Tusimple, Inc.

Dinghua Li

Yixin Yang

Arda Kurt

Junbo Jing

Chasen Sherman

Techniques for analysis of autonomous vehicle operations are described. As an example, a method of autonomous vehicle operation includes storing sensor data from one or more sensors located on the autonomous vehicle into a storage medium, performing, based on at least some of the sensor data, a simulated execution of one or more programs associated with the operations of the autonomous vehicle, generating, based on the simulated execution of the one or more programs and as part of a simulation, one or more control signal values that control a simulated driving behavior of a simulated vehicle, and providing a visual feedback of the simulated driving behavior of the simulated vehicle on a simulated road.

Autonomous vehicle simulation system

Devices, systems, and methods for integrated predictive dynamic control of a vehicle powertrain in an autonomous vehicle are described. An example method for controlling a vehicle includes generating, based on performing an optimization on a blended smooth wheel domain fuel consumption map subject to a modified torque availability constraint, one or more wheel domain control commands, converting the one or more wheel domain control commands to one or more powertrain-executable engine domain control commands, and transmitting the one or more powertrain-executable engine domain control commands to a powertrain of the vehicle, the powertrain configured to operate a plurality of gears, wherein the one or more powertrain-executable engine domain control commands enable the vehicle to track a reference kinematic trajectory associated with a vehicle speed driving plan within a predetermined tolerance.

Vehicle powertrain integrated predictive dynamic control for autonomous driving

Systems and methods of managing error data associated with a vehicle to provide a more user-friendly vehicle error data system. A method of managing error data associated with a vehicle incorporates storing error data in a memory; categorizing the error data via topics or time frame via a processor coupled to the memory; analyzing the error data to result in at least one topic thread or a time thread; selecting desired error data in response to selection of the desired error data from the error data by a user; converting the desired error data into readable information via the processor; and displaying the readable information via a user interface providing a human-machine interaction.

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San Diego, CA, United States of America

Dinghua Li