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: 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 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: 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: 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: 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.

Abstract: Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer implemented method includes obtaining, by one or more computing devices on-board an autonomous vehicle, data associated with one or more objects that are proximate to the autonomous vehicle. The data includes a predicted path of each respective object. The method includes identifying at least one object as an object of interest based at least in part on the data associated with the object of interest. The method includes generating cost data associated with the object of interest. The method includes determining a motion plan for the autonomous vehicle based at least in part on the cost data associated with the object of interest. The method includes providing data indicative of the motion plan to one or more vehicle control systems to implement the motion plan for the autonomous vehicle.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for associating objects are provided. For example, the disclosed technology can receive sensor data associated with the detection of objects over time. An association dataset can be generated and can include information associated with object detections of the objects at a most recent time interval and object tracks of the objects at time intervals in the past. A subset of the association dataset including the object detections that satisfy some association subset criteria can be determined. Association scores for the object detections in the subset of the association dataset can be determined. Further, the object detections can be associated with the object tracks based on the association scores for each of the object detections in the subset of the association dataset that satisfy some association criteria.

Abstract: Systems and methods for determining object intentions through visual attributes are provided. A method can include determining, by a computing system, one or more regions of interest. The regions of interest can be associated with surrounding environment of a first vehicle. The method can include determining, by a computing system, spatial features and temporal features associated with the regions of interest. The spatial features can be indicative of a vehicle orientation associated with a vehicle of interest. The temporal features can be indicative of a semantic state associated with signal lights of the vehicle of interest. The method can include determining, by the computing system, a vehicle intention. The vehicle intention can be based on the spatial and temporal features. The method can include initiating, by the computing system, an action. The action can be based on the vehicle intention.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a computing system can receive object data that includes portions of sensor data. The computing system can determine, in a first stage of a multiple stage classification using hardware components, one or more first stage characteristics of the portions of sensor data based on a first machine-learned model. In a second stage of the multiple stage classification, the computing system can determine second stage characteristics of the portions of sensor data based on a second machine-learned model. The computing system can generate an object output based on the first stage characteristics and the second stage characteristics. The object output can include indications associated with detection of objects in the portions of sensor data.

Abstract: Systems and methods are provided for generating data indicative of a friction associated with a driving surface, and for using the friction data in association with one or more vehicles. In one example, a computing system can detect a stop associated with a vehicle and initiate a steering action of the vehicle during the stop. The steering action is associated with movement of at least one tire of the vehicle relative to a driving surface. The computing system can obtain operational data associated with the steering action during the stop of the vehicle. The computing system can determine a friction associated with the driving surface based at least in part on the operational data associated with the steering action. The computing system can generate data indicative of the friction associated with the driving surface.

Abstract: In one example embodiment, a computer-implemented method for transporting cargo using smart palettes includes determining receipt of a first cargo onto a platform of a first smart palette at a first distribution hub. The method includes generating one or more signals that control a loading of the first smart palette and the first cargo onto a trailer located at the first distribution hub. The method includes determining a coordination with one or more second smart palettes associated with the trailer to determine a first position inside the trailer for the first smart palette and the first cargo. The method includes generating one or more signals that position the first smart palette and the first cargo at the first position inside the trailer.

Abstract: Systems and methods for a moveable cover panel of an autonomous vehicle is provided. A vehicle can include a front panel disposed proximate to the front end of the passenger compartment, a vehicle motion control device located at the front panel, and a cover panel located at the front panel. The cover panel moveable relative to the front panel between an isolating position and an exposing position. The cover panel can isolate the vehicle motion control device from the passenger compartment when in the isolating position and expose the vehicle motion control device to the passenger compartment when in the exposing position. A method can include obtaining vehicle data identifying an operational mode, state, and/or status of the vehicle, determining a first position of the cover panel, and initiating a positional change for the cover panel based on the vehicle data and the first position.

Abstract: The present disclosure provides systems and methods to obtain feedback descriptive of autonomous vehicle failures. In particular, the systems and methods of the present disclosure can detect that a vehicle failure event occurred at an autonomous vehicle and, in response, provide an interactive user interface that enables a human located within the autonomous vehicle to enter feedback that describes the vehicle failure event. Thus, the systems and methods of the present disclosure can actively prompt and/or enable entry of feedback in response to a particular instance of a vehicle failure event, thereby enabling improved and streamlined collection of information about autonomous vehicle failures.

Abstract: The present disclosure provides systems and methods that control the motion of an autonomous vehicle by rewarding or otherwise encouraging progress toward a goal, rather than simply rewarding distance travelled. In particular, the systems and methods of the present disclosure can project a candidate motion plan that describes a proposed motion path for the autonomous vehicle onto a nominal pathway to determine a projected distance associated with the candidate motion plan. The systems and methods of the present disclosure can use the projected distance to evaluate a reward function that provides a reward that is positively correlated to the magnitude of the projected distance. The motion of the vehicle can be controlled based on the reward value provided by the reward function. For example, the candidate motion plan can be selected for implementation or revised based at least in part on the determined reward value.

Abstract: The present disclosure is directed to validating motion plans for autonomous vehicles. In particular, the methods, devices, and systems of the present disclosure can: receive data indicating a motion plan of an autonomous vehicle through an environment of the autonomous vehicle; receive data indicating one or more inputs utilized in generating the motion plan; and determine, based at least in part on the data indicating the motion plan and the data indicating the input(s), whether execution of the motion plan by the autonomous vehicle would violate one or more predetermined constraints applicable to motion plans for the autonomous vehicle.

Abstract: Systems and methods for using parametric modeling to design scenarios for autonomous vehicle simulation are provided. In particular, a computing system can obtain data identifying a plurality of parameters, each parameter associated with a particular scenario component. The computing system can determine values associated with a first set of parameters in the plurality of parameters. The computing system can determine one or more parameter relationships, such that values associated with a second set of parameters in the plurality of parameters are determined, at least in part, based on the values associated with the first set of parameters. The computing system can initiate a simulation of a scenario based, at least in part, on the values associated with the first set of the parameters and the one or more parameter relationships. The computing system can determine whether the simulated autonomous vehicle has successfully completed the scenario.

Abstract: Systems and methods are directed to generating behavioral predictions in reaction to autonomous vehicle movement. In one example, a computer-implemented method includes obtaining, by a computing system, local scene data associated with an environment external to an autonomous vehicle, the local scene data including actor data for an actor in the environment external to the autonomous vehicle. The method includes extracting, by the computing system and from the local scene data, one or more actor prediction parameters for the actor using a machine-learned parameter extraction model. The method includes determining, by the computing system, a candidate motion plan for the autonomous vehicle. The method includes generating, by the computing system and using a machine-learned prediction model, a reactive prediction for the actor based at least in part on the one or more actor prediction parameters and the candidate motion plan.