Tusimple, Inc.

Arda Kurt

Junbo Jing

Zijie Xuan

Yujia Wu

Chasen Sherman

Haoming Sun

Tianqu Shao

Xing Sun

Jun Chen

Aaron Havens

Xiaoling Han

Zhujia Shi

Dinghua Li

Riad I Hammoud

Jingxuan Liu

John E Panttila

Zehua Huang

Todd B Skinner

Charles Seunghwan Han

Yishi Liu

Yue Pan

Shuhan Yang

Haimo Bi

Zhiqi Gong

Jianqiu Cao

Devices, systems, and methods for predictive anti-lag braking control for autonomous vehicles are described. An example method of controlling a vehicle includes receiving, from a controller, a first plurality of values indicative of a brake wheel torque demand associated with a deceleration profile of the vehicle, deriving, based on the first plurality of values and a steady-state brake model, a second plurality of values indicative of a brake pressure demand, wherein an input to the steady-state brake model comprises the brake pressure demand and at least one previous brake wheel torque demand, and wherein an output of the steady-state brake system comprises a current brake wheel torque demand, generating, based on the second plurality of values and at least the steady-state model, a plurality of brake control commands, and controlling, using the plurality of brake control commands, the air brake system.

Predictive anti-lag braking control for autonomous driving

Techniques are described for determining visibility conditions of an environment in which an autonomous vehicle is operated and performing driving related operations based on the visibility conditions. An example method of adjusting driving related operations of a vehicle includes determining, by a computer located in an autonomous vehicle, a visibility related condition of an environment in which the autonomous vehicle is operating, adjusting, based at least on the visibility related condition, a set of one or more values of one or more variables associated with a driving related operation of the autonomous vehicle, and causing the autonomous vehicle to be driven to a destination by causing the driving related operation of one or more devices located in the autonomous vehicle based on at least the set of one or more values.

Visibility condition determinations for autonomous driving operations

Devices, systems, and methods for estimating wheel torque in an autonomous vehicle include a method for controlling a vehicle includes using a dual filter framework to generate an updated estimate of a wheel torque, and controlling, based on the updated estimate of the wheel torque, the vehicle. A first filter of the dual filter framework is configured to generate a blended estimate of the wheel torque by combining a first estimate of the wheel torque that is generated based on a set of mechanical torque transfer models of at least a powertrain and a brake of the vehicle and a second estimate of wheel torque that is generated based on an inverted longitudinal dynamic model, and a second non-linear filter of the dual filter framework is configured to generate the updated estimate of a wheel torque and vehicle velocity based on a torque delivery damping process model on vehicle motion.

Wheel torque estimation for autonomous driving

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

Methods, systems and apparatus for autonomous vehicle path planning and path navigation are described. One example system includes an offline server configured to generate a library of optimal paths for navigating a geographic area, wherein the geographic area is represented as a grid node map and an orientation grid bin map and wherein the optimal paths correspond to paths between pairs of grid node pairs in the grid node map based on optimization criteria, a storage device on the autonomous vehicle for storing the library of optimal paths, and an online server located on the autonomous vehicle configured to access information from the library of optimal paths from the storage device based on a current position and a current heading of the autonomous vehicle, and navigating the autonomous vehicle through the geographic area based on the information.

Optimal path library for local path planning of an autonomous vehicle

Systems and methods for dynamic predictive control of autonomous vehicles are disclosed. In one aspect, an in-vehicle control system for a semi-truck includes one or more control mechanisms configured to control movement of the semi-truck and a processor. The system further includes computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the processor to receive a desired trajectory and a vehicle status of the semi-truck, determine a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, determine at least one quadratic program (QP) problem based on the dynamic model, generate at least one control command for controlling the semi-truck by solving the at least one QP problem, and provide the at least one control command to the one or more control mechanisms.

Systems and methods for dynamic predictive control of autonomous vehicles

Described is a two-level optimal path planning process for autonomous tractor-trailer trucks which incorporates offline planning, online planning, and utilizing online estimation and perception results for adapting a planned path to real-world changes in the driving environment. In one aspect, a method of navigating an autonomous vehicle includes determining, by an online server, a current vehicle state of the autonomous vehicle in a mapped driving area. The method includes receiving, by the online server from an offline path library, a path for the autonomous driving vehicle through the mapped driving area from the current vehicle state to a destination vehicle state, and receiving fixed and moving obstacle information. The method includes adjusting the path to generate an optimized path that avoids the fixed and moving obstacles and ends at a targeted final vehicle state, and navigating the autonomous vehicle based on the optimized path.

Two-level path planning for autonomous vehicles

An autonomous vehicle includes a detection system for identifying the presence changes in wind incident on the autonomous vehicle, particularly wind gusts. The detection system may include one or more wind sensors, particularly those configured to detect wind incident on the vehicle from a direction that is transverse or perpendicular to the direction of motion of the autonomous vehicle. Additionally, systems may be present that correlate the detected wind gusts to changes in the behavior of the autonomous vehicle. The autonomous vehicle may react to the detected wind gusts by altering the vehicle's trajectory, by stopping the vehicle, or by communicating with a control center for further instructions.

Wind gust detection by an autonomous vehicle

Methods, systems, and devices related to a method of controlling an autonomous vehicle, in particular, an autonomous diesel-engine truck are disclosed. In one example aspect, the method includes determining an available engine brake torque generation mechanism for reducing a current speed of the autonomous vehicle to a lower speed and selecting a brake mode corresponding to the engine brake torque availability. In case a rate of speed reduction is equal to or smaller than a threshold, the brake mode includes only an engine brake in which engine exhaust valve opening is adjusted for reducing the current speed. The threshold determined in part based on the available engine brake torque, gear position of the transmission, and the online estimated vehicle longitudinal dynamic model. In case the rate of speed reduction is greater than the threshold, the brake mode incudes a combination of the engine brake and the foundation brake.

Adaptive brake mode selection

Techniques are described for determining an occurrence of theft in a vehicle. An example processor implemented method comprises receiving, by a computer located in an autonomous vehicle and at a first time, a first torque value that indicates a first amount of torque applied by an engine of the autonomous vehicle to drive the autonomous vehicle, receiving, at a second time that is later in time than the first time, a second torque value that indicates a second amount of torque applied by the engine of the autonomous vehicle, determining that a difference between a first value and a second value is greater than a pre-determined value, where the first value and the second value are functions of at least the first torque value and at least the second value respectively, and displaying, in response to the determining, a message that indicates a theft detection in the autonomous vehicle.

Techniques to detect theft of goods in vehicles

Techniques are described to determine parameters and/or values for a control model that can be used to operate an autonomous vehicle, such as an autonomous semi-trailer truck. For example, a method of obtaining a data-driven model for autonomous driving may include obtaining data associated with a first set of variables that characterize movements of an autonomous vehicle over time and commands provided to the autonomous vehicle over time, determining, using at least the first set of data, non-zero values and an associated second set of variables that describe a control model used to perform an autonomous driving operation of the autonomous vehicle, and calculating values for a feedback controller that describes a transfer function used to perform the autonomous driving operation of the autonomous vehicle driven on a road.

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San Diego, CA, United States of America

Arda Kurt