Baidu USA LLC

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

Qi Kong

Fan Zhu

Guang Yu Yang

Jingao Wang

Qi Luo

Xiang Yu

Yifei Jiang

Sen Hu

Haoyang Fan

Yuchang Pan

Dong Li

Zhenguang Zhu

Weicheng Zhu

Liangliang Zhang

Li Zhuang

Hui Jiang

Lin Ma

Yajia Zhang

Shuang Wu

Zhongpu Xia

Jiarui He

Zhuang Li

Chunming Zhao

Jiangtao Hu

Jiaming Tao

Xiaoxin Fu

Xin Xu

Wenli Yang

Hongye Li

Feiyi Jiang

Youling Zou

Lei Ma

Changchun Liu

Yaqin Chen

Fuxiao Xin

Chuming Zhao

Wenda Zeng

Guang Yang

In one embodiment, a lateral drifting error is determined based on at least a current location of an ADV. The lateral drifting error is segmented into a first drifting error and a second drifting error using a predetermined segmentation algorithm. A planning module plans a path or trajectory for a current driving cycle (e.g., planning cycle) to drive the ADV from the current location for a predetermined period of time. The planning module performs a first drifting error correction on the trajectory by modifying at least a starting point of the trajectory based on the first drifting error to generate a modified trajectory. A control module controls the ADV to drive according to the modified trajectory, including performing a second drifting error correction based on the second drifting error.

Drifting correction between planning stage and controlling stage of operating autonomous driving vehicles

In response to a search query received from a client, a content item is identified from a content database based on one or more keywords associated with the search query. In addition, a search is performed in an image store to identify a list of image candidates based on one or more keywords associated with the search query. An image is selected from the list of image candidates using an image selection algorithm. Metadata associated with the content provider is inscribed onto the selected image to generate a customized image. The customized image is integrated with the content item to generate a composite content item such as a poster. As a result, the content represented by the composite content item is closely tied to the content provider. The composite content item is then transmitted to the client.

Method and system for dynamically overlay content provider information on images matched with content items in response to search queries

Embodiments of the present disclosure disclose a method for determining a vehicle control parameter, an apparatus for the same, a vehicle on-board controller, and an autonomous vehicle. An embodiment of the method comprises: obtaining a lateral offset sequence of a vehicle and a control input sequence of a controller for controlling a lateral output of the vehicle, wherein a lateral offset in the lateral offset sequence is for characterizing an offset between an actual lateral output of the vehicle and a desired lateral output; executing a step of determining a vehicle control parameter; wherein the executing the step of determining the vehicle control parameter includes: with the lateral offset sequence as an input and the control input sequence as the desired output, training a pre-established vehicle dynamic model to obtain a trained vehicle dynamic model; and determining the vehicle control parameter from the trained vehicle dynamic model.

Method for determining a vehicle control parameter, apparatus for the same, vehicle on-board controller, and autonomous vehicle

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, during a planning stage of autonomous driving of an autonomous driving vehicle (ADV), it is determined that the ADV needs to change lanes from a source lane to a target lane. A first trajectory is generated from a current location of the ADV in the source lane to the target lane such as a center line of the target lane. A lane shifting correction is then calculated based on the lane configuration of at least the source lane and/or target lane, as well as the current state of the ADV. Based on the lane shifting correction, at least the starting point of the first trajectory is modified, which in turn generates a second trajectory. In one embodiment, the starting point of the first trajectory is shifted laterally with respect to a heading direction of the source lane based on the lane shifting correction.

Method for determining lane changing trajectories for autonomous driving vehicles

According to an embodiment, a system generates a number of sample trajectories from a trajectory sample space for a driving scenario. The system determines a reward based on a reward model for each of the sample trajectories, where the reward model is generated using a rank based conditional inverse reinforcement learning algorithm. The system ranks the sample trajectories based on the determined rewards. The system determines a highest ranked trajectory based on the ranking. The system selects the highest ranked trajectory to control the ADV autonomously according to the highest ranked trajectory.

Auto-tuning motion planning system for autonomous vehicles

According to some embodiments, a system receives a first control command and a speed measurement of the ADV. The system determines an expected acceleration of the ADV based on the speed measurement and the first control command. The system receives an acceleration measurement of the ADV. The system determines a feedback error based on the acceleration measurement and the expected acceleration. The system updates a portion of the calibration table based on the determined feedback error. The system generates a second control command to control the ADV based on the calibration table having the updated portion to control the ADV autonomously according to the second control command.

Speed control command auto-calibration system for autonomous vehicles

A first localization system performs a first localization using a first set of sensors to track locations of the ADV along the path from a starting point to a destination point. A first localization curve is generated as a result representing the locations of the ADV along the path tracked by the first localization system. Currently, a second localization system performs a second localization using a second set of sensors to track the locations of the ADV along the path. A second localization curve is generated as a result representing the locations of the ADV along the path tracked by the second localization system. A system delay of the second localization system is determined by comparing the second localization curve against the first localization curve as a localization reference. The system delay of the second localization system can then be utilized to compensate path planning of the ADV subsequently.

Method for evaluating localization system of autonomous driving vehicles

Embodiments of the disclosure disclose a method and an apparatus for calibrating a vehicle control parameter, an on-board controller, and an autonomous vehicle; one embodiment of the method comprises: executing a calibrating step in response to reaching a preset update condition, the calibrating step comprises: obtaining a current offset data set, wherein the current offset data in the current offset data set are determined in a period of time including a current time point; determining a current offset data reference value for characterizing a value feature of the current offset data set; and performing offset correction for the vehicle control parameter based on an offset between the current offset data reference value and a historical offset data reference value. This embodiment implements autonomous calibration of the vehicle parameter based on changes of vehicle offset, such that the vehicle may accurately follow a corresponding control indicator.

Method and apparatus for calibrating a vehicle control parameter, vehicle controller and autonomous vehicle

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, where for each of the obstacles, the path profile includes a decision to yield or nudge to left or right of the obstacle. The system generates a speed profile based on the path profile in view of the traffic rules. The system performs a gradient descent optimization based on the path profile and the speed profile to generate a second trajectory representing an optimized first trajectory and controls the ADV according to the second trajectory.

Dynamic programming and gradient descent based decision and planning for autonomous driving vehicles

Embodiments of the present disclosure disclose a method for determining a vehicle control parameter, an apparatus for the same, a vehicle controller, and an autonomous vehicle. An embodiment of the method comprises: determining a current vehicle speed and an expected acceleration; determining, from a pre-generated parameter calibration table, a longitudinal control parameter corresponding to the current vehicle speed and the expected acceleration; wherein the parameter calibration table is obtained by: obtaining a training sample set, a training sample in the training sample set including the vehicle speed, the longitudinal control parameter and the acceleration; training a pre-established parameter calibration model with the vehicle speed and acceleration of respective samples in the training sample set as inputs and the longitudinal control parameter value in the training sample as an expected output; and obtaining the parameter calibration table based on the trained parameter calibration model.

Method and apparatus for determining a vehicle control parameter, vehicle on-board controller and autonomous vehicle

According to one embodiment, an ADV is controlled according to a first trajectory planned during a first planning cycle. A control error is determined which represents a drifting error at a first location of the ADV at a first point in time at the end of the first planning cycle. A second point in time is selected on the first trajectory. A second trajectory is generated from a second location on the first trajectory corresponding o the second point in time as a starting location of the second trajectory for a second planning cycle as a next planning cycle. A segment of the first trajectory between the first point in time and the second point in time is combined with the second trajectory to generate a third trajectory for the second planning cycle. The ADV is driven and controlled according to the third trajectory corresponding to the second planning cycle.

Method and system for stitching planning trajectories from consecutive planning cycles for smooth control execution of autonomous driving vehicles

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

A set of driving scenarios are determined for different types of vehicles. Each driving scenario corresponds to a specific movement of a particular type of autonomous vehicles. For each of the driving scenarios of each type of autonomous vehicles, a set of driving statistics is obtained, including driving parameters used to control and drive the vehicle, a driving condition at the point in time, and a sideslip caused by the driving parameters and the driving condition under the driving scenario. A driving scenario/sideslip mapping table or database is constructed. The scenario/sideslip mapping table includes a number of mapping entries. Each mapping entry maps a particular driving scenario to a sideslip that is calculated based on the driving statistics. The scenario/sideslip mapping table is utilized subsequently to predict the sideslip under the similar driving environment, such that the driving planning and control can be compensated.

Sideslip compensated control method for autonomous vehicles

According to one embodiment, in response to a search query received from a client, a search is performed in a content database to identify a list of one or more content items based on one or more keywords of the search query. A first search is performed in an image store to identify a first set of one or more images using a first image searching method. A second search is performed in the image store to identify a second set of one or more images using a second image searching method that is different than the first image searching method. A search result is transmitted to the client, the search result having at least a portion of the content items to the client. Each content item is associated with one of the images selected from the first set of images or the second set of images.

Growing community of inventors

Sunnyvale, CA, United States of America

Qi Kong