Tusimple, Inc.

Yujia Wu

Zijie Xuan

Arda Kurt

Haoming Sun

Jun Chen

Aaron Havens

Lei Wang

Yufei Zhao

Tianqu Shao

Xiaoling Han

Zehua Huang

Xing Sun

Todd B Skinner

Yishi Liu

Junbo Jing

Chasen Sherman

Yue Pan

Shuhan Yang

Zhiqi Gong

Weiyang Zhang

Jianqiu Cao

A system for implementing a lane bias maneuver to negotiate a curved road comprises an autonomous vehicle and a control device. The control device determines that the autonomous vehicle is approaching a curved road. The control device determines a road radius of the curved road. The control device calculates a first lane bias adjustment amount associated with a road curvature of the curved road based on the road radius. The control device calculates a second lane bias adjustment amount associated with a trailer angle between a trailer and a semi-truck tractor unit of the autonomous vehicle. The control device calculates a total lane bias adjustment amount by combining the first and second lane bias adjustment amounts. The control device instructs the autonomous vehicle to perform a lane bias maneuver that comprises driving the autonomous vehicle off-center in a curved lane based on the total lane bias adjustment amount.

Lane bias maneuver for autonomous vehicles to negotiate a curved road

A system includes an autonomous vehicle (AV) configured to travel along a road and a control device communicatively coupled to the AV. The control device determines that the AV should move from a current lane of the road to an adjacent lane of the road. The control device determines two or more candidate windows into which the AV may move in the adjacent lane. Each candidate window corresponds to a space in the adjacent lane between two vehicles traveling in the adjacent lane. The control device determines that the AV should move into a first candidate window, and, in response to this determination, causes the AV to begin moving along a trajectory leading to the first candidate window (e.g., by accelerating or decelerating).

Proactive lane change for autonomous vehicles

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

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.

Data-driven control for autonomous driving

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Emeryville, CA, United States of America

Yujia Wu