Abstract: Disclosed herein are system, method, and computer program product aspects for testing autonomous vehicle intent using protolanes. For example, the method includes selecting a first lane segment of a roadway within a geonet, wherein the geonet comprises a plurality of lane segments. It is determined that the first lane segment corresponds to a protolane of a set of protolanes, wherein the protolane is associated with one or more additional lane segments of the plurality of lane segments. The protolane is associated with the first lane segment based on the determination that the first lane segment corresponds to the protolane.

Abstract: A system receives a road network map that corresponds to a road network that is in an environment of an autonomous vehicle. For each of the one or more lane segments, the system identifies one or more conflicting lane segments from the plurality of lane segments, each of which conflicts with the lane segment, and adds conflict data pertaining to a conflict between the lane segment and the one or more conflicting lane segments to a set of conflict data. The system analyzes the conflict data to identify a conflict cluster that is representative of an intersection. The system groups predecessor lane segments and the successor lane segments as inlets or outlets of the intersection, generates an outer geometric boundary of the intersection, generates an inner geometric boundary of the intersection, creates a data representation of the intersection and adds the data representation to the road network map.

Abstract: Systems and methods for managing data. The methods comprise: receiving, by a load driver node of a network, a request for upload of source data associated with a resource name to a data warehouse; generating a publication identifier and a version value for the source data by the load driver node in response to the request; causing, by the load driver node, operations to be performed by load worker nodes to facilitate population of at least one fact table of the data warehouse with the publication identifier and the source data; and causing, by the load driver node, a publication table of the data warehouse to be updated to include the publication identifier and the version value so as to be associated with the resource name.

Abstract: Systems, methods, and computer-readable media are disclosed for obtaining data from multiple internal vehicle networks. An example method may include receiving, using a first internal communication network of a vehicle, a first query for data from one or more devices of the vehicle, wherein the first query is formatted for communication over the first internal communication network. The example method may also include generating, based on the first query, a second query associated with a supplemental communication network, wherein the supplemental communication network is also internal to the vehicle. The example method may also include sending, using the supplemental communication network, the second query to a first device, the first device being located on the supplemental communication network. The example method may also include receiving, from the first device, data relating to the second query.

Abstract: A mounting device for selectively positioning an optical element within a field-of-view of an optical sensor of a vehicle includes: a housing defining an opening sized to fit over an aperture of the optical sensor; a holder for the optical element connected to the housing and positioned such that, when the holder is in a first position, the optical element is at least partially within the field-of-view of the optical sensor; and a motorized actuator. The motorized actuator can be configured to move the holder to adjust the position of the optical element relative to the field-of-view of the optical sensor.

Abstract: Systems and methods for selecting data for training a machine learning model using active learning are disclosed. The methods include receiving a plurality of unlabeled sensor data logs corresponding to surroundings of an autonomous vehicle, identifying one or more trends associated with a training dataset comprising a plurality of labeled data logs, determining a function for assigning an importance score to each of the plurality of unlabeled sensor data logs, using the one or more trends, using the function for assigning the importance score to each of the plurality of unlabeled sensor data logs, selecting a subset of the plurality of sensor data logs that have an importance score greater than a threshold, and using the subset of the plurality of sensor data logs for further training the machine learning model trained using the training dataset to generate an updated model.

Abstract: Devices, systems, and methods are provided for communications between autonomous and emergency vehicles. A method may include identifying, by an autonomous vehicle (AV), a first message received from a first vehicle, and identifying, by the AV, in the first message, information associated with identifying the AV, a security key associated with identifying the first vehicle, and an instruction associated with causing the AV to perform an action. The method may include authenticating, by the AV, based on the security key, the first vehicle, and controlling operation, based on the instruction and the information associated with identifying the AV, of the AV to perform the action.

Abstract: Systems, methods, and computer-readable media are disclosed for a systems and methods for improved LIDAR return light capture efficiency. One example method may include comparing, by a controller including a processor and at a first time, a first temperature of a first computing element to a first threshold temperature and a second temperature of a second computing element to a second threshold temperature. The example method may also include sending, based on a determination that the first temperature is below the first threshold temperature and the second temperature is above the second threshold temperature, a first signal to a switch to activate a data output corresponding to the second computing element. The example method may also include sending, to the second computing element, a second signal to cause a third computing element to increase heat dissipation from the third computing element to the first computing element.

Abstract: Methods of forecasting intentions of actors that an autonomous vehicle (AV) encounters in are disclosed. The AV uses the intentions to improve its ability to predict trajectories for the actors, and accordingly making decisions about its own trajectories to avoid conflict with the actors. To do this, for any given actor the AV determines a class of the actor and detects an action that the actor is taking. The system uses the class and action to identify candidate intentions of the actor and evaluating a likelihood of each candidate intention. The system repeats this process over multiple cycles to determine overall probabilities for each of the candidate intentions. The AV's motion planning system can use the probabilities to determine likely trajectories of the actor, and accordingly influence the trajectory that the AV will itself follow in the environment.

Abstract: Disclosed herein are system, method, and computer program product aspects for enabling an autonomous vehicle (AV) to detect objects and forecast their predicted positions. The system can monitor an object within a vicinity of the AV. A plurality of trajectories predicting paths the object will take at a future time can be generated, the plurality of trajectories being based on a generated three-dimensional (3D) point cloud map indicating current and past characteristics of the object. Using a learned model, a forecasted position of the object at an instance in time can be generated along one or more of the plurality of trajectories. A maneuver for the AV can be performed based on the forecasted position.

Abstract: A test module is provided with a housing for mounting within a cabin of an autonomous vehicle (AV). At least two user input devices are supported by the housing. A controller is disposed within the housing and programmed to: generate a first request to control an AV system of the AV based on manual activation of one of the at least two user input devices, and generate a second request to control the AV system based on manual activation of the other of the at least two user input devices. At least one transceiver provides the first request to the AV on a first communication interface and provides the second request to the AV system on a second communication interface.

Abstract: Systems and methods for retrieving and using data stored in a data warehouse. The methods comprise performing the following operations by a computing device: receiving a query for information directed to a plurality of fact tables (each fact table comprising sets of source data that are arranged so as to be respectively associated with a plurality of first publication identifiers); obtaining second publication identifiers from a publication table, in response to the query; and obtaining at least one of the sets of source data from each fact table that is associated with a first publication identifier of the plurality of first publication identifiers which matches one of the second publication identifiers.

Abstract: A system for testing an autonomous vehicle obtains a list of required tests the autonomous vehicle is to run, where each test is part of a commissioning process for the autonomous vehicle. The system causes the list to be displayed on a display device of the autonomous vehicle, receives a selection of a selected test from an operator of the autonomous vehicle, and receives a test input profile associated with the selected test. The system causes the autonomous vehicle to execute at the instructions of the test input profile, and logs response data to one or more log files. During execution of the test instructions, the system generates metadata associated with the selected test. The system logs the metadata to the one or more log files, and transmits at least a portion of the one or more log files to an electronic device located remotely from the autonomous vehicle.

Abstract: Systems and methods for processing point cloud data are disclosed. The methods include receiving a 3D image including point cloud data, displaying a 2D image of the 3D image, and generating a 2D bounding box that envelops an object of interest in the 2D image. The methods further include generating a projected image frame comprising a projected plurality of points by projecting a plurality of points in a first direction. The methods may then include displaying an image frame that includes the 2D image and the 2D bounding box superimposed by the projected image frame, receiving a user input that includes an identification of a set of points in the projected plurality of points that correspond to the object of interest, identifying a label for the object of interest, and storing the set of points that correspond to the object of interest in association with the label.

Abstract: Devices, systems, and methods are provided for testing and validation of a camera. A device may capture a first image of a target using a camera, wherein the camera is in a clean state, and wherein the target is in a line of sight of the camera. The device may apply an obstruction to a portion of a lens of the camera. The device may apply a camera cleaning system to the lens of the camera. The device may capture a post-clean image after applying the camera cleaning system. The device may determine a post-clean SSIM score based on comparing the post clean image to the first image. The device may compare the post-clean SSIM score to a validation threshold. The device may determine a validation state of the camera cleaning system based on the comparison.

Abstract: A method of determining a trajectory for an autonomous vehicle (AV) is disclosed. When the AV's on-board system detects another actor near the AV, it will generate a virtual doppelganger that is co-located with the autonomous vehicle. The system will predict possible modalities for the other actor and, for each of the modalities, one or more possible trajectories for the other actor. The system then forecast candidate actions of the virtual doppelganger, each of which corresponds to one or more of the possible trajectories of the other actor. The computing system will then determine candidate trajectories of the autonomous vehicle, and it will select one of the candidate trajectories as a selected trajectory for the autonomous vehicle to follow.

Abstract: Systems and methods of maneuvering an autonomous vehicle in a local region using topological planning, while traversing a route to a destination location, are disclosed. The system includes an autonomous vehicle including one or more sensors and a processor. The processor is configured to determine the local region on the route and receive real-time information corresponding to the local region. The processor performs topological planning to identify on or more topologically distinct classes of trajectories, compute a constraint set for each of the one or more topologically distinct classes of trajectories, optimize a trajectory to generate a candidate trajectory for each constraint set, and select a trajectory for the autonomous vehicle to traverse the local region from amongst the one or more candidate trajectories.

Abstract: Systems, methods, and computer-readable media are disclosed for obtaining data from multiple internal vehicle networks. An example method may include receiving, using a first internal communication network of a vehicle, a first query for data from one or more devices of the vehicle, wherein the first query is formatted for communication over the first internal communication network. The example method may also include generating, based on the first query, a second query associated with a supplemental communication network, wherein the supplemental communication network is also internal to the vehicle. The example method may also include sending, using the supplemental communication network, the second query to a first device, the first device being located on the supplemental communication network. The example method may also include receiving, from the first device, data relating to the second query.

Abstract: A mounting device includes an elongated beam having a first end portion, a second end portion, and a side surface extending between the first end portion and the second end portion. The mounting device also includes a first camera mount attached to the first end portion configured to support a first camera, a second camera mount attached to the second end portion configured to support a second camera, and a bracket for fixedly connecting the elongated beam to a vehicle. The bracket is positioned between the first end portion and the second end portion. The bracket includes at least one base configured to be attached to the vehicle and a wall extending from the at least one base comprising an opening sized to receive the elongated beam, such that engagement between the wall and the elongated beam restricts rotation of the elongated beam about multiple axes.

Abstract: Devices, systems, and methods are provided for enhanced sensor alignment. A device may determine a first array of displacement sensors proximate to a first test structure. The device may determine a second array of displacement sensors proximate to a second test structure. The device may apply a test condition to the first array, the second array, the first test structure, and the second test structure. The device may collect a first output from applying the test condition to the first test structure. The device may collect a second output from applying the test condition to the second test structure. The device may generate a first deviation vector associated with the first output. The device may generate a second deviation vector associated with the second output. The device may determine a first design status of the first structure based on the first deviation vector. The device may determine a second design status of the second structure based on the second deviation vector.