Abstract: Embodiments herein include an automated vehicle performing for identifying vehicles and lanes in roadway by an autonomy system of an automated vehicle. The autonomy system gathers image inputs from cameras or other sensors. The autonomy system assigns index values to the driving lanes and shoulder lanes, and then assigns the index values to the vehicles. The autonomy system generates data segments from the image data, corresponding to creating segments of an image, such that a single image is segmented for portions of the image, such as segmented outputs of each lane line or segmented outputs of portions of the vehicle. The autonomy system compares the segmented portions of the image to detect that a lane contains a vehicle.

Abstract: Embodiments herein include an automated vehicle performing for identifying vehicles and lanes in roadway by an autonomy system of an automated vehicle. The autonomy system gathers image inputs from cameras or other sensors. The autonomy system assigns index values to the driving lanes and shoulder lanes, and then assigns the index values to the vehicles. The autonomy system generates data segments from the image data, corresponding to creating segments of an image, such that a single image is segmented for portions of the image, such as segmented outputs of each lane line or segmented outputs of portions of the vehicle. The autonomy system compares the segmented portions of the image to detect that a lane contains a vehicle.

Abstract: Embodiments herein include an automated vehicle performing for identifying vehicles and lanes in roadway by an autonomy system of an automated vehicle. The autonomy system gathers image inputs from cameras or other sensors. The autonomy system assigns index values to the driving lanes and shoulder lanes, and then assigns the index values to the vehicles. The autonomy system generates data segments from the image data, corresponding to creating segments of an image, such that a single image is segmented for portions of the image, such as segmented outputs of each lane line or segmented outputs of portions of the vehicle. The autonomy system compares the segmented portions of the image to detect that a lane contains a vehicle.

Abstract: A system including a supervisory computing device and a sensor apparatus positioned at a drivable intersection, the sensor apparatus remote from an autonomous vehicle and in communication with the autonomous vehicle. The sensor apparatus includes one or more sensors and one or more processors configured to receive from the sensors an indication of a presence of the autonomous vehicle at the drivable intersection and alert the supervisory computing device of the presence of the autonomous vehicle. The processors then receive a signal associated with a drivable area on which the autonomous vehicle may travel and predict a drivable path for the autonomous vehicle. The processors transmit an indication of the drivable path to the supervisory computing device to request approval to travel the drivable path. Upon receiving approval, the one or more processors transmit a signal to adjust an operating parameter to initiate travelling on the drivable path.

Abstract: A method of passing an object on the side of the road by receiving a first signal from a first sensor configured to detect a presence of the object positioned on a shoulder of a road; determining a first value; assigning a first confidence value to a confidence level, wherein the first confidence value is associated with the first value; responsive to the first confidence value exceeding a confidence threshold, adjusting a first operating parameter of a vehicle; responsive to entering a threshold range of the object, receiving a second signal from a second sensor; determining a second value associated with the second signal; assigning a second confidence value to the confidence level, wherein the second confidence value is associated with the second value; responsive to the second confidence value exceeding a second confidence threshold, adjusting a second operating parameter of the vehicle.

Abstract: A method of determining a location of an autonomous vehicle hub including receiving from one or more sensors associated with a portable sensor apparatus, a signal associated with a drivable area; applying the signal associated with the drivable area to a viability model, wherein the viability model is configured to: simulate an entrance procedure of one or more autonomous vehicles into the drivable area; determine, based at least on the signal associated with the drivable area, a drivable surface on which an autonomous vehicle may travel to enter the drivable area; determine, based at least on the signal associated with the drivable area, a frequency in which the drivable surface is available for the autonomous vehicle to enter the drivable area; and output a viability score associated with the drivable area, the viability score indicating a viability of establishing the autonomous vehicle hub proximate the drivable area.

Abstract: Aspects of this technical solution can include obtaining, by one or more processors coupled with non-transitory memory, a first plurality of images each including corresponding first time stamps, the plurality of images corresponding to video data of a physical environment, extracting, by the one or more processors and from among the first plurality of images, a second plurality of images each having corresponding second time stamps and each having corresponding features indicating an object in the physical environment, and training, by the one or more processors and with input including the features and the second time stamps corresponding to the features, a machine learning model to generate an output indicating a pattern of movement of one or more objects corresponding to the features and the second time stamps corresponding to the features.

Abstract: Aspects of this technical solution can include generating, by a processor, a first metric corresponding to a first signal from one or more first sensors, the first sensor configured to detect a position of a fifth-wheel hitch of a vehicle with the fifth-wheel hitch in a first position, generating, by the processor, a second metric corresponding to a second signal from a drive assembly, the drive assembly configured to reposition of the fifth-wheel hitch, generating, by the processor and based on the first and second metrics, a third metric, the third metric corresponding to a second position of the fifth-wheel hitch with a predetermined load balancing configuration and transmitting, by the processor and responsive to a determination that the second position of the fifth-wheel hitch corresponds to a predetermined load balancing configuration of the vehicle, the third metric to the drive assembly.

Abstract: Trailer detection is provided. A vehicle can determine a distance to a portion of the trailer from the time-of-flight sensor data. The vehicle can determine a doppler shift indicative of a speed of a wheel of a wheel assembly from the data. The vehicle can determine, based on the distance and the doppler shift, a wheelbase of the trailer.

Abstract: Embodiments herein include an oversized automated vehicle having a trailer portion and tractor portion performing a J-hook turn at an intersection. The automated vehicle includes an autonomy system for gathering and processing various types of data. The autonomy system generates a candidate trajectory, including the trailer path and the tractor path performing a J-hook turn, and generates driving instructions for the automated vehicle according to a satisfactory candidate trajectory. The autonomy system applies an optimization function for iteratively generating and modifying the candidate trajectory. Using the map data for the intersection, the optimization function modifies the candidate trajectory, such that if the tractor or the trailer were to drive along the predicted tractor path and trailer path, then the tractor and the trailer would perform the J-hook turn while fitting within the drivable surface boundary and inner boundaries (e.g., driving lanes).

Abstract: Embodiments herein include systems and methods of generating lane selection cost values to control autonomous vehicles to accommodate merging vehicles in a tapering lane (or merge lane). An autonomy system can identify a tapering lane in map data and detect a merging vehicle situated in the tapering lane using perception sensor data. The autonomy system includes a lane-selection cost function that generates lane-selection cost values for the lanes available to the automated vehicle, which the autonomy system references to determine whether to continue traveling a current lane or change lanes into an adjacent lane. The lane-selection cost function may apply a courtesy weight when detecting the merging vehicle, such that the autonomy system causes the automated vehicle to change lanes as a courtesy to the merging vehicle, but without overriding other safety-related factors of the lane-selection cost function or trajectory planning functions.

Abstract: Embodiments herein include systems and methods of generating lane selection cost values to control autonomous vehicles to accommodate merging vehicles in a tapering lane (or merge lane). An autonomy system can identify a tapering lane in map data and detect a merging vehicle situated in the tapering lane using perception sensor data. The autonomy system includes a lane-selection cost function that generates lane-selection cost values for the lanes available to the automated vehicle, which the autonomy system references to determine whether to continue traveling a current lane or change lanes into an adjacent lane. The lane-selection cost function may apply a courtesy weight when detecting the merging vehicle, such that the autonomy system causes the automated vehicle to change lanes as a courtesy to the merging vehicle, but without overriding other safety-related factors of the lane-selection cost function or trajectory planning functions.

Abstract: Detection of trailer dynamic parameters is provided. A vehicle can receive a geometry of a plurality of points of support disposed along a longitudinal axis of the trailer. The vehicle can determine a center of mass of the trailer based on the plurality of points of support and weights bearing down thereupon. The vehicle can bound or otherwise determine, based on the geometry of the plurality of points of support and the center of mass, a moment of inertia of the trailer about at least one of the plurality of points of support. The vehicle can perform a navigational action based on the moment of inertia or center of mass.

Abstract: Trailer detection is provided. An autonomous vehicle can receive, from a time of flight sensor, an indication of a position of a wheel assembly of a trailer. The vehicle can determine a rotation of a rotatable coupler. The vehicle can determine a centerline distance from the rotatable coupler to the wheel assembly.

Abstract: Trailer detection is provided. An autonomous vehicle can cause a first transceiver thereof to convey a signal to energize a second transceiver coupled to a trailer. The autonomous vehicle can receive, from the second transceiver, first information associated with the trailer, the first information comprising an indication of a trailer wheelbase.

Abstract: Systems and methods of generating lane selection cost maps to control autonomous vehicles are disclosed. An autonomous vehicle system can receive a plurality of cost values from a plurality of components of an autonomous vehicle; generate a lane selection cost map based on the plurality of cost values; determining that the autonomous vehicle should change lanes based on the lane selection cost map; and transmit a command that causes the autonomous vehicle to change lanes responsive to determining that the autonomous vehicle should change lanes.

Abstract: A method comprises receiving a first set of data associated with a plurality of retroreflective features from a vehicle; retrieving a digital map comprising vectorized data associated with the plurality of retroreflective features and a location associated with the plurality of retroreflective features, the map previously generated based on monitoring a second set of data retrieved from a sensor of a second vehicle where the second set of data is associated with the plurality of retroreflective features near a road being driven by the second vehicle; generating a score for each retroreflective feature indicating a match between each retroreflective feature as indicated within the digital map and a location of each retroreflective feature as identified within the first set of data; and localizing the vehicle.

Abstract: Systems and methods of automatic correction of map data for autonomous vehicle navigation are disclosed. One or more servers can receive an indication of a traffic condition at an autonomous vehicle hub; upon receiving the indication of the traffic condition at the autonomous vehicle hub, identify an autonomous vehicle traveling a route that includes the autonomous vehicle hub; generate a control command for the autonomous vehicle based on the route and the traffic condition; and transmit the control command to the autonomous vehicle to correct the traffic condition at the autonomous vehicle hub.

Abstract: Aspects of this technical solution can include a first layer having a first region and a second region, the first region and the second region having a first transmissivity property corresponding to a first portion of a spectrum of light and a second portion of the spectrum of light, having the first transmissivity property, and a second layer disposed over the first layer and having a third region and a fourth region, the third region and the fourth region having a second transmissivity property corresponding to the first portion of the spectrum of light and the second portion of the spectrum of light.

Abstract: Embodiments herein include an autonomy system of an automated vehicle performing operations for detecting and avoiding lazy or unpredictable vehicles on a roadway. The autonomy system generates bounding boxes for tracking traffic vehicles recognized in sensor data. For close vehicles, the autonomy system generates expanded bounding boxes. The autonomy system determines the close vehicle is an encroaching vehicle by detecting that the expanded bounding box of the close vehicle overlapped the middle lane line or enters into the automated vehicle's current lane of travel. The autonomy system may simulate the behavior of the close vehicle by forward-propagating predicted positions of the close vehicle and expanded bounding box over some future time. In response to detecting the encroaching vehicle, the autonomy system may determine whether to perform an avoidance action or select a particular avoidance action, such as biasing away from the encroaching vehicle.