Qualcomm Incorporated

Beijing Baidu Netcom Science Technology Co., Ltd

Apollo Autonomous Driving USA LLC

Xiaoyi Zhu

Yin Huang

Ruowei Wang

Liang Zhang

Jiangtao Ren

Matthew Stuart Grob

Shu Jiang

Jiming Guo

Hao Liu

Haofeng Kou

Leslie Chiming Chan

Michael M Fan

Szu-Hao Wu

William Huang

Helen K Pan

Fuyang Zhao

In one embodiment, a microcontroller unit (MCU) receives an expected state of an autonomous driving vehicle (ADV) from a controller of the ADV, where the controller controls motions of the ADV using a control algorithm. The MCU receives sensor data from one or more sensors of the ADV. The MCU determine an actual state of the ADV based on the sensor data. The MCU determines a performance metric of the control algorithm based on the expected state and the actual state. In response to determining the performance metric has satisfied a predetermined condition, the MCU determines a plurality of weight values for the control algorithm. The MCU sends the plurality of weight values to the control system to tune one or more weight parameters of the control algorithm using the plurality of weight values, where the controller controls the ADV using the tuned control algorithm.

On-board parameter tuning for control module for autonomous vehicles

Embodiments include devices and methods for processing an image captured by an image sensor of a robotic vehicle. A processor of the robotic vehicle may calculate an image output orientation matrix for an image that is output by the image sensor. The processor may calculate image sensor orientation matrix the image sensor. The processor may calculate a body orientation matrix of the robotic vehicle. The processor may transform the image captured by the image sensor based on the image output orientation matrix, the image sensor orientation matrix, and the body orientation matrix.

Image output adjustment in a robotic vehicle

Embodiments include devices and methods for processing an image captured by an image sensor of an unmanned autonomous vehicle (UAV). A processor of the UAV may determine a body coordinate matrix of the UAV. The processor may determine an estimated rotation of the image sensor of the UAV. The processor may determine an estimated rotation of the UAV. The processor may transform an image captured by the image sensor based on the body coordinate matrix, the estimated rotation of the image sensor, and the estimated rotation of the UAV.

Image processing in an unmanned autonomous vehicle

Embodiments include devices and methods for adaptive image processing in an unmanned autonomous vehicle (UAV). In various embodiments, an image sensor may capture an image, while a processor of the UAV obtains attitude information from one or more attitude sensors. Such information may include the relative attitude of the UAV and changes in attitude. The processor of the UAV may determine a UAV motion mode based, at least in part, on the obtained attitude information. The UAV motion mode may result in the modification of yaw correction parameters. The processor of the UAV may further execute yaw filtering on the image based, at least in part, on the determined motion mode.

Adaptive motion filtering in an unmanned autonomous vehicle

A method, apparatus, and system for providing ad hoc venue-cast content to a plurality of receiving access terminals within a venue boundary. Ad hoc content may be generated at an access terminal, transmitted to a venue-cast server, and broadcast to a plurality of venue-cast receiving access terminals within a venue. In addition to broadcasting the ad hoc content, a notification is given to the plurality of receiving access terminals, notifying them regarding the availability of the ad hoc content.

Method and apparatus for ad hoc venue-cast service

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Beijing, China

Xiaoyi Zhu