Abstract: Systems and methods for designing a robotic system architecture are disclosed. The methods include generating a model that defines one or more requirements for a robotic device for a mapping between a software graph and a hardware graph. The model is used for allocating a plurality of computational tasks in a computational path included in the software graph to a plurality of hardware components of the robotic device to yield a robotic system architecture. The methods also include using the robotic system architecture to configure the robotic device to be capable of performing functions corresponding to the software graph, where the robotic system architecture is optimized to meet one or more latency requirements.

Abstract: Systems, methods, and autonomous vehicles for automated lane conflict estimation may obtain map data associated with a map of a geographic location including a roadway, determine, based on the map data, a relative lane geometry between a first lane segment and a second lane segment of a pair of overlapping lane segments; process, with a machine learning model, the relative lane geometry and a type of a traffic signal or sign associated with the pair of overlapping lane segments to generate a prediction of whether the first lane segment yields to the second lane segment for a given state of the traffic signal or sign; and use the prediction to at least one of generate a map including the lane segment associated with the prediction, facilitate at least one autonomous driving operation of an autonomous vehicle, or any combination thereof.

Abstract: Systems, methods, and computer-readable media are disclosed for context aware verification for sensor pipelines. Autonomous vehicles (AVs) may include an extensive number of sensors to provide sufficient situational awareness to perception and control systems of the AV. For those systems to operate reliably, the data coming from the different sensors should be checked for integrity. To this end, the systems and methods described herein may use contextual clues to ensure that the data coming from the different the sensors is reliable.

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: Disclosed herein are system, method, and computer program product embodiments for an asymmetrical Autonomous Vehicle Systems (AVS). A backup AVS is implemented on a vehicle to serve as a failover system for one or more of the primary AVS components or processes (e.g., steering, braking, etc.). In this way, during primary AVS failures, the backup AVS can dynamically handle a subset of vehicle operations in various component configuration levels based on a desired mission level.

Abstract: Method and systems for generating vehicle motion planning model simulation scenarios are disclosed. The system receives a base simulation scenario with features of a scene through which a vehicle may travel. The system then generates an augmentation element with a simulated behavior for an object in the scene by: (i) accessing a data store in which behavior probabilities are mapped to object types to retrieve a set of behavior probabilities for the object; and (ii) applying a randomization function to the behavior probabilities to select the simulated behavior for the object. The system will add the augmentation element to the base simulation scenario at the interaction zone to yield an augmented simulation scenario. The system will then apply the augmented simulation scenario to an autonomous vehicle motion planning model to train the motion planning model.

Abstract: Devices, systems, and methods are provided for an enhanced anechoic chamber. An enhanced anechoic chamber device may operate a gimbal setup attached to a mounting arm of an anechoic chamber and a radar under test to modify an azimuth angle and an elevation angle of a radar under test. The enhanced anechoic chamber device may cause the radar under test to transmit one or more signals towards one or more reflectors situated in a field of view of the radar through an aperture of an anechoic chamber, wherein the one or more reflectors are situated outside the anechoic chamber. The enhanced anechoic chamber device may receive reflected signals from the one or more reflectors at the radar under test, wherein the reflected signals pass through the aperture before reaching the radar under test. The enhanced anechoic chamber device may measure signal energy of at least one of the reflected signals. The enhanced anechoic chamber device may generate an output indicating an operational status of the radar under test.

Abstract: Systems, methods, and autonomous vehicles may obtain sensor data associated with an environment surrounding an autonomous vehicle; provide the sensor data to a plurality of plugins; independently determine, with each plugin, based on the sensor data, whether to request a remote guidance session for the autonomous vehicle, each plugin of the plurality of plugins including a different model that is applied by that plugin to the sensor data to determine whether to request the remote guidance session; receive, from at least one plugin, a request to initiate the remote guidance session; and in response to receiving the request to initiate the remote guidance session, communicate with a computing device external to the autonomous vehicle to establish the remote guidance session.

Abstract: Devices, systems, and methods are provided for enhanced multispectral sensor calibration. A device may include a first layer having copper, a second layer having solder material, the second layer above the first layer, and a third layer having a white silkscreen material, the third layer above the second layer. Regarding the device, the first layer may be used for calibration of a thermal sensor, the second layer may be used for calibration of an image sensor and calibration of a light detection and ranging (LIDAR) sensor, and the third layer may be used for the calibration of the image sensor and the calibration of the LIDAR sensor.

Abstract: Methods and systems for controlling navigation of a vehicle are disclosed. The system will first detect a URU within a threshold distance of a drivable area that a vehicle is traversing or will traverse. The system will then receive perception information relating to the URU, and use a plurality of features associated with each of a plurality of entry points on a drivable area boundary that the URU can use to enter the drivable area to determine a likelihood that the URU will enter the drivable area from that entry point. The system will then generate a trajectory of the URU using the plurality of entry points and the corresponding likelihoods, and control navigation of the vehicle while traversing the drivable area to avoid collision with the URU.

Abstract: This document discloses system, method, and computer program product embodiments for operating an autonomous vehicle (AV). For example, the method includes performing the following operations by a muxing tool when AV is deployed within a particular geographic area in a real-world environment: receiving perception data that is representative of at least one actual object which is perceived while AV is deployed within the particular geographic area in a real-world environment; receiving simulation data that represents a simulated object that could be perceived by AV in the real-world environment and that was generated using a simulation scenario which is selected from a plurality of simulation scenarios based on at least one of the particular geographic area in which AV is currently located and a current operational state of AV; and generating augmented perception data by combining the simulation data with the perception data.

Abstract: Systems/methods for operating an autonomous vehicle. The methods comprise, by a computing device: using sensor data to track an object that was detected in proximity to the autonomous vehicle; classifying the object into at least one dynamic state class of a plurality of dynamic state classes; transforming the at least one dynamic state class into at least one goal class of a plurality of goal classes; transforming the at least one goal class into at least one proposed future intention class of a plurality of proposed future intention classes; determining at least one predicted future intention of the object based on the proposed future intention class; and/or causing the autonomous vehicle to perform at least one autonomous driving operation based on the at least one predicted future intention determined for the object.

Abstract: Systems, methods, and autonomous vehicles may obtain one or more images associated with an environment surrounding an autonomous vehicle; determine, based on the one or more images, an orientation of a head worn item of protective equipment of an operator of a vehicle; determine, based on the orientation of the head worn item of protective equipment, a direction of a gaze of the operator and a time period associated with the direction of the gaze of the operator; determine, based on the direction of the gaze of the operator and the time period associated with the direction of the gaze of the operator, a predicted motion path of the vehicle; and control, based on the predicted motion path of the vehicle, at least one autonomous driving operation of the autonomous vehicle.

Abstract: Disclosed herein are system, method, and computer program product embodiments for switching between local and remote guidance instructions for autonomous vehicles. For example, the method includes, in response to monitoring one or more actions of objects detected in a scene in which the autonomous robotic system is moving, causing the autonomous robotic system to slow or cease movement in the scene. The method includes detecting a trigger condition based on movement of the autonomous robotic system in the scene. In response to the one or more monitored actions and detecting the trigger condition, the method includes transmitting a remote guidance request to a remote server. After transmitting the remote guidance request, the method includes receiving remote guidance instructions from the remote server and causing the autonomous robotic system to begin operating according to the remote guidance instructions.

Abstract: Methods and systems for controlling navigation of an autonomous vehicle for traversing a drivable area are disclosed. The methods include receiving information relating to a drivable area that includes a plurality of polygons, identifying a plurality of logical edges that form a boundary of the drivable area, sequentially and repeatedly analyzing concavities of each the plurality of logical edges until identification of a first logical edge that has a concavity greater than a threshold, creating a first logical segment of the boundary of the drivable area. This segmentation may be repeated until each of the plurality of logical edges has been classified. The method may include creating and adding (to a map) a data representation of the drivable area that comprises an indication of the plurality of logical segments, and adding the data representation to a road network map comprising the drivable area.

Abstract: Disclosed herein are system, method, and computer program product embodiments for automated autonomous vehicle pose validation. An embodiment operates by generating a range image from a point cloud solution comprising a pose estimate for an autonomous vehicle. The embodiment queries the range image for predicted ranges and predicted class labels corresponding to lidar beams projected into the range image. The embodiment generates a vector of features from the range image. The embodiment compares a plurality of values to the vector of features using a binary classifier. The embodiment validates the autonomous vehicle pose based on the comparison of the plurality of values to the vector of features using the binary classifier.

Abstract: A time master and sensor data collection module for a robotic system such as an autonomous vehicle is disclosed. The module includes a processing device, one or more sensors, and programming instructions that are configured to cause the processing device to operate as a timer that generates a vehicle time, receive data from the one or more sensors contained within the housing, and synchronize the data from the one or more sensors contained within the housing with the vehicle time. The integrated sensors may include sensors such as a global positioning system (GPS) unit and/or an inertial measurement unit (IMU). The module may interface with external sensors such as a LiDAR system and/or cameras.

Abstract: Disclosed herein are system, method, and computer program product embodiments for clustering lane segments of a roadway in order to improve and simplify autonomous vehicle behavior testing. The approaches disclosed herein provide a hybrid methodology of dividing lane segments into hard features and soft features, and using a metric learning model trained in a supervised process on the entirety of lane segment features to cluster the lane segments based on the soft features. These clustered lane segments can then be assigned to what is termed as protolanes, where a single set of tests applied to a given protolane is considered valid across all of the lane segments assigned to the protolane.

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.