Abstract: Systems and methods for generating motion forecast data for actors with respect to an autonomous vehicle and training a machine learned model for the same are disclosed. The computing system can include an object detection model and a graph neural network including a plurality of nodes and a plurality of edges. The computing system can be configured to input sensor data into the object detection model; receive object detection data describing the location of the plurality of the actors relative to the autonomous vehicle as an output of the object detection model; input the object detection data into the graph neural network; iteratively update a plurality of node states respectively associated with the plurality of nodes; and receive, as an output of the graph neural network, the motion forecast data with respect to the plurality of actors.

Abstract: Systems and methods for basis path generation are provided. In particular, a computing system can obtain a target nominal path. The computing system can determine a current pose for an autonomous vehicle. The computing system can determine, based at least in part on the current pose of the autonomous vehicle and the target nominal path, a lane change region. The computing system can determine one or more merge points on the target nominal path. The computing system can, for each respective merge point in the one or more merge points, generate a candidate basis path from the current pose of the autonomous vehicle to the respective merge point. The computing system can generate a suitability classification for each candidate basis path. The computing system can select one or more candidate basis paths based on the suitability classification for each respective candidate basis path in the plurality of candidate basis paths.

Abstract: A LIDAR system includes a LIDAR unit. The LIDAR unit includes a housing defining a cavity. The LIDAR unit further include a plurality of emitters disposed on a circuit board within the cavity. Each of the emitters emits a laser beam along a transmit path. The LIDAR system further includes a first optic rotatable about a first axis at a first rotational speed and a second optic rotatable about a second axis at a second rotational speed that is faster than the first rotational speed. The first optic is positioned relative to the LIDAR unit such that a plurality of laser beams exiting the LIDAR unit pass through the first optic. The second optic is positioned relative to the first optic such that each of a plurality of refracted laser beams exiting the prism disk reflect off of the second optic.

Abstract: Systems and methods for servicing vehicle messages utilizing a common communications infrastructure are provided. A method includes obtaining, by a vehicle computing system onboard an autonomous vehicle, a message including a cryptographic signature and contextual information, a request, or a command. The method includes determining an originating sender associated with the message based on the cryptographic signature and determining a message privilege for the message based on the originating sender. The originating sender is a process that generated the message. The method includes determining a response for the contextual information, the request, or the command based on the message privilege. The response is indicative of an acceptance or rejection of contextual information, an assignment of computing resources to a request, or an acceptance or rejection of a command. The method includes performing a vehicle action based on the response.

Abstract: The present disclosure provides systems and methods for generating photorealistic image simulation data with geometry-aware composition for testing autonomous vehicles. In particular, aspects of the present disclosure can involve the intake of data on an environment and output of augmented data on the environment with the photorealistic addition of an object. As one example, data on the driving experiences of a self-driving vehicle can be augmented to add another vehicle into the collected environment data. The augmented data may then be used to test safety features of software for a self-driving vehicle.

Abstract: Systems and methods for controlling autonomous vehicle test trips are provided. In one example embodiment, a computer implemented method includes obtaining, by a computing system, data indicative of a test trip index associated with an autonomous vehicle. The test trip index includes a plurality of test trips for the autonomous vehicle and each test trip is associated with one or more test trip parameters. The method includes obtaining, by the computing system, data indicating that the autonomous vehicle is available to travel in accordance with at least one of the test trips of the test trip index. The method includes selecting, by the computing system and from the test trip index, at least one selected test trip for the autonomous vehicle. The method includes causing, by the computing system, the autonomous vehicle to travel in accordance with the test trip parameters associated with the at least one selected test trip.

Abstract: A first vehicle testing tuple comprising a plurality of first testing parameters and a second vehicle testing tuple comprising a plurality of second testing parameters can be obtained. The plurality of first testing parameters can be determined to be associated with an evaluated operating condition. The first tuple can be appended to a first portion of a plurality of portions of a vehicle testing knowledge structure. A second testing parameter can be determined to be associated with an unevaluated operating condition. The unevaluated operating condition can be evaluated. A second portion comprising the second vehicle testing tuple can be generated for the vehicle testing knowledge structure.

Abstract: An autonomous can include one or more configurable passenger seats to accommodate a plurality of different seating configurations. For instance, the one or more passenger seats can include a passenger seat defining a seating orientation. The passenger seat can be configurable in a first configuration in which the seating orientation is directed towards a forward end of the autonomous vehicle and a second configuration in which the seating orientation is directed towards a rear end of the autonomous vehicle. The passenger seat can include a seatback rotatable about a pivot point on a base of the passenger seat to switch between the first configuration and the second configuration. Alternatively, or additionally, the autonomous vehicle can include a door assembly pivotably fixed to a vehicle body of the autonomous vehicle such that a swept path of the door assembly when moving between an open position and a closed position is reduced.

Abstract: Systems and methods for predicting interactions between objects and predicting a trajectory of an object are presented herein. A system can obtain object data associated with a first object and a second object. The object data can have position data and velocity data for the first object and the second object. Additionally, the system can process the obtained object data to generate a hybrid graph using a graph generator. The hybrid graph can have a first node indicative of the first object and a second node indicative of the second object. Moreover, the system can process, using an interaction prediction model, the generated hybrid graph to predict an interaction type between the first node and the second node. Furthermore, the system can process, using a graph neural network model, the predicted interaction type between the first node and the second node to predict a trajectory of the first object.

Abstract: An autonomous vehicle can access map data comprising travel ways within a surrounding environment of the autonomous vehicle, and further access constraint data that define original navigational constraints within the map data. The vehicle can further receive constraint files comprising additional navigational constraints within the map data and modify the constraint data based on the constraint files. The vehicle can determine a travel route to a destination using composite constraint data and autonomously driver to the destination along the travel route accordingly.

Abstract: Aspects of the present disclosure include systems, methods, and devices use a controllable matrix filter to selectively obscure regions of an image sensor's field of view. The controllable matrix filter is a physical component that may be placed in front of an image sensor and, in certain situations, one or more regions of the otherwise transparent matrix filter may be selectively configured to have an increased optical density such that the one or more regions become opaque thereby blocking out certain regions of the image sensor's field of view. In this way, the controllable matrix filter may be used to mask out certain regions in an image sensor's field of view that may present processing difficulties for downstream systems that utilize information from the image sensor.

Abstract: Systems and methods for facilitating communication with autonomous vehicles are provided. In one example embodiment, a computing system can obtain a first type of sensor data (e.g., camera image data) associated with a surrounding environment of an autonomous vehicle and/or a second type of sensor data (e.g., LIDAR data) associated with the surrounding environment of the autonomous vehicle. The computing system can generate overhead image data indicative of at least a portion of the surrounding environment of the autonomous vehicle based at least in part on the first and/or second types of sensor data. The computing system can determine one or more lane boundaries within the surrounding environment of the autonomous vehicle based at least in part on the overhead image data indicative of at least the portion of the surrounding environment of the autonomous vehicle and a machine-learned lane boundary detection model.

Abstract: Systems and methods for controlling autonomous vehicle are provided. A method can include obtaining, by a computing system, data indicative of a plurality of objects in a surrounding environment of the autonomous vehicle. The method can further include determining, by the computing system, one or more clusters of the objects based at least in part on the data indicative of the plurality of objects. The method can further include determining, by the computing system, whether to enter an operation mode having one or more limited operational capabilities based at least in part on one or more properties of the one or more clusters. In response to determining that the operation mode is to be entered by the autonomous vehicle, the method can include controlling, by the computing system, the operation of the autonomous vehicle based at least in part on the one or more limited operational capabilities.

Abstract: Systems and methods are directed to automated cleaning systems for vehicles. In one example, a vehicle is provided that includes a drive system, a passenger cabin, and a vehicle cleaning system. The vehicle cleaning system includes a vacuum unit and a robotic manipulator. The robotic manipulator includes an arm including a plurality of arm sections and a gripper unit positioned at a distal end of the arm. The robotic manipulator is connected to the vacuum unit such that the arm provides a vacuum path between the vacuum unit and the gripper unit.

Abstract: A secure start system for an autonomous vehicle can include a communications router comprising an input interface to receive a boot-loader to enable network communications with a backend system. The secure start system utilizes a tunnel key from the backend system to establish a private communications session with a backend data vault. The secure start system then retrieves a set of decryption keys from the backend data vault, via the private communications session, to decrypt a plurality of encrypted drives of the autonomous vehicle, which enables one or more functions of the autonomous vehicle.

Abstract: Systems and methods for detecting a surprise or unexpected movement of an actor with respect to an autonomous vehicle are provided. An example computer-implemented method can include, for a first compute cycle, obtaining motion forecast data based on first sensor data collected with respect to an actor relative to an autonomous vehicle; and determining, based on the motion forecast data, failsafe region data representing an unexpected path or area where a likelihood of the actor following the unexpected path or entering the unexpected area is below a threshold. For a second compute cycle after the first compute cycle, the method can include obtaining second sensor data; determining, based on the second sensor data and the failsafe region data, that the actor has followed the unexpected path or entered the unexpected area; and in response to such determination, determining a deviation for controlling a movement of the autonomous vehicle.

Abstract: Aspects of the present disclosure involve systems, methods, and devices for determining specular reflectivity characteristics of objects using a Lidar system of an autonomous vehicle (AV) system. A method includes transmitting at least two light signals directed at a target object utilizing the Lidar system of the AV system. The method further includes determining at least two reflectivity values for the target object based on return signals corresponding to the at least two light signals. The method further includes classifying specular reflectivity characteristics of the target object based on a comparison of the first and second reflectivity value. The method further includes updating a motion plan for the AV system based on the specular reflectivity characteristics of the target object.

Abstract: Systems and methods for trajectory prediction are provided. A method can include obtaining LIDAR data, radar data, and map data; inputting the LIDAR data, the radar data, and the map data into a network model; transforming, by the network model, the radar data into a coordinate frame associated with a most recent radar sweep in the radar data; generating, by the network model, one or more features for each of the LIDAR data, the transformed radar data, and the map data; combining, by the network model, the one or more generated features to generate fused feature data; generating, by the network model, prediction data based at least in part on the fused feature data; and receiving, as an output of the network model, the prediction data. The prediction data can include a respective predicted trajectory for a future time period for one or more detected objects.

Abstract: Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes determining vehicle diagnostics information associated with a first autonomous vehicle that is part of a fleet of vehicles controlled by a first entity to provide a vehicle service to a second entity. The method includes determining remote inspection information that includes an assessment of one or more categories pertaining to a third entity, based at least in part on the vehicle diagnostics information. The method includes providing the remote inspection information to the third entity to provide the vehicle service.

Abstract: An autonomous vehicle includes one or more sensors for detecting an object in an environment surrounding the autonomous vehicle and a vehicle computing system comprising one or more processors receiving canonical route data associated with at least one canonical route, and controlling travel of the autonomous vehicle based on sensor data from the one or more sensors and the canonical route data associated with the at least one canonical route. The at least one canonical route comprises at least one roadway connected with another roadway in a plurality of roadways in a geographic location that satisfies at least one route optimization function derived based on trip data associated with one or more traversals of the plurality of roadways in a geographic location by one or more autonomous vehicles.