Baidu USA LLC

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

Apollo Intelligent Driving Technology (beijing) Co., Ltd.

Other

Fan Zhu

Qi Kong

Jingao Wang

Guang Yu Yang

Lin Ma

Xin Xu

Yifei Jiang

Dong Li

Qi Luo

Xiang Yu

Liangliang Zhang

Sen Hu

Yuchang Pan

Jiaming Tao

Ning Yu

Zhenguang Zhu

Jiangtao Hu

Weicheng Zhu

Haoyang Fan

Li Zhuang

Leibing Lv

Fan Yang

Hui Jiang

Jingjing Xue

Xiaoxin Fu

Shuang Wu

Zhongpu Xia

Zhuang Li

Chunming Zhao

Jiarui He

Bin Gao

Kecheng Xu

Yongyi Sun

Wenli Yang

Hongye Li

Dongchao Gao

Dongchun Yao

Feiyi Jiang

Shuang Zhang

Bei Liu

Dayang Hao

Zhiyuan Chen

Youling Zou

Yue Wang

Yajia Zhang

Chengfa Wang

Junping Wang

Yingnan Liu

Yu Dong

Guohao Gong

Lei Ma

Changchun Liu

Yaqin Chen

Li Dong

Fuxiao Xin

According to one embodiment, a dynamic parameter server is provided in an ADV to update parameters of an autonomous driving system (ADS) of the ADV in real time without requiring the reboot of the ADS. The dynamic parameter server can obtain new parameters from a configuration file created by users based on their experiences and expectations. Each new parameter is mapped to certain physical conditions. When the ADV encounters the physical conditions mapped to a particular parameter, the dynamic parameter server can broadcast the new parameters to the ADS, which can use the new parameters to control the ADV. The physical conditions can be used as selection factors for the dynamic parameter to determine which ADS parameter to update.

Dynamic parameter server for autonomous driving vehicles

Embodiments of the present disclosures disclose a method and a system to notify an operator that perception sensors of an autonomous driving vehicle (ADV) need to be recalibrated. In one embodiment, a system perceives a surrounding environment of an autonomous driving vehicle (ADV), including one or more obstacles. The system extracts feature information from previously stored point clouds mapping a three-dimensional surrounding environment of the ADV. The system identifies one or more matching features between the extracted feature information and features of the one or more obstacles. The system determines an average offset distance based on each of the matching features. The system determines an average offset distance distribution based on the average offset distance over a period of time. The system sends an alert to the ADV to alert that the one or more sensors is recommended for recalibration if the average offset distance distribution satisfies a predetermined condition.

Point clouds based lidar recalibration system for autonomous vehicles

In response to a request to park an ADV in a parking lot, a set of parking-trajectories associated with a set of predetermined locations near one or more parking spots in the parking lot may be obtained, where the set of parking-trajectories was previously generated based on prior collected planning and control data of the parking lot. Each parking-trajectory of the set of parking-trajectories may correspond to one parking spot of the one or more parking spots in the parking lot. A parking-trajectory may be selected from the set of parking-trajectories based on a current location of the ADV. The ADV may be controlled to park in a corresponding parking spot according to the selected parking-trajectory.

Parking-trajectory generation method combined with offline and online solutions

A driving environment is perceived based on sensor data obtained from a plurality of sensors mounted on an ADV. A first set of features is extracted from the sensor data representing the driving environment, where the first set of features include one or more obstacles moving relative to the ADV. A precaution notification is generated by applying a precautionary slowdown predictive model to the first set of features and a second set of features determined based on internal states of the ADV. In response to the precaution notification, a speed planning is performed to lower a speed limit of the ADV to a predetermined percentage of the speed limit. The ADV is controlled to drive autonomously according to the lowered speed limit to perform a precautionary slowdown.

Precautionary slowdown speed planning

Embodiments of the present disclosures disclose a method and a system to generate a path planning trajectory with a random lateral shift for an autonomous driving vehicle (ADV). In one embodiment, a system generates a reference line to navigate the ADV from a start location to a destination location. The system determines a lateral shift distance value to shift a lane center for the reference line. The system generates a shifted trajectory using the reference line based on the lateral shift distance value. The system controls the ADV based on the shifted trajectory to navigate the ADV.

Random shift based path centering system for autonomous vehicles

A computer-implement method for operating an ADV is disclosed. A starting point and an ending point of a route along which an ADV is to be driven is determined. Whether each of the starting point and the ending point is within a first driving area having a lane boundary or a second driving area as an open space is determined. The route is divided into a first route segment and a second route segment. Dependent upon whether the starting point or the ending point is within the first driving area or the second driving area, the method comprises operating in one of an on-lane mode or an open-space mode to plan a first trajectory for the first route segment and operating in one of the on-lane mode or the open-space mode to plan a second trajectory for the second route segment.

Mixed regular and open-space trajectory planning method for autonomous driving vehicle

Embodiments of a system/method is disclosed to operate an autonomous driving vehicle (ADV). In one embodiment, a system perceives a driving environment surrounding the ADV using a plurality of sensors mounted on the ADV including one or more obstacles. The system receives traffic signal information from one or more traffic indicators identified within a predetermined radius of the ADV. For each of the one or more obstacles, the system determines if the obstacle is situated on a lane with traffic flow coordinated by the one or more traffic indicators. The system predicts a behavior of the obstacle based on the traffic signal information for the lane. The system plans a trajectory based on the predicted behaviors for the one or more obstacles to control the ADV based on the planned trajectory.

Obstacle prediction system for autonomous driving vehicles

A starting point, a set of one or more way points, and a destination point of a route along which the ADV is to be driven are determined. All lane segments near the starting point, the set of way points, and the destination point within a predetermined threshold distance are determined respectively. A set of route candidates are determined with an A-star (A*) searching algorithm based on a set of nodes representing all the lane segments near the starting point, the set of way points, and the destination point respectively. The route is selected from the set of route candidates based on respective costs of the set of route candidates. The ADV is being controlled to drive along the selected route autonomously.

Navigation route planning method for autonomous vehicles

In one embodiment, a set of dynamic vehicle parameters of an ADV associated with a road location are determined based on outputs of an IMU of the ADV measured when the ADV is traveling through the road location. Whether the set of dynamic vehicle parameters satisfy one of a first set of criteria and a second set of criteria is determined. In response to the dynamic vehicle parameters satisfying the first set of criteria for a first predetermined quantity of times or satisfying the second set of criteria for a second predetermined quantity of times, the speed limit associated with the road location is adjusted within a limited range spanning from a minimum speed limit to a maximum speed limit. Operations of the ADV when the ADV subsequently travels through the road location are controlled based at least in part on the adjusted speed limit.

IMU feedback based HD map speed limit adjustment system

Embodiments disclose a method and a system to operate an autonomous driving vehicle (ADV). In one embodiment, a system determines a source lane speed limit (SLSL) for a source lane (SL) and a target lane speed limit (TLSL) for a target lane (TL), where the SLSL is greater than the TLSL. The system generates a first trajectory for the ADV to follow the SL, the first trajectory having a modified speed corresponding to the TLSL instead of the SLSL. The system generates a second trajectory for the ADV to change from the SL to the TL, the second trajectory having a speed corresponding to the TLSL. The system determines a first cost and a second cost for the first trajectory and the second trajectory respectively based on a cost function for comparison. The system controls the ADV according to a trajectory with a lowest cost.

Lane change system for lanes with different speed limits

A method, apparatus, and system for determining average lane travel speeds is disclosed. A plurality of vehicles traveling in a same direction as the ADV in a plurality of lanes are identified. Over a first time period, the plurality of vehicles is tracked. At least a first quantity of representative vehicles within the plurality of vehicles that are representative of vehicles traveling in the lane over the first time period are identified. For each of the plurality of lanes, an average speed over the first time period of the representative vehicles associated with the lane is determined. A trajectory is planned for the ADV, wherein the planned trajectory moves toward a lane whose representative vehicles have a fastest average speed. Thereafter, control signals are generated to control operations of the ADV based on the planned trajectory.

Fastest lane determination algorithm under traffic jam

A computer-implement method for operating an ADV is disclosed. A first trajectory for the ADV to drive along is planned. The ADV is to autonomously drive along the first trajectory. An obstacle in an affected region of the ADV is detected based on sensor data obtained from a plurality of sensors mounted on the ADV. An expected residence time of the obstacle in the affected region is determined. Whether to plan a second trajectory or to wait for the obstacle to leave the affected region is determined based on the expected residence time of the obstacle in the affected region. A second trajectory for the ADV to drive along is planned and the ADV is to autonomously drive along the second trajectory, or the ADV is to wait for the obstacle to leave the affected region and to autonomously drive along the first trajectory afterwards.

Trajectory planning with obstacle avoidance for autonomous driving vehicles

In one embodiment, a set of LIDAR images are received representing the LIDAR point cloud data captured by a LIDAR device of an autonomous driving vehicle (ADV) at different point in times. Each of the LIDAR imagers is transformed or translated from a local coordinate system (e.g., LIDAR coordinate space) to a global coordinate system (e.g., GPS coordinate space) using a coordinate converter configured with a set of parameters. A first LIDAR reflection map is generated based on the transformed LIDAR images, for example, by merging the transformed LIDAR images together. The coordinate converter is optimized by adjusting one or more parameters of the coordinate converter based on the difference between the first LIDAR reflection map and a second LIDAR reflection map that serves as a reference LIDAR reflection map. The optimized coordinate converter can then be utilized to process LIDAR data for autonomous driving at real-time.

Automatic lidar calibration based on pre-collected static reflection map for autonomous driving

A method, apparatus, and system for filtering a point cloud generated by a LIDAR device in an autonomous vehicle is disclosed. A point cloud comprising a plurality of points is generated based on outputs of the LIDAR device. The point cloud is filtered to remove a first set of points in the plurality of points that correspond to noise based on one or more of: point intensity measurements, distances between points, or a combination thereof. Perception data is generated based on the filtered point cloud. Operations of the autonomous vehicle are controlled based on the perception data.

Neighbor-based point cloud filter system

A planned trajectory is analyzed by an L4 emergency braking module, before the trajectory is navigated by the autonomous driving vehicle (ADV). An L4 emergency braking signal is generated in response to an autonomous driving vehicle (ADV) determining that any portion of a planned trajectory of the ADV has a deceleration that is less than &#x2212;2 m/secand a time to collision with the ADV, by a vehicle following the ADV at a speed Z and a distance Y&#x3c;100 m, during a period of 1 to 8 seconds after time 0 of the planned trajectory, if for any time X&#x3f5;{1 second . . . 8 seconds} after time 0, wherein Tis the location of the ADV in the trajectory at time X. Optionally, a sharp braking command can be issued by the L4 emergency braking module in response to activating the emergency braking signal.

L4 auto-emergency light system for future harsh brake

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Sunnyvale, CA, United States of America

Fan Zhu