Baidu USA LLC

Weride Corp.

Yiqing Yang

Yu Long Cao

Guang Yu Yang

Jun Zhan

Siyang Yu

Zhang Li

Xuan Liu

Qiu Jin

Chao Lin

Hua Zhong

Huan Chen

Albert M Wu

Xuanzhu Lin

Xiong Wu

Chunlei Meng

Zhenzhen Kou

Zhiyuan Zuo

Tianming Lu

Zeyin Zhang

A method for generating a road map for vehicle navigation comprises: receiving an original road map having a crossroad junction, an inward lane entering the crossroad junction, and an outward lane leaving the crossroad junction; generating a reference path capable of guiding a vehicle driving from the inward lane to the outward lane through the crossroad junction; generating a polygonal hub that encompasses the crossroad junction, wherein the reference path enters and leaves the hub by crossing a first binder and a second binder, respectively; and generating automatically a type of the reference path based on the geometrical relationship between the first binder and the second binder.

Method for generating road map for autonomous vehicle navigation

An apparatus and a method for controlling a velocity of an autonomous driving vehicle is provided. The method includes steps of: obtaining information of an environment surrounding the vehicle when an obstacle is detected to be on a planning path of the vehicle; obtaining an initial reference velocity profile of the vehicle; determining a safety factor based on the initial reference velocity profile, the information of the environment and information of the vehicle, wherein the safety factor at least comprises a safety distance between the vehicle and the obstacle for the vehicle to follow the obstacle; determining an optimized reference velocity profile based on the information of the environment, the information of the vehicle and the safety factor; and performing the step of determining the safety factor by using the optimized reference velocity profile as the initial reference velocity profile and the step of determining the optimized reference velocity iteratively.

Apparatus and method for controlling velocity of autonomous driving vehicle, and storage medium

A method for generating a road map for vehicle navigation comprises: providing original road image data having an image of a road; identifying boundary lines defining respective lane segments of the road from the original road image data; associating traffic properties with the lane segments defined by the boundary lines, respectively; grouping the lane segments to generate road segments; and combining the road segments with traffic control objects corresponding thereto so as to generate the road map for vehicle navigation.

Method for generating road map for vehicle navigation and navigation device

An autonomous driving system includes a number of sensors and a number of autonomous driving modules. The autonomous driving system further includes a global store to store data generated and used by processing modules such as sensors and/or autonomous driving modules. The autonomous driving system further includes a task scheduler coupled to the sensors, the autonomous driving modules, and the global store. In response to output data generated by any one or more of processing modules, the task scheduler stores the output data in the global store. In response to a request from any of the processing modules for processing data, the task scheduler provides input data stored in the global store to the processing module. The task scheduler is executed in a single thread that is responsible for managing data stored in the global store and dispatching tasks to be performed by the processing modules.

Centralized scheduling system for operating autonomous driving vehicles

An event queue is maintained to store IO events generated from a number of sensors and timer events generated for a number of autonomous driving modules. For each of the events pending in the event queue, in response to determining that the event is an IO event, the data associated with the IO event is stored in a data structure associated with the sensor in a global store. In response to determining that the event is a timer event, a worker thread associated with the timer event is launched. The worker thread executes one of the autonomous driving modules triggered or initiated the timer event. Input data is retrieved from the global store and provided to the worker thread to allow the worker thread to process the input data.

Centralized scheduling system using event loop for operating autonomous driving vehicles

A global store is maintained to store a number of data structures. Each data structure includes a number of entries and each entry stores data of at least one event in a chronological order. Each data structure is associated with at least one sensor or an autonomous driving module of an autonomous driving vehicle. When a first event associated with a first autonomous driving module is received, where the first event includes a first topic ID, the first topic ID is hashed to identify a first data structure corresponding to the first event. A pointer pointing to a head of the first data structure is passed to the first autonomous driving module to allow the first autonomous driving module to process data associated with the first event.

Centralized scheduling system using global store for operating autonomous driving vehicles

Described is a system (and method) for generating a driving scenario for an autonomous driving simulator. The system may use a camera mounted to a vehicle as a cost effective approach to obtain real-life driving scenario data. The system may then analyze the two-dimensional image data to create a three-dimensional driving simulation. The analysis may include detecting objects (e.g. vehicles, pedestrians, etc.) within the two-dimensional image data and determining movements of the object based on a position, trajectory, and velocity of the object. The determined information of the object may then be projected onto a map that may be used for generating the three-dimensional driving simulation. The use of cost-effective cameras provides the ability to obtain vast amounts of driving image data that may be used to provide an extensive coverage of the potential types of driving scenarios an autonomous vehicle may encounter.

Vision-based driving scenario generator for autonomous driving simulation

Growing community of inventors

Sunnyvale, CA, United States of America

Yiqing Yang