Baidu USA LLC

Oracle International Corporation

Baidu.com Times Technology (beijing) Co. Ltd.

Xiangtao You

Manjiang Zhang

Tiffany Zhang

Oh Kwan

Shuai Wang

Davy Huang

Xu Zhou

Weiyu Wendy Lu

Fu-Kang Liao

Erwin Tsaur

Xiao-dong Zhao

Zhou Xu

In one embodiment, a sensor unit to be utilized in an autonomous driving vehicle (ADV) includes a sensor interface that can be coupled to a number of sensors mounted on a number of different locations of the ADV. The sensor unit further includes a host interface that can be coupled to a host system such as a planning and control system utilized to autonomously drive the vehicle. The sensor unit further includes a number of data transfer modules corresponding to the sensors. Each of the data transfer modules can be configured to operate in one of the operating modes, dependent upon the type of the corresponding sensor. The operating modes include a low latency mode, a high bandwidth mode, and a memory mode.

Data transfer logic for transferring data between sensors and planning and control of an autonomous driving vehicle

A sensor unit includes a sensor interface and a host interface. The sensor interface can be coupled to a number of sensors mounted on various locations of the ADV. The host interface can be coupled to a host system that is configured to perceive a driving environment surrounding the ADV based on sensor data obtained from the sensors and to plan a path to autonomously drive the ADV. The sensor unit further includes a sensor processing module and a sensor control module coupled to the sensor interface, and a time module coupled to the sensor processing module and the sensor control module. The sensor processing module is configured to process sensor data received from the sensors via the sensor interface. The sensor control module is configured to control operations of the sensors. The time module is configured to generate time to synchronize timing of the operations of the sensors.

Sensor integration and synchronization unit for an autonomous driving vehicle

In one embodiment, a system determines a difference in time between a local time source and a time of a GPS sensor. The system determines a max limit in difference and a max recovery increment or max recovery time interval for a smooth time source recovery. The system determines that the difference between the local time source and a time of the GPS sensor to be less than the max limit. The system plans a smooth recovery of the time source to converge the local time source to a time of the GPS sensor within the max recovery time interval. The system generates a timestamp based on the recovered time source to timestamp sensor data for a sensor unit of the ADV.

Time source recovery system for an autonomous driving vehicle

In one embodiment, a system receives a number of times from a number of time sources including sensors and real-time clocks (RTCs), wherein the sensors are in communication with an autonomous driving vehicle (ADV) and the sensors include at least a GPS sensor. The system generates a difference histogram based on a time for each of the time sources for a difference between a time of the GPS sensor and a time for each of the other sensors and RTCs. The system ranks the sensors and RTCs based on the difference histogram. The system selects a time source from one of the sensors or RTCs with a least difference in time with respect to the GPS sensor. The system generates a timestamp based on the selected time source to timestamp sensor data for a sensor unit of the ADV.

Time source ranking system for an autonomous driving vehicle

A sensor unit used in an ADV includes a sensor interface that can be coupled to cameras mounted on the ADV. The sensor unit further includes a host interface that can be coupled to a host system. The host system is configured to perceive a driving environment surrounding the ADV based on at least image data obtained from the cameras and to plan a path to autonomously drive the ADV. The sensor unit further includes one or more data acquisition modules corresponding to the cameras. Each data acquisition module includes a pixel alignment module and a frame processing module. The pixel alignment module is configured to reformat pixels of image data from an original format associated with the corresponding camera to a predetermined format. The frame processing module is configured to generate an image frame based on the image data and to transmit the image frame to the host system.

Image data acquisition logic of an autonomous driving vehicle for capturing image data using cameras

Systems and methods for allowing software developers and the like to embed various different types of code and/or data manipulations into a software product. The embedded code manipulation instructions may be accessed and executed by test engineers or other users to induce fault paths and the like in the software product at run-time for analyzing the software's response to the induced fault paths. For instance, a user application of a software manipulation framework may expose a list of all of the instruction sets to a test engineer or the like in conjunction with one or more testing procedures to be performed on the software. Unless accessed by the test engineer, all of the testing probes may be represented by noops or placeholders at runtime to limit performance impact on the software.

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Sunnyvale, CA, United States of America

Xiangtao You