Uatc, LLC

Uber Technologies, Inc.

Dima Kislovskiy

David McAllister Bradley

Andrew Raymond Sturges

An on-demand transportation management system can collect vehicle fleet utilization data corresponding to human-driven vehicles (HDVs) and autonomous vehicles (AVs) operating within a given region in connection with an on-demand transportation service. The on-demand transportation management system can then establish a set of selection priorities for respective areas of the given region based on the vehicle fleet utilization data, each selection priority indicating whether a respective area of the given region is to favor HDVs or AVs for servicing transport requests.

Fleet utilization efficiency for on-demand transportation services

An on-demand transportation management system can collect historical data of harmful events of human-driven vehicles (HDVs) operating throughout a given region. For each road segment of the given region, the system can determine a fractional risk value for the HDVs, and based on the fractional risk value for each road segment, the system can route drivers within the given region along lowest risk route options.

Generalized risk routing for human drivers

An on-demand transportation management system can receive transport requests from requesting users for an on-demand transportation service for a given region, each transport request indicating a pick-up location and a destination. The system can determine a candidate set of vehicles, within a proximity of the pick-up location, to service each transport request. The system may then determine an individual risk value for each vehicle in the candidate set of vehicles for servicing the transport request, based, at least in part, on the individual risk value for each vehicle of the candidate set of vehicles, the system can select a vehicle from the candidate set of vehicles to service the transport request.

Individualized risk vehicle matching for an on-demand transportation service

An on-demand transportation management system can receive transport requests in connection with an on-demand transportation service, each transport request indicating a start location and a destination. The system can determine a set of candidate vehicles to service each transport request, and can further determine a non-trip risk value for servicing the transport request. The system may then select an optimal vehicle in the set of candidate vehicles based at least in part on the non-trip risk value.

Non-trip risk matching and routing for on-demand transportation services

An autonomous vehicle software management system can distribute AV software versions to safety-driven autonomous vehicles (SDAVs) operating within a given region. The system can receive log data from the SDAVs indicating any trip anomalies of the SDAVs while executing the AV software version. When a predetermined safety standard has been met based on the log data, the system can verify the AV software version for execution on fully autonomous vehicles (FAVs) operating within the given region.

Software version verification for autonomous vehicles

An autonomous vehicle (AV) software management system can collect historical data of harmful events of human-driven vehicles (HDVs) within an autonomy grid on which AVs operate. For each path segment of the autonomy grid, the system can determine a fractional risk value for HDVs. The system may also receive AV data from a fleet of AVs operating throughout the autonomy grid, and for each path segment of the autonomy grid, the system can evaluate AV performance against the fractional risk values for HDVs based on the received AV data.

Fractional risk performance evaluation for autonomous vehicles

An on-demand transportation management service can perform a selection process between a set of safety-driven autonomous vehicles (SDAVs), fully autonomous vehicles (FAVs), and human-driven vehicles (HDVs) to service transport requests based on a variety of selection parameters.

Vehicle selection for on-demand transportation services

A transportation management system can maintain a set of driver logs for drivers operating throughout a given region, where each driver log indicates driving characteristics of a respective driver. The system can determine a destination for the respective driver operating a vehicle from an initial location to the destination, and determine a set of routes between the initial location and the destination. Based at least in part on the driving characteristics of the respective driver, the system can determine an individualized risk value for the respective driver for each route of the set of routes, and select an optimal route from the set of routes based, at least in part, on the individualized risk value for each route.

Individualized risk routing for human drivers

An on-trip monitoring system for an on-demand transportation service can receive log data from autonomous vehicles operating throughout a given region. Based on a set of triggering conditions, the on-trip monitoring system can transmit switching commands to cause the AVs to switch between software versions and/or operative modes.

Software version and mode switching for autonomous vehicles

A risk regression system can computationally determine aggregate risk values for each of a plurality of routes for each transport request of an on-demand transportation service. The aggregate risk values can be based on fractional risk quantities determined through, for example, autonomous vehicle logs form autonomous vehicles operating throughout a given region. Based on the aggregate risk values, the risk regression system can facilitate vehicle matching between autonomous vehicles and human-driven vehicles.

Path segment risk regression system for on-demand transportation services

An on-trip monitoring system for an on-demand transportation service can monitor live log data from autonomous vehicles (AVs) operating throughout a given region. The system can determine a degradation level for a respective AV based on the live log data, and when the degradation level exceeds a determined threshold, the system can transmit an update command to the respective AV to service or decommission the respective AV.

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Pittsburgh, PA, United States of America

Dima Kislovskiy