Rideos, Inc.

Uber Technologies, Inc.

Gobrands, Inc.

Rohan Paranjpe

Billy P Chen

Christopher Blumenberg

Justin Wayne Ho

Thomas Kielbus

Christopher D Moore

Joseph Kelly Barker

Karan Rajen Parikh

Erik Reed

Michel Tricot

Seth Harris Rubin

Jintao Guan

Somchaya Liemhetcharat

Min Ji Lee

Systems and methods for generating a composite map are provided. In one aspect, a method is provided that includes identifying a first node and a source node on a first map. The method further includes determining a candidate source node on a second map that corresponds to the source node on the first map. The method further includes determining a plurality of candidate nodes on the second map that potentially correspond to the first node and selecting a respective candidate node from the plurality of candidate nodes based on a similarity of a routing distance between: the respective candidate node and the candidate source node on the second map and the first node and the source node on the first map. The method may also include connecting the selected respective candidate node and the first node and combining the first map and the second map into a composite map.

Systems and methods of generating composite routing maps

A method includes of routing an autonomous vehicle includes receiving information obtained from a camera on a second vehicle distinct from the autonomous vehicle. The method includes automatically identifying a road condition using image analysis of the information received from the camera on the second vehicle. The method includes receiving a request to route the autonomous vehicle from a first location to a second location; and in response to the request: generating a cost model for routing the autonomous vehicle, wherein the cost model includes a cost of the road condition automatically identified from the information received from the camera on the second vehicle; selecting a route from the first location to the second location in accordance with the cost model; and routing an autonomous vehicle in accordance with the selected route.

System and method of overlaying real-time data onto an autonomous vehicle routing cost model

A method includes storing representations of a passengers and fleet vehicles. The method includes generating a graph representation of a geographic map that includes requested pick-up locations and drop-off locations for the passengers and generating a state graph representation of the passengers and the fleet vehicles. The state graph representation includes a plurality of nodes connected by edges. Each of the plurality of nodes represents a candidate state of the passengers and the fleet vehicles. A respective edge of the state graph representation represents an action of a respective vehicle picking up or dropping off a passenger. The respective edge has a cost that is based at least in part on traversal of the graph representation of the geographic map. The method further includes using the state graph representation to generate a set of routes and route the fleet vehicles in accordance with the generated set of routes.

System and method for routing in a ride-share transportation network

Systems and methods for routing a fleet of vehicles

A method includes generating a cost model for routing an autonomous vehicle. The cost model includes, for an intersection, a plurality of costs for distinct maneuvers through the intersection. The cost model is represented as a graph of nodes and edges, the edges having respective edge weights that represent costs in the cost model. Each edge of the representation of the intersection represents a respective maneuver through the intersection. The method further includes, in response to a request to route an autonomous vehicle: selecting a route in accordance with the cost model, wherein the selecting is based at least in part on one of the plurality of costs for the distinct maneuvers through the intersection; and routing the autonomous vehicle in accordance with the selected route.

System and method for routing using intersection costs

A transport service system identifies a map matched trajectory for a transport vehicle based on planned route information and location data associated with a trip. The system receives planned route information and location data for a transport vehicle. For each location data point, the transport service system identifies a set of candidate road segments. Sequential pairs of candidate road segments represent transitions. The system uses a map of the corresponding area to calculate a transition probability, the likelihood of traversing from the first candidate road segment to the second candidate road segment. The system identifies transitions that correspond to the planned route and adjusts the transition probabilities based on the identified planned route transitions. Based on the adjusted probabilities, the transport service system determines a most-likely traversed path for the transport vehicle.

Biasing map matched trajectories based on planned route information

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San Francisco, CA, United States of America

Rohan Paranjpe