Abstract: A roof mounted sensor pod assembly for a vehicle. the roof mounted sensor pod having a frame configured to attach to the vehicle, a connecting assembly located on the frame, and a sensor pod configured to couple to the frame with the connecting assembly. The connecting assembly is configured to support the weight of the sensor pod during installation of the sensor pod and prior to securing of the sensor pod to the frame. A vehicle includes a roof mounted sensor pod assembly.

Abstract: A roof mounted sensor pod assembly for a vehicle. The roof mounted sensor pod assembly having a bracket comprising a first distal side surface and a second distal side surface, the first distal side surface configured to attach to the vehicle, a connecting assembly located at the second distal side surface of the bracket, and a sensor pod configured to couple to the second distal side surface of the bracket, with the connecting assembly. The connecting assembly is configured to support the weight of the sensor pod during installation of the sensor pod and prior to securing of the sensor pod to the bracket. A vehicle includes a roof mounted sensor pod assembly.

Abstract: A method of installing a sensor pod on a roof of a vehicle includes providing a connecting assembly having a vehicle portion configured to couple to the vehicle and a sensor pod portion attached to the sensor pod, installing the vehicle portion of the connecting assembly on the vehicle, coupling the sensor pod portion of the connecting assembly to the vehicle portion, after coupling the sensor pod portion to the vehicle portion, supporting the weight of the sensor pod with the connecting assembly, and securing the sensor pod to the vehicle portion of the connecting assembly. The connecting assembly supports the weight of the sensor pod before and after securing the sensor pod to the vehicle portion. A method of uninstalling a sensor pod is also provided.

Abstract: A roof mounted sensor pod assembly for a vehicle having a sensor pod configured to couple to the vehicle and a connecting assembly. The connecting assembly having a vehicle portion integral with the vehicle and a sensor pod portion attached to the sensor pod and removably coupled to the vehicle portion. The connecting assembly is configured to allow the sensor pod to be removably coupled to the vehicle. A vehicle includes a roof mounted sensor pod assembly.

Abstract: Aspects of the disclosure relate to determining and responding to an internal state of a self-driving vehicle. For instance, an image of an interior of the vehicle captured by a camera mounted in the vehicle is received. The image is processed in order to identify one or more visible markers at predetermined locations within the vehicle. The internal state of the vehicle is determined based on the identified one or more visible markers. A responsive action is identified action using the determined internal state, and the vehicle is controlled in order to perform the responsive action.

Abstract: A method for two-way communication includes providing a vehicle with a sensor pod. The method includes initiating two-way communication between a local user and a remote user via the sensor pod.

Abstract: An autonomous vehicle includes a sensor pod having a mirror, a connecting assembly extending between the sensor pod and the autonomous vehicle, and a user interface provided on the sensor pod. The user interface is configured to provide two-way communication between a local user and a remote user.

Abstract: Example implementations may relate to sun-aware vehicle routing. In particular, a computing system of a vehicle may determine an expected position of the sun relative to a geographic area. Based on the expected position, the computing system may make a determination that travel of the vehicle through certain location(s) within the geographic area is expected to result in the sun being proximate to an object within a field of view of the vehicle's image capture device. Responsively, the computing system may generate a route for the vehicle in the geographic area based at least on the route avoiding travel of the vehicle through these certain location(s), and may then operate the vehicle to travel in accordance with the generated route. Ultimately, this may help reduce or prevent situations where quality of image(s) degrades due to sunlight, which may allow for use of these image(s) as basis for operating the vehicle.

Abstract: The disclosure relates to assessing operation of a camera. In one instance, a volume of space corresponding to a first vehicle in an environment of a second vehicle may be identified using sensor data generated by a LIDAR system of the second vehicle. An image captured by a camera of the second vehicle may be identified. The camera may have an overlapping field of view of the LIDAR system at a time when the sensor data was generated. An area of the image corresponding to the volume of space may be identified and processed in order to identify a vehicle light. The operation of the camera may be assessed based on the processing.

Abstract: The technology relates to partially redundant equipment architectures for vehicles able to operate in an autonomous driving mode. Aspects of the technology employ fallback configurations, such as two or more fallback sensor configurations that provide some minimum amount of field of view (FOV) around the vehicle. For instance, different sensor arrangements are logically associated with different operating domains of the vehicle. Fallback configurations for computing resources and/or power resources are also provided. Each fallback configuration may have different reasons for being triggered, and may result in different types of fallback modes of operation. Triggering conditions may relate, e.g., to a type of failure, fault or other reduction in component capability, the current driving mode, environmental conditions in the vicinity of vehicle or along a planned route, or other factors.

Abstract: This disclosure provides methods and systems for dynamically creating a trajectory for navigating a vehicle. The method may include receiving sensor data from at least one sensor of the autonomous vehicle, the sensor data representative of a driving surface in a field of view of the autonomous vehicle; segmenting a portion of the driving surface in the field of view of the autonomous vehicle by determining nominal path based at least in part on the image data; assigning a plurality of nodes to at least a portion of the nominal path; associating the plurality of the nodes assigned to the nominal path with a line to generate at least one segmentation polyline; determining updated nominal path by fitting the each of the plurality of segmentation lines to the nominal path; generating a trajectory based on the updated nominal path; and navigating the autonomous vehicle according to the trajectory.

Abstract: Aspects of the disclosure relate to determining whether a vehicle should continue through an intersection. For example, the one or more of the vehicle's computers may identify a time when the traffic signal light will turn from yellow to red. The one or more computers may also estimate a location of a vehicle at the time when the traffic signal light will turn from yellow to red. A starting point of the intersection may be identified. Based on whether the estimated location of the vehicle is at least a threshold distance past the starting point at the time when the traffic signal light will turn from yellow to red, the computers can determine whether the vehicle should continue through the intersection.

Abstract: Example embodiments relate to multiple operating modes to expand dynamic range. An example embodiment includes a camera system. The camera system may include a first image sensor having a first dynamic range corresponding to a first range of luminance levels in a scene. The system may also include a second image sensor having a second dynamic range corresponding to a second range of luminance levels in the scene. The camera system may further include a processor coupled to the first image sensor and the second image sensor. The processor may be configured to execute instructions to identify objects of a first type in a first image of the scene captured by the first image sensor and identify objects of a second object type in a second image of the scene captured by the second image sensor.

Abstract: Systems and methods for detecting and measuring object velocity are provided. The method comprises, using a spinning Light Detection and Ranging (LiDAR) system, generating a first LiDAR point cloud of an environment surrounding a vehicle, using a scanning LiDAR system, generating a second LiDAR point cloud of the environment surrounding the vehicle, and, using a processor, identifying points within the first LiDAR point cloud coinciding with a position of an object and points within the second LiDAR point cloud coinciding with a position of the object, determining whether the points within the second LiDAR point cloud line up with the points within the first LiDAR point cloud, and, when the points within the second LiDAR point cloud do not line up with the points within the first LiDAR point cloud, determining that the object is moving.

Abstract: Systems and methods are provided of downsampling an image to a plurality of image resolutions. The method comprises capturing, using a camera, an image depicting an environment within view of the camera, identifying a first section of the image depicting an area of the environment spaced within a first distance range from the camera, identifying a second section of the image depicting an area of the environment spaced within a second distance range from the camera, identifying a third section of the image depicting an area of the environment spaced within a third distance range from the camera, and downsampling the first section of the image to a first image resolution, generating a first processed image, the second section of the image to a second image resolution, generating a second processed image, and the third section of the image to a third image resolution, generating a third processed image.

Abstract: This disclosure provides methods and systems for generating a training set for a neural network configured to generate candidate trajectories for an autonomous vehicle, comprising: receiving a set of sensor data representative of one or more portions of a plurality of objects in the environment of the autonomous vehicle; for each object, calculating a representative box enclosing the object, the representative box having portions comprising corners, edges, and planes; for each representative box, calculating at least one vector into the representative box from a position on the autonomous vehicle; for each vector, calculating a first and second corner position of the representative box, an edge of the representative box, and a plane of the representative box; determining the highest confidence corners, edge, and plane of each representative based on calculation from the at least one vector; and generating a training set including the highest confidence corners, edge, and plane of each representative box.

Abstract: This disclosure provides methods and systems for detecting and tracking objects in an environment of an autonomous vehicle.

Abstract: This disclosure provides systems and methods for detecting and tracking objects or obstacles in an environment of an autonomous vehicle. The method may include receiving data from a set of sensors, wherein the data represents objects or obstacles in an environment of the autonomous vehicle; and using a processor: generating high precision detection data based on the received data; identifying, from the high precision detection data, a set of objects that are classifiable by at least one known classifier; generating high recall detection data based on the received data; identifying from the high recall detection data a set of obstacles; and performing an operation on the high precision detection data of the objects and the high recall detection data of the obstacles, based on a status of the autonomous vehicle or based on one or more characteristics of the objects or the obstacles.

Abstract: This disclosure provides systems and methods for controlling a vehicle. The method comprises receiving data from a set of sensors, wherein the data represents objects or obstacles in an environment of the autonomous vehicle; determining attributes of each of the objects or obstacles based on the received data from the set of sensors; integrating the attributes of the each of the objects or obstacles; identifying objects or obstacles based on the integrated attributes; determining a candidate trajectory for the autonomous vehicle to avoid the objects or obstacles; and controlling the autonomous vehicle according to the candidate trajectory.

Abstract: This disclosure provides systems and methods for controlling a vehicle. The method comprises receiving data from a set of sensors, wherein the data represents objects or obstacles in an environment of the autonomous vehicle; identifying objects or obstacles from the received data; determining multiple sets of attributes of the objects or obstacles, wherein each set of attributes of the objects or obstacles are determined based on data received by an individual sensor; determining a candidate trajectory for the autonomous vehicle based on the multiple sets of attributes of the objects or obstacles; and controlling the autonomous vehicle according to the candidate trajectory.