Ford Global Technolgoies, LLC

Argo Ai Gmbh

Other

Volkswagen Group of America Investments, LLC

Gregory Boyd Nichols

Andrew Thomas Hartnett

Constantin Savtchenko

Greydon Foil

Friederike Schneemann

Alexander Metz

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.

Methods and system for predicting trajectories of uncertain road users by semantic segmentation of drivable area boundaries

Methods and systems for controlling navigation of a vehicle are disclosed. The system will first identify a plurality of goal points corresponding to a drivable area that a vehicle is traversing or will traverse, where the plurality of goal points are potential targets that an uncertain road user (URU) within the drivable area can use to exit the drivable area. The system will then receive perception information relating to the URU within the drivable area, and identify a target exit point from the plurality goal points based on a score. The score is computed based on the received perception information and a loss function. The system will generate a trajectory of the URU from a current position of the URU to the target exit point, and control navigation of the vehicle to avoid collision with the URU.

Methods and system for predicting trajectories of actors with respect to a drivable area

Methods by which an autonomous vehicle may predict actions of other actors are disclosed. A vehicle will assign either a high priority rating or a low priority rating to each actor that it detects. The vehicle will then generate a forecast for each of the detected actors. Some of not all high priority actors will receive a high resolution forecast. Low priority actors, and optionally also some of the high priority actors, will receive a low resolution forecast. The system will the forecasts to predict actions for the actors. The autonomous vehicle will then use the predicted actions to determine its trajectory.

Method and system for predicting behavior of actors in an environment of an autonomous vehicle

At compile-time, a processor develops a computer program by receiving an input that includes multiple nodes and connections between pairs of the nodes. The nodes represent object properties such as properties of objects that an autonomous vehicle (AV) detects while moving about an environment. For each node, the system will identify a depth that represents a number of nodes along a longest path from that node to any available input node. The system will order the nodes by depth in a sequence, and it will build a graph-based program specification that includes the nodes in the sequence, along with the connections. The graph-based program specification may correspond to a directed acyclic graph (DAG). The system will compile the graph-based program specification into a computer-readable program, and it will save the computer-readable program to a memory so that the AV or other system can use it at run-time.

Method and system for constructing static directed acyclic graphs

Methods and systems for determining information about an area that includes a polygon for controlling navigation of an autonomous vehicle are disclosed. The methods include defining a bounding box that encloses the area, and generating a KD-tree from the bounding box that partitions the polygon into a plurality of leaf nodes that each include at least some of a plurality of edges of the polygon. The methods also include assigning a reference point to each leaf node, creating a data representation of the area that comprises the KD-tree, and adding the data representation to map data comprising the area. A reference point is associated with a location within that leaf node, and information relating to whether the reference point lies outside or inside the at least one polygon.

Data structure for storing information relating to an environment of an autonomous vehicle and methods of use thereof

A modular panel system for the purpose of partitioning spaces. The modular panel system includes at least one panel, and a plurality of panel supports. Each panel support includes a member, a cap at a first end, a base at a second end, and a panel retaining arrangement. The base is shaped to support the member in a free-standing upright position. The panel retaining arrangement serves to permit the attachment and removal of an appropriately configured edge of a vertically oriented panel between the cap and the base without the use of tools, and the rotational movement of the panel about an axis defined by the edge.

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Franklin Park, PA, United States of America

Gregory Boyd Nichols