Baidu USA LLC

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

Li Zhuang

Fan Zhu

Qi Kong

Jingao Wang

Guang Yu Yang

Weicheng Zhu

Haoyang Fan

Liangliang Zhang

Dong Li

Qi Luo

Xiang Yu

Yifei Jiang

Jiaming Tao

Hui Jiang

Yuchang Pan

Sen Hu

Chunming Zhao

Jiangtao Hu

Xin Xu

Yajia Zhang

Hongyi Sun

Zhenguang Zhu

Shuang Wu

Zhongpu Xia

Xiaoxin Fu

Feiyi Jiang

Lei Ma

Fuxiao Xin

Guang Yang

According to various embodiments, systems and methods described in the disclosure combine mapped features with point cloud features to improve object detection precision of an autonomous driving vehicle (ADV). The map features and the point cloud features can be extracted from a perception area of the ADV within a particular angle view at each driving cycle based on a position of the ADV. The map features and the point cloud features can be concatenated and provided to a neutral network for object detections.

Comprehensive and efficient method to incorporate map features for object detection with LiDAR

In one embodiment, instead of using map data, a relative coordinate system is utilized to assist perception of the driving environment surrounding an ADV for some driving situations. One of such driving situations is driving on a highway. Typically, a highway has fewer intersections and exits. The relative coordinate system is utilized based on the relative lane configuration and relative obstacle information to control the ADV to simply follow the lane and avoid potential collision with any obstacles discovered within the road, without having to use map data. Once the relative lane configuration and obstacle information have been determined, regular path and speed planning and optimization can be performed to generate a trajectory to drive the ADV. Such a perception system is referred to as a relative perception system based on a relative coordinate system.

Map-less and localization-less lane following method for autonomous driving of autonomous driving vehicles on highway

In one embodiment, in response to a route from a source location to a target location, the route is analyzed to identify a list of one or more driving scenarios along the route that match one or more predetermined driving scenarios. The route is segmented into a list of route segments based on the driving scenarios. At least one of the route segments corresponds to one of the identified driving scenarios. A path is generated based on the route segments for driving an autonomous driving vehicle from the source location to the target location. The path includes a number of path segments corresponding to the route segments. At least one of the path segments of the path is determined based on a preconfigured path segment of a predetermined driving scenario associated with the path segment, without having to calculating the same at real time.

Driving scenario based lane guidelines for path planning of autonomous driving vehicles

According to some embodiments, a system calculates a first trajectory based on a map and a route information. The system generates a path profile based on the first trajectory, traffic rules, and an obstacle information describing one or more obstacles perceived by the ADV. The system generates a speed profile based on the path profile, where the speed profile includes, for each of the obstacles, a decision to yield or overtake the obstacle. The system performs a quadratic programming optimization on the path profile and the speed profile to identify an optimal path with optimal speeds. The system generates a second trajectory based on the optimal path and optimal speeds to control the ADV autonomously according to the second trajectory.

Dynamic programming and quadratic programming based decision and planning for autonomous driving vehicles

Driving parameters (e.g., speed, heading direction) that an autonomous driving vehicle (ADV) likely utilize as target driving parameters are grouped into a number of ranges and one of the driving parameters in each range is selected as a driving parameter representative or a target driving parameter representing the respective range or segment. For each of the target driving parameters representing the ranges, a particle swarm optimization method is utilized to derive a set of most optimized coefficients for a controller (e.g., speed controller, steering controller) for controlling an ADV. A driving parameter to coefficient (parameter/coefficient) mapping table is generated to map a particular driving parameter representing a range of driving parameter to a set of one or more coefficients of a particular controller. The parameter/coefficient mapping table is utilized at real-time to configure a controller in response to a particular target driving parameter using the corresponding coefficients.

Method and system for determining optimal coefficients of controllers for autonomous driving vehicles

In one embodiment, a computer system generates a first vehicular path based on map information. The system collects data points representing geographical coordinates of vehicles that drove on a vehicular path lane at different points in time. The system segments the first vehicular path into path segments based on the collected data points. For each of the path segments, the system applies a smoothing function to select a subset of the data points that are within a predetermined proximity of the corresponding path segment and calculates a segment reference point to represent the path segment by combining the selected data points. The segment reference points of the path segments of the first vehicular path are interpolated to generate a second vehicular path such that the second vehicular path is used as a reference line to control ADVs driving on the first vehicular path.

Scalable smooth reference path generator for autonomous driving vehicles

When generating a control command of an autonomous driving vehicle (ADV), a pitch status and/or a roll status of the road is determined. The control command is adjusted based on the pitch status and the roll status. For example, when an ADV is driving on an uphill or downhill road, a pitch status of the road is determined and a speed control command will be generated based on the pitch status of the road, such that the ADV have a similar acceleration rate as of driving on a flat road. Similarly, when the ADV is driving on a road that is tilted or rolled left or right, a roll status of the road is determined and a steering control command will be generated in view of the roll status of the road, such that the ADV have a similar heading direction as of driving on a flat road.

Speed control and steering control assistant based on pitch status and roll status of autonomous driving vehicle

In one embodiment, autonomous driving control for an autonomous vehicle is provided by determining a first state of the autonomous vehicle from among a number of states and determining whether one or more conditions have been satisfied, based on current information and historical information of the autonomous vehicle. A next state of the autonomous vehicle and a transition of the autonomous vehicle from the first state to the next state are determined, based on the one or more conditions that are determined to have been satisfied. Based on the transition of the autonomous vehicle, one of a plurality of motion plans is selected.

Method and system for operating autonomous driving vehicles based on motion plans

According to one embodiment, a first keyword-to-image (keyword/image) mapping table is provided. The first keyword mapping table includes a number of entries, each entry mapping a keyword to one or more image identifiers (IDs) identifying one or more images. For each of the keywords of the first keyword/image mapping table, an analysis is performed on the keyword to determine one or more related keywords that are related to the keyword. One or more additional entries corresponding to the one or more related keywords are generated to be incorporated into the first keyword/image mapping table to generate a second keyword/image mapping table. The second keyword/image mapping table is utilized to associated a particular image to a particular content item related to a particular keyword.

Method for categorizing images to be associated with content items based on keywords of search queries

In one embodiment, motion planning and control data is received, indicating that an autonomous vehicle is to move from a first point to a second point of a path. The motion planning and control data describes a plurality of routes from the first point to the second point within the path. For each of the routes, a simulation of the route is performed in view of physical characteristics of the autonomous vehicle to generate a simulated route. A controlling error is calculated, the controlling error representing a discrepancy between the route and the simulated route. One of the routes is selected based on controlling errors between the routes and associated simulated routes. The autonomous vehicle is operated to move from the first point to the second point according to the selected route.

Controlling error corrected planning methods for operating autonomous vehicles

In one embodiment, autonomous driving control is provided for an autonomous vehicle changing from a source lane to a target lane. Using a topological map, a reference node is selected in the source lane. With respect to the reference node, an earliest node is determined in the source lane at which it is first possible for the vehicle to change lanes and a last node is determined in the source lane after which it is no longer possible to change lanes. A range of the source lane is determined for which the vehicle can change lanes.

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Sunnyvale, CA, United States of America

Li Zhuang