to-collision (TTC) is smaller than a second predetermined threshold T2(i.e., TTC<T2) (step1930). The second predetermined threshold T2may be any suitable value, such as, for example, 0.1 second, 0.2 second, 0.5 second, etc. The second predetermined threshold T2may or may not be the same as the first predetermined threshold T1.

Processing unit110may trigger a warning if the first probability P1is greater than a first predetermined probability threshold Tp1, or if the second probability P2is greater than a second predetermined probability threshold Tp2. Tp1and Tp2may be any suitable value, such as, for example, 0.5, 0.8, 0.9, etc. Tp1and Tp2may or may not be the same. In some embodiments, Tp2may be smaller than Tp1. Processing unit110may compare the first probability P1with the first predetermined probability threshold Tp1to determine whether the first probability P1is greater than the first predetermined probability threshold Tp1. Processing unit110may also compare the second probability P2with the second predetermined probability threshold Tp2to determine whether the second probability P2is greater than the second predetermined probability threshold Tp2. The warning may include an audio alert, a video alert, a vibrational alert, or a combination thereof. The warning may alert the driver so that the driver may take suitable navigational action, such as applying brakes, steering the wheel, etc.

FIG. 20is a flowchart showing an exemplary process2000for determining the first time-to-collision (TTC1) that may be implemented in the first image analysis shown inFIG. 11. Process2000may be used in the first image analysis as an alternative texture based TTC1calculation method replacing the process1200shown inFIG. 12. Processing unit110may process at least two images that are acquired at a predetermined time interval DT. Processing unit110may determine a first dimension length y1of an object in a first image (step2010). The first dimension length of the object may be a length of a feature of the object in the vertical direction, horizontal direction, or in any direction on the first image. For example, when the object is a vehicle, the feature may be a vertical side of a license plate on the vehicle. Processing unit110may determine a second dimension length y2of the same object (or the same feature of the same object) in a second image, which may be acquired after the first image at a time that is DT after the time the first image is acquired (step2020). Processing unit110may determine a scaling factor Sf based on the first dimension length and the second dimension length. For example, processing unit110may calculate Sf based on Sf=y1/y2. In some embodiments, processing unit may track the horizontal and vertical coordinates (dx, dy) of the object in the first and second image, and may calculate Sf based on the coordinates (dx, dy), for example, Sf=dy1/dy2.

Processing unit110may determine a time-to-collision (which may be the first time-to-collision shown inFIG. 11) based on the scaling factor and the time interval DT. In some embodiments, processing unit110may calculate TTC1from TTC1=DT/(Sf−1).

Using two, i.e., texture based and structure based, sensing modalities, methods, or models, as described above, have several benefits or advantages. For example, an immediate result of using two modalities in a braking decisioning system is a reduction of the probability of system failure may be achieved, as compared to braking decisioning systems using only one of the modalities (or only one information channel).

Another benefit relates to system validation. Using at least two modalities (or channels of information, or two independent and/or separate analyses) enable the vehicle to assess the overall system failure rate (e.g., mean-time-between-failures or MTBF). The overall system failure rate may be determined by assessing each modality's failure rate separately (e.g., by assessing the failure rate of the texture based analysis and the failure rate of the structure based analysis separately). The two modalities may be implemented in two sub-systems: a textural analysis based sub-system and a structural analysis based sub-system. The failure rates of the two modalities may be multiplied. A result of the multiplication may be used as an indicator of or may be used to compute the overall system failure rate. In some embodiments, the MTBF may be estimated based on the result of the multiplication, even if it is significantly higher than the extent (in time) of available validation data.

For example, in one scenario, a record of only 1000 hours of data is available, and the combined systems MTBF is 1000000 hours. In 1/100 hours the texture based analysis may trigger a false braking decision. In 1/10000 of the cases the texture based analysis is checking the hypothesized decision (e.g., the hypothesized decision being a decision to brake because of a low TTC upright object in front of vehicle200). In such cases, the texture based analysis may falsely approve the hypothesized decision. By direct assessment of the combined system (or combined analyses of the texture based analysis and the structure based analysis), vehicle systems may only achieve 0 failure over 1000 hours, and may not be able to assess the MTBF. By taking two independent channels of information into account in the braking decisioning for vehicle200, e.g., using the texture based analysis and the structure based analysis, a vehicle system may be able to assess the failure rate of each sub-system (e.g., textural analysis based sub-system and structural analysis based sub-system) separately. Such failure rates may be multiplied, and a result of the multiplication may be used to assess the MTBF. For example, in some embodiments, the textural analysis based sub-system may be keep running alone and it may fail 10 times over 1000 hours. If the structural analysis based sub-system is falsely provoked 1000000 times, as if, for example, the textural analysis based sub-system reached a decision that there is a need to issue an autonomous emergency braking on an open road, the structural analysis based sub-system may fail once in 10000 provocations. Based on these observations, processing unit110may deduce (or determine) that the total system mean-time-between failures (MTBFs) is indeed 1000000 hours. This estimate may be reached using only 1000 hours of recorded data. Thus, by using the disclosed two modalities, system validation may be more efficiently performed based on only limited hours of recorded data.

FIG. 21is a flowchart showing an exemplary process2100for performing system validation using two modalities. Process2100summarizes the system validation discussed above. Processing unit110may determine a first failure rate associated with the textural analysis based sub-system (step2110). For example, processing unit110may determine the first failure rate based on the total number of times the textural analysis based sub-system makes a false braking decision (e.g., falsely detected a low TTC upright object) among a total number of system running hours. As discussed above, the textural analysis based sub-system may make a decision to brake based on detection of a change in a texture (e.g., a change in footprint size) between at least two images of an area in front of vehicle200. Processing unit110may determine a second failure rate associated with the structural analysis based sub-system (step2120). For example, processing unit110may determine the second failure rate based on the total number of times the structural analysis based sub-system makes a false braking decision (e.g., falsely detected a low TTC upright object) among the same total number of system running hours or a different total number of system running hours. Processing unit110may determine an overall system failure rate based on the first failure rate and the second failure rate (step2130). For example, processing unit110may multiply the first failure rate and the second failure rate, and use a result of the multiplication as an indicator of overall system mean-time-between-failures (MTBF).

The foregoing description has been presented for purposes of illustration. It is not exhaustive and is not limited to the precise forms or embodiments disclosed. Modifications and adaptations will be apparent to those skilled in the art from consideration of the specification and practice of the disclosed embodiments. Additionally, although aspects of the disclosed embodiments are described as being stored in memory, one skilled in the art will appreciate that these aspects can also be stored on other types of computer readable media, such as secondary storage devices, for example, hard disks or CD ROM, or other forms of RAM or ROM, USB media, DVD, Blu-ray, or other optical drive media.