Abstract: Systems and methods for operating a vehicle are disclosed. The methods comprise: generating, by a computing device, a vehicle trajectory for the vehicle while the vehicle is in motion; detecting an object within a given distance from the vehicle; generating at least one possible object trajectory for the object which was detected; performing a collision check to determine whether a collision between the vehicle and the object can be avoided based on the vehicle trajectory and the at least one possible object trajectory; performing a plausibility check to determine whether the collision is plausible based on content of a map, when a determination is made in the collision check that a collision between the vehicle and the object cannot be avoided; and performing operations to selectively cause the vehicle to perform an emergency maneuver based on results of the plausibility check.

Abstract: Devices, systems, and methods are provided for testing and validation of a camera. For example, a system includes a camera that is configured to capture an image of a content object. A processor is programmed to: receive a signal indicative of the image of the content object during an electromagnetic event; generate an image quality score of the image; and generate a validation status of the camera based on a comparison of the image quality score to a validation threshold.

Abstract: This document describes methods and systems for enabling an autonomous vehicle (AV) to determine a path to a stopping location. The AV will determine a desired stop location (DSL) that is associated with a service request. The AV's motion control system will move the AV along a path to the DSL. While moving along the path, the AV's perception system will detect ambient conditions near the DSL. The ambient conditions will be parameters associated with a stopping rule. The AV will apply the stopping rule to the ambient conditions to determine whether the stopping rule permits the AV to stop at the DSL. If the stopping rule permits the AV to stop at the DSL, the motion control system will move the AV to, and stop at, the DSL. Otherwise, the motion control system will not stop the AV at the DSL.

Abstract: Methods and systems are disclosed for correlating synthetic LiDAR data to a real-world domain for use in training an model for use by autonomous vehicle when operating in an environment. To do this, the system will obtain a data set of synthetic LiDAR data, along with images of a real-world environment. The system will transfer the synthetic LiDAR data to a two-dimensional representation, use the two-dimensional representation and the images to train a model that a vehicle can use to operate in a real-world environment.

Abstract: A system for communicating with multiple vehicles or other electronic devices that share a common media access control (MAC) or other address is disclosed. Upon receiving a certificate signing request (CSR) from a connected device and determining that the device does not have a unique address, the system will generate a unique address for the device and embedding the unique addresses in a certificate, sign the certificate, and transfer the certificate to the device. Then, when the system communicates with the device, the system may use that unique address to identify the device.

Abstract: Systems and methods for forecasting trajectories of objects. The method includes obtaining a prediction model trained to predict future trajectories of objects. The prediction model is trained over a first prediction horizon selected to encode inertial constraints in a predicted trajectory and over a second prediction horizon selected to encode behavioral constraints in the predicted trajectory. The method also include generating a planned trajectory of an autonomous vehicle by receiving state data corresponding to the autonomous vehicle, receiving perception data corresponding to an object, predicting a future trajectory of the object based on the perception data and the prediction model, and generating the planned trajectory of the autonomous vehicle based on the future trajectory of the object and the state data.

Abstract: Disclosed herein are systems, methods, and computer program products for predicting movement of an object in a real-world environment. The methods comprise: obtaining a plurality of image frames captured in a sequence during a period of time; identifying first image frames of the plurality of image frames that contain an image of at least one object with one or more turn signals; analyzing the first image frames to obtain a classification for a pose of the at least one object; using the classification of the pose of the at least one object to further obtain a type classification for at least one of the turn signals and a state classification for a state of at least one of the turn signals; and predicting movement of the at least one object based at least on the type and state classifications obtained for at least one of the turn signals.

Abstract: Devices, systems, and methods are provided for mitigating vehicle power loss. A vehicle charging system may include a power supply, and a voltage control device associated with receiving first voltage from the power supply, providing the first voltage to a hybrid vehicle or a battery electric vehicle, and blocking a second voltage from the hybrid vehicle or the battery electric vehicle, wherein the vehicle charging system is external to the hybrid vehicle or the battery electric vehicle.

Abstract: Systems and methods are provided for navigating a vehicle to veer around a lane obstruction safely into a neighboring lane. The system may plan a trajectory around the obstructed lane. Over a temporal horizon, the system determines temporal margins by measuring an amount of time between a predicted state of a moving actor in the neighboring lane and a predicted state of the vehicle. The system identifies a minimum temporal margin of the temporal margins and determines whether the minimum temporal margin is equal to or larger than a required temporal buffer. If the minimum temporal margin is equal to or larger than the required temporal buffer, the system generates a motion control signal to cause the vehicle to follow the trajectory to veer around the obstruction into the neighboring lane. Otherwise, the system generates a motion control signal to cause the vehicle to reduce speed or stop.

Abstract: Systems, methods, and computer-readable media are disclosed for improved smart infrastructure data transfer. An example method may involve receiving, by a smart infrastructure device and from a first vehicle, first information associated with the first vehicle in a first format associated with a first communication protocol. The first information is converted from the first format into an agnostic format. An image, video, or real-time feed of an environment of the smart infrastructure device is captured. The first vehicle and a second vehicle in the image, video, or real-time feed is identified. It is determined that the second vehicle is temporarily or permanently incapable of performing a communication with the smart infrastructure device based on the image, video, or real-time feed. The image, video, or real-time feed is analyzed to generate second information associated with the second vehicle. The second information is converted into the agnostic format.

Abstract: Methods, systems, and products for generating an updated map for use with an autonomous vehicle driving operation or a simulation thereof may include obtaining first map data associated with a first map of a geographic location including a roadway, and the first map data may include at least one first lane segment. Second map data associated with a second map of the geographic location may be obtained, and the second map data may include at least one second lane segment. A plurality of non-overlapping areas may be determined based on the first lane segment(s) and the second lane segment(s). A first non-overlapping and/or a first warp point within the first non-overlapping area may be selected. The first lane segment(s) may be warped around the first warp point to increase a total overlapping area based on the based on the second lane segment(s) and the first lane segment(s) after warping.

Abstract: Provided are autonomous vehicles and methods of controlling autonomous vehicles through topological planning with bounds, including receiving map data and sensor data, expanding a topological tree by adding a plurality of nodes to represent a plurality of actions associated with the plurality of constraints, generating a bound based on a constraint in the geographic area, the bound associated with an action for navigating the autonomous vehicle relative to the at least one constraint, storing the bound in a central bound storage, linking a set of bounds of a tree node to the bound via a bound identifier, wherein the first bound is initially linked as an active bound, or alternatively, as an inactive bound after determining it is not the most restrictive bound at any sample index, and control the autonomous vehicle based on the topological tree, to navigate the plurality of constraints.

Abstract: Systems for managing access to an autonomous vehicle includes an autonomous vehicle including a plurality of storage compartments, wherein each of the plurality of storage compartments comprises a locking mechanism and at least one processor to receive data associated with an item to be positioned in a storage compartment of the plurality of storage compartments, determine that one of the plurality of storage compartments has storage capacity for the item, designate one of the plurality of storage compartments for storage of the item, activate the locking mechanism of the designated storage compartment to lock the designated storage compartment after the item is positioned in the designated storage compartment, and activate the locking mechanism of the designated storage compartment to unlock the designated storage compartment to allow removal of the item from the designated storage compartment. Methods, computer program products, and autonomous vehicles are also disclosed.

Abstract: Methods and systems for jointly estimating a pose and a shape of an object perceived by an autonomous vehicle are described. The system includes data and program code collectively defining a neural network which has been trained to jointly estimate a pose and a shape of a plurality of objects from incomplete point cloud data. The neural network includes a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network. The method includes receiving an incomplete point cloud representation of an object, inputting the point cloud data into the trained shared encoder, outputting a code representative of the point cloud data. The method also includes generating an estimated pose and shape of the object based on the code. The pose includes at least a heading or a translation and the shape includes a denser point cloud representation of the object.

Abstract: A method of transferring data between an autonomous vehicle and a vehicle traffic control infrastructure system. The method includes receiving, by a communication device of a vehicle, a data payload for a smart traffic control infrastructure node. The method includes, by a computing device of the vehicle: determining that the vehicle is or will be within a communication range of the smart traffic control infrastructure node, determining a length of time that the vehicle will be in the communication range of the smart traffic control infrastructure node, and assembling a communication package with at least a portion of the data payload that can be transferred in the determined length of time. The method includes, by a communication device of the vehicle when the vehicle has an ad hoc communication link with the smart traffic control infrastructure node, transmitting the assembled communication package to the smart node.

Abstract: Disclosed herein are system, method, and computer program product aspects for enabling an autonomous vehicle (AV) to react to objects posing a risk to the AV. The system can monitor an object within a vicinity of the AV. A plurality of trajectories predicting paths the object will take can be generated, the plurality of trajectories being based on a plurality of inputs 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. An error value representing how accurate the forecasted position is versus an observed position of the object can be stored. Error values can be accumulated over a period of time. A risk factor can be assigned for the object based on the accumulated error values. A maneuver for the AV can be performed based on the risk factor.

Abstract: A method of scheduling a plurality of tasks in an autonomous vehicle system (AVS) includes, by a processor, prior to runtime of an autonomous vehicle, identifying a plurality of tasks to be implemented by the AVS of the autonomous vehicle, for each of the tasks, identifying at least one fixed parameter and at least one variable, and developing a schedule for each of the tasks. The schedule includes an event loop that minimizes an overall time for execution of the tasks. The method includes compiling the schedule into an execution plan, and saving the execution plan to a memory of the autonomous vehicle. During runtime of the autonomous vehicle, the processor receives data corresponding to the variables of the tasks, and uses the variables to implement the execution plan on the autonomous vehicle.

Abstract: Systems and methods for managing data. The methods comprise by a computing system: generating publication identifiers and version values for source data to be stored into a data warehouse; causing a plurality of fact tables in the data warehouse to be populated with the source data and the publication identifiers; causing a publication table in the data warehouse to be updated to include the publication identifiers and the version values so as to be respectively associated with resource names; receiving a query for information directed to the plurality of fact tables; retrieving the publication identifiers from the publication table, in response to the query; and obtaining source data from each said fact table of the plurality of fact tables that is associated with publication identifiers that are stored in both the fact table and the publication table.

Abstract: A system uses data captured by vehicle-mounted sensors to generate a view of a ground surface. The system does this by receiving digital image frames and associating a location and pose of the vehicle that captured the image with each digital image frame. The system will access a three dimensional (3D) ground surface estimation model of the ground surface, select a region of interest (ROI) of the ground surface, and select a vehicle pose. The system will identify digital image frames that are associated with the pose and also with a location that corresponds to the ROI. The system will generate a visual representation of the ground surface in the ROI by projecting ground data for the ROI from the ground surface estimation model to normalized 2D images that are created from the digital image frames. The system will save the visual representation to a two-dimensional grid.

Abstract: Methods, systems, and computer program products for navigating a vehicle are disclosed. The methods include extracting lane segment data associated with lane segments of a vector map that are within a region of interest, and analyzing the lane segment data and a heading of the vehicle to determine whether motion of the vehicle satisfies a condition. The condition can be associated with (i) an association between the heading of the vehicle and a direction of travel of a lane that corresponds to the current location of the vehicle and/or (ii) a minimum stopping distance to an imminent traffic control measure in the lane that corresponds to the current location of the vehicle. When the motion does not satisfy the condition, the methods include causing the vehicle to perform a motion correction.