Volkswagen Group of America Investments, LLC

Scott Plant

Shubhashree Venkatesh

Sebastian Dovenor

Jason Parraga

Jonathan Pan

Noe Brito

John Drake

Yee-Ning Cheng

Madhav Chhura

Brett Browning

Ramadev Burigsay Hukkeri

Shenglong Gao

Israel Marc Kositsky

Methods and systems for obtaining serviceable areas for a robotic system in a metropolitan area are described. A computing device obtains information about places where (i) the system can route to and from in the area and/or (ii) the system can stop in the area. The computing device uses the information to generate clusters of places where the robotic system can route or stop in the metropolitan area. The computing device creates a geometric shape for each cluster, wherein each shape which has a boundary defined by outermost places contained in the cluster. The computing device uses the geometric shapes to define the serviceable areas for the robotic system within the metropolitan area. The computing device uses the serviceable areas to generate a map displaying at least one geographic area representing a portion of the metropolitan area where a concentrated number of the places exist.

Systems and methods for defining serviceable areas

Methods and systems for generating a planned path for a vehicle are disclosed. Upon receiving a trip service request, a processor will access a data store containing multiple candidate motion planning systems, each of which is associated with at least one vehicle or fleet. The processor will identify a starting point and a destination for the trip service request, and it will use an identifier for the vehicle or its fleet to select, from the candidate systems, a motion planning system. The processor will use the functions of the selected motion planning system to generate candidate trajectories for the first vehicle from the starting point to the destination in a high definition map. The processor will select a planned route from the candidate trajectories, and it will output trip instructions to cause the vehicle to move along the planned route.

System and method for generating a planned path for a vehicle using a cloud deployment system

Methods and systems that use a phantom vehicle to help generate a planned path for a real-world vehicle are described. The system will identify a starting point and a destination for a trip of the real-world vehicle. The system will select, from the data store of vehicle profiles, a phantom vehicle having an associated motion planning system that corresponds to a system that is deployed on the real-world vehicle. The system will use a high definition map to generate a planned route for the real-world vehicle from the starting point to the destination in the map. The system will run a simulation in which the phantom vehicle moves along the planned route in the map. The system will then output a record of the simulation to a user of the real world-vehicle or to a system of the real-world vehicle.

System and method for generating a planned path using a phantom vehicle

Devices, systems, and methods are provided for enhanced rider pairing of an autonomous vehicle (AV). A system may pair a first user profile of a first user located at a first location with a first autonomous vehicle (AV) to complete a trip to a destination selected by the first user. The system may detect a second AV at the first location, wherein the second AV is associated with a second user profile. The system may connect the second AV with the first user using a connection mechanism. The system may select a profile status of the first user profile based on the connection to the second AV. The system may pair the first user profile with the second AV based on the profile status.

Enhanced rider pairing for autonomous vehicles

Methods and systems for estimating a time of arrival for a vehicle at a destination are disclosed. The system will access an adjacency graph comprising nodes and edges. Each node is associated with a unique location in a geographic area in which the vehicle is traveling. Each edge connects two of the nodes and is associated with an estimated travel time between the two connected nodes. The system will select, from the locations in adjacency graph, a first location that is near the vehicle and a second location that is near the destination. The location and destination are each associated with nodes in adjacency graph. The system will calculate a shortest path along the edges in the adjacency graph from the location and destination nodes, and it will calculate an estimated time of arrival for the vehicle as a function of the estimated travel times along the shortest path.

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San Jose, CA, United States of America

Scott Plant