Joby Aero, Inc.

Eric Mueller

Ian Andreas Villa

Christabelle Bosson

Thomas Prevot

Bogu Wei

Adam Shaw Warmoth

Raphael Max Lurie

Igor Dolgov

Kellen Christopher Mollahan

Karl Weston Schulz

Philipp Haban

John Conway Badalamenti

Matthew William Derkach

Lucas McPhee Fischer

Christabelle Simone Juliette Bosson

Gregory Mark Gerard Belaus

Systems and methods for simulating aircraft systems are provided. A system includes simulation entity agents defined to represent the behavior of multi-modal ride-sharing agents (e.g., aerial vehicle, ground vehicle, air traffic controllers, etc.) and world data representing a world environment within which the simulation entity agents may operate. The system obtains agent data indicative of a simulation aerial vehicle and world data indicative of a particular simulated world environment. The system determines scenario data including a scenario definition defining a simulation scenario and generates a simulation instance based on the scenario data. The simulation instance includes the simulation aerial vehicle within the simulated world environment. The simulation aerial vehicle can have access, within the simulation, to one or more backend services of a transportation services system configured to facilitate an aerial transportation service. The system can initiate the simulation instance and obtain data while the simulation is running.

Systems and methods for simulating aircraft systems

The present disclosure provides systems and methods for systems and methods for generating potential flight plans to be used by a ride sharing network, including dynamic and/or automated changes to flight plans that have been engaged with passengers based on real-time information. In particular, the systems and methods of the present disclosure can operate to generate a fleet-level set of potential flight plans which comply with one or more constraints for a fleet of aircraft. The potential flight plans can be exposed into and used by a ride sharing network to provide transportation to users.

Systems and methods for generating flight plans used by a ride sharing network

Example embodiments are directed to generating an optimized network of flight paths and an operations volume around each of these flight paths. A network system creates a source network of paths, whereby the source network comprises a set of possible paths between two locations. The network system assigns a cost for traversing each edge of each path of the source network and aggregates the cost for traversing each edge of each path to obtain a cost for each path of the source network. Based on the cost for each path, the network system identifies a path having the lowest cost, whereby the path having the lowest cost is the optimized route between the two locations. The network system then generates an operations volume for the optimized route. The operations volume represents airspace surrounding the optimized route. The operations volume is transmitted to a further system for use.

Dynamic aircraft routing

Example embodiments are directed to systems and methods for providing cloud service to an onboard aerial vehicle system, A cloud service system accesses a flight related data. Using the flight related data, the cloud service system generates flight operations in a format of an avionics system on an aerial vehicle. A communication link is established over a communication network between the cloud service system and the aerial vehicle and the generated flight operations is transmitted to the aerial vehicle as digital data sent as data packets. The cloud service system then monitors, in real time, the aerial vehicle during a flight, wherein the monitoring comprises receiving and storing in-flight data from the aerial vehicle and the in-flight data is data reconstructed from a plurality of data packets received from the aerial vehicle. The cloud service system determines, based on the received in-flight data, whether to update the flight operations.

Cloud service integration with onboard vehicle system

Disclosed are embodiments for employing off board sensors to augment data used by a ground based autonomous vehicle. In some aspects, the off-board sensors may be positioned on another autonomous vehicle, such as an aerial autonomous vehicle (AAV). The disclosed embodiments determine uncertainty scores associated with ground regions. The uncertainty scores indicate a need to reimage the ground regions. An AAV may be tasked to reimage a region having a relatively high uncertainty score, depending on a cost associated with the tasking.

Integrating air and ground data collection for improved drone operation

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San Francisco, CA, United States of America

Eric Mueller