Tusimple, Inc.

Tianqu Shao

Arda Kurt

Junbo Jing

Chasen Sherman

Xing Sun

Haoming Sun

Yujia Wu

Lei Wang

Yufei Zhao

Zijie Xuan

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

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

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

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.

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San Diego, CA, United States of America

Tianqu Shao