Abstract: The present disclosure is directed to an autonomous vehicle having a vehicle control system. The vehicle control system includes an image processing system. The image processing system receives an image that includes a light indicator. The light indicator includes an illuminated component. The image processing system determines a color of the illuminated component of the light indicator and an associated confidence level of the determination of the color of the illuminated component. The image processing system also determines a shape of the illuminated component of the light indicator and an associated confidence level of the determination of the shape of the illuminated component. The determined confidence levels represent an estimated accuracy of the determinations of the shape and color. Additionally, the image processing system provides instructions executable by a computing device to control the autonomous vehicle based on at least one of the determined confidence levels exceeding a threshold value.

Abstract: A system and method is provided for detecting and responding to emergency vehicles. In one aspect, one or more computing devices may identify a set of light sources from an image based at least in part on one or more templates, and may filter the set of light sources in order to identify one or more light sources corresponding to a potential emergency vehicle. Moreover, the one or more computing devices may determine whether any of the one or more light sources is flashing and whether any of the one or more light sources is associated with a particular type of the potential emergency vehicle. Further, the one or more computing devices may maneuver a vehicle based on the determination to yield in response to at least one of the one or more flashing light sources and the particular type of the emergency vehicle.

Abstract: Aspects of the disclosure relate to identifying construction objects. As an example, an image captured by a camera associated with a vehicle as the vehicle is driven along a roadway may be received. This image may be converted into a first channel corresponding to an average brightness contribution from red, blue and green channels of the image. The image may also be converted into a second channel corresponding to a contribution of a color from the red and the green channels of the image. A template may then be used to identify a region of the image corresponding to a potential construction object from the first channel and the second channel.

Abstract: Aspects of the disclosure relate generally to speed control in an autonomous vehicle. For example, an autonomous vehicle may include a user interface which allows the driver to input speed preferences. These preferences may include the maximum speed above the speed limit the user would like the autonomous vehicle to drive when other vehicles are present and driving above or below certain speeds. The other vehicles may be in adjacent or the same lane the vehicle, and need not be in front of the vehicle.

Abstract: Methods and systems are provided that may allow an autonomous vehicle to discern a school bus from image data. An example method may include receiving image data indicative of a vehicles operating in an environment. The image data may depict sizes of the vehicles. The method may also include, based on relative sizes of the vehicles, determining a vehicle that is larger in size as compared the other vehicles. The method may additionally include comparing a size of the determined vehicle to a size of a school bus and based on the size of vehicle being within a threshold size of the school bus, comparing a color of the vehicle to a color of the school bus. The method may further include based on the vehicle being substantially the same color as the school bus, determining that the vehicle is representative of the school bus.

Abstract: A vehicle is provided that may distinguish between dynamic obstacles and static obstacles. Given a detector for a class of static obstacles or objects, the vehicle may receive sensor data indicative of an environment of the vehicle. When a possible object is detected in a single frame, a location of the object and a time of observation of the object may be compared to previous observations. Based on the object being observed a threshold number of times, in substantially the same location, and within some window of time, the vehicle may accurately detect the presence of the object and reduce any false detections.

Abstract: A vehicle configured to operate in an autonomous mode could determine a current state of the vehicle and the current state of the environment of the vehicle. The environment of the vehicle includes at least one other vehicle. A predicted behavior of the at least one other vehicle could be determined based on the current state of the vehicle and the current state of the environment of the vehicle. A confidence level could also be determined based on the predicted behavior, the current state of the vehicle, and the current state of the environment of the vehicle. In some embodiments, the confidence level may be related to the likelihood of the at least one other vehicle to perform the predicted behavior. The vehicle in the autonomous mode could be controlled based on the predicted behavior, the confidence level, and the current state of the vehicle and its environment.

Abstract: Aspects of the disclosure relate to determining whether a feature of map information. For example, data identifying an object detected in a vehicle's environment and including location coordinates is received. This information is used to identify a corresponding feature from pre-stored map information based on a map location of the corresponding feature. The corresponding feature is defined as a curve and associated with a tag identifying a type of the corresponding object. A tolerance constraint is identified based on the tag. The curve is divided into two or more line segments. Each line segment has a first position. The first position of a line segment is changed in order to determine a second position based on the location coordinates and the tolerance constraint. A value is determined based on a comparison of the first position to the second position. This value indicates a likelihood that the corresponding feature has changed.

Abstract: An autonomous vehicle is configured to detect an active turn signal indicator on another vehicle. An image-capture device of the autonomous vehicle captures an image of a field of view of the autonomous vehicle. The autonomous vehicle captures the image with a short exposure to emphasize objects having brightness above a threshold. Additionally, a bounding area for a second vehicle located within the image is determined. The autonomous vehicle identifies a group of pixels within the bounding area based on a first color of the group of pixels. The autonomous vehicle also calculates an oscillation of an intensity of the group of pixels. Based on the oscillation of the intensity, the autonomous vehicle determines a likelihood that the second vehicle has a first active turn signal. Additionally, the autonomous vehicle is controlled based at least on the likelihood that the second vehicle has a first active turn signal.

Abstract: Methods and systems for real-time road flare detection using templates and appropriate color spaces are described. A computing device of a vehicle may be configured to receive an image of an environment of the vehicle. The computing device may be configured to identify a given pixels in the plurality of pixels having one or more of: (i) a red color value greater than a green color value, and (ii) the red color value greater than a blue color value. Further, the computing device may be configured to make a comparison between one or more characteristics of a shape of an object represented by the given pixels in the image and corresponding one or more characteristics of a predetermined shape of a road flare; and determine a likelihood that the object represents the road flare.

Abstract: Disclosed herein are methods and apparatus for controlling autonomous vehicles utilizing maps that include visibility information. A map is stored at a computing device associated with a vehicle. The vehicle is configured to operate in an autonomous mode that supports a plurality of driving behaviors. The map includes information about a plurality of roads, a plurality of features, and visibility information for at least a first feature in the plurality of features. The computing device queries the map for visibility information for the first feature at a first position. The computing device, in response to querying the map, receives the visibility information for the first feature at the first position. The computing device selects a driving behavior for the vehicle based on the visibility information. The computing device controls the vehicle in accordance with the selected driving behavior.

Abstract: Methods and systems are provided that may allow an autonomous vehicle to discern a school bus from image data. An example method may include receiving image data indicative of a vehicles operating in an environment. The image data may depict sizes of the vehicles. The method may also include, based on relative sizes of the vehicles, determining a vehicle that is larger in size as compared the other vehicles. The method may additionally include comparing a size of the determined vehicle to a size of a school bus and based on the size of vehicle being within a threshold size of the school bus, comparing a color of the vehicle to a color of the school bus. The method may further include based on the vehicle being substantially the same color as the school bus, determining that the vehicle is representative of the school bus.

Abstract: An autonomous vehicle may be configured to receive, using a computer system, a plurality of remission signals from a portion of a lane of travel in an environment in response to at least one sensor of the vehicle sensing the portion of the lane of travel. A given remission signal of the plurality of remission signals may include a remission value indicative of a level of reflectiveness for the portion of the lane of travel. The vehicle may also be configured to compare the plurality of remission signals to a known remission value indicative of a level of reflectiveness for a lane marker in the lane of travel. Based on the comparison, the vehicle may additionally be configured to determine whether the portion of the lane of travel in the environment is indicative of a presence of the lane marker.

Abstract: Aspects of the disclosure relate to detecting and responding to objects in a vehicle's environment. For example, an object may be identified in a vehicle's environment, the object having a heading and location. A set of possible actions for the object may be generated using map information describing the vehicle's environment and the heading and location of the object. A set of possible future trajectories of the object may be generated based on the set of possible actions. A likelihood value of each trajectory of the set of possible future trajectories may be determined based on contextual information including a status of the detected object. A final future trajectory is determined based on the determined likelihood value for each trajectory of the set of possible future trajectories. The vehicle is then maneuvered in order to avoid the final future trajectory and the object.

Abstract: A vehicle is provided that may combine multiple estimates of an environment into a consolidated estimate. The vehicle may receive first data indicative of the region of interest in an environment from a sensor of the vehicle. The first data may include a first accuracy value and a first estimate of the region of interest. The vehicle may also receive second data indicative of the region of interest in the environment, and the second data may include a second accuracy value and a second estimate of the region of interest. Based on the first data and the second data, the vehicle may combine the first estimate of the region of interest and the second estimate of the region of interest.

Abstract: Aspects of the disclosure relate generally to detecting discrete actions by traveling vehicles. The features described improve the safety, use, driver experience, and performance of autonomously controlled vehicles by performing a behavior analysis on mobile objects in the vicinity of an autonomous vehicle. Specifically, an autonomous vehicle is capable of detecting and tracking nearby vehicles and is able to determine when these nearby vehicles have performed actions of interest by comparing their tracked movements with map data.

Abstract: A method is provided that includes receiving user input identifying a travel destination for a first vehicle, determining, by a processor, a first route for the first vehicle to follow, and configuring the first vehicle to follow the first route. The method further includes obtaining a model for a second vehicle that shares a road with the first vehicle and comparing model to a pre-determined template for a vehicle that is known to be a special purpose vehicle in order to determine whether the first template and the second template match. The method further includes determining, by the processor, a second route that leads to the travel destination, when a match is found to exist, and switching the first vehicle from following the first route to following the second route.

Abstract: The present disclosure is directed to autonomous vehicle having a vehicle control system. The vehicle control system includes a processing system that receives input values that indicate attributes of an object within a threshold distance of the autonomous vehicle and variance values indicating uncertainty associated with the input values. The processing system also provides a plurality of outcomes that are associated with combinations of split decisions. A given split decision indicates whether a particular input value is above or below a threshold value associated with the given split decision. The processing system further determines (i) a probability that the particular input value is above a threshold value and (ii) a probability that the particular input is below the threshold value for a given split decision. Additionally, the processing system determines one or more likelihoods associated with a given outcome. Further, the processing system provides instructions to control the autonomous vehicle.

Abstract: Methods and systems for object and ground segmentation from a sparse one-dimensional range data are described. A computing device may be configured to receive scan data representing points in an environment of a vehicle. The computing device may be configured to determine if a test point in the scan data is likely to be an obstacle or ground by comparing the point to other points in the scan data to determine if specific constraints are violated. Points that do not pass these tests are likely to be above the ground, and therefore likely belong to obstacles.

Abstract: Methods and systems for detecting a vehicle signal through image differencing and filtering are described. A computing device may be configured to receive a sequence of images of an identified vehicle in a vicinity of a given vehicle. The computing device may be configured to determine, based on a comparison of a first image of a pair of images of the sequence of images to a second image of the pair of images, a portion of image data exhibiting a change in color and a change in brightness between the first image and the second image of the pair of images. The computing device may be configured to determine that the portion indicates a light signal for the identified vehicle; and provide instructions to control the given vehicle based on the light signal of the identified vehicle.