Baidu USA LLC

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

Microsoft Technology Licensing, LLC

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

Xiang Yu

Fan Zhu

Guang Yu Yang

Qi Kong

Qi Luo

Sen Hu

Jingao Wang

Yuchang Pan

Zhenguang Zhu

Jiarui He

Haoyang Fan

Li Zhuang

Wenli Yang

Zhongpu Xia

Xiaoxin Fu

Hongye Li

Chunming Zhao

Weicheng Zhu

Hardik Bati

Liangliang Zhang

Yifei Jiang

Dong Li

Yajia Zhang

Sanjay Sureshchandra Dubey

Shuang Wu

Edward Wu

Lihong Pei

Yawen Wei

Haiyan Luo

John P Moore

Qi Liu

Swetha Nagabhushan Karthik

Lei Ma

Waitat Peter Poon

Guang Yang

Techniques are provided for server-to-server interaction for content item selection. A system receives a request for one or more content items from a client device. In response to receiving the request, the system generates multiple content requests. Each content request is sent to a different content provider of multiple content providers. At least one of the content requests is transmitted over a network to a content provider that is remote relative to the system. The system receives multiple responses, each of which is from a different content provider of the multiple content providers. Each response identifies a different content item. The system makes a determination that at least one content item is associated with a particular web domain. The system selects, based on the determination, a particular content item of the plurality of content items and causes the particular content item to be transmitted to the client device.

Server to server interaction in content item selection events

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

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

A lane departure detection system detects that an autonomous driving vehicle (ADV) is departing from the lane in which the ADV is driving based on sensor data captured when the ADV contact a deceleration curb such as a speed bump laid across the lane. When the ADV contacts the deceleration curb, the lane departure detection system detects and calculates an angle of a moving direction of the ADV vs a longitudinal direction of the deceleration curb. Based on the angle, the system calculates how much the moving direction of the ADV is off compared to a lane direction of the lane. The lane direction is typically substantially perpendicular to the longitudinal direction of the deceleration curb. A control command such as a speed control command and/or a steering control command is generated based on the angle to correct the moving direction of the ADV.

Deceleration curb-based direction checking and lane keeping system for autonomous driving vehicles

Generally discussed herein are methods, systems, and apparatuses for tracking user interaction with sponsored and/or unsponsored content. A method can include receiving a tracking event packet including data fields comprising a packet identification, a tracking event identifier, a content identifier, a cost, a campaign identifier, and/or an advertiser identifier, determining whether the tracking event packet is a duplicate based on the packet identification, aggregating non-duplicate tracking event packets by advertiser identification such that packets with advertiser identifications that hash to a same value are aggregated together, and transferring aggregated tracking event packets to a first database and a second database, the first database includes data stored for analytics and the second database includes data stored for billing and campaign performance monitoring.

Tracking interaction with sponsored and unsponsored content

In one embodiment, an autonomous driving vehicle (ADV) speed following system determines how much and when to apply a throttle or a brake control of an ADV to maneuver the ADV around, or to avoid, obstacles of a planned route. The speed following system calculates a first torque force to accelerate the ADV, a second torque force to counteract frictional forces and wind resistances to maintain a reference speed, and a third torque force to minimize an initial difference and external disturbances thereafter between predefined target speed and actual speed of the ADV over a planned route. The speed following system determines a throttle-brake torque force based on the first, second, and third torque forces and utilizes the throttle-brake torque force to control a subsequent speed of the ADV.

Method and system for autonomous vehicle speed following

In one embodiment, it is determined that an ADV is about to decelerate based on perception of a driving environment surrounding the ADV. In addition, if there is another vehicle that is following the ADV, a distance between the ADV and the following vehicle, as well as the speed of the following vehicle, is determined. A deceleration rate that is required for the following vehicle to avoid a collision with the ADV is determined based on the distance between the ADV and the following vehicle and the speed of the following vehicle. If the deceleration rate is greater than a predetermined threshold, a brake light and an emergency light of the ADV are turned on to warn the following vehicle that the ADV is about to rapidly decelerate as it is treated as an emergency situation.

Method and system for automated vehicle emergency light control of an autonomous driving vehicle

In one embodiment, when an ADV is driving on a road segment, a driving parameter is recorded in response to a first control command. A difference between the first driving parameter and a target driving parameter corresponding to the first control command is determined. In response to determining that the difference exceeds a predetermined threshold, a second control command is issued to compensate the difference and cause the ADV to drive with a second driving parameter closer to the target driving parameter. A slope status of the road segment is derived based on at least the second control command. Map data of a map corresponding to the road segment of the road is updated based on the derived slope status. The updated map can be utilized to generate and issue proper control commands in view of the slope status of the road when the ADV drives on the same road subsequently.

Method and system for updating maps based on control feedbacks of autonomous driving vehicles

State of an autonomous driving vehicle (ADV) is measured and stored for a location and speed of the ADV. Later, the state of the ADV is measured for the location and speed corresponding to a previously stored state of the ADV at the same location and speed. Fields of the measured stored states of the ADV are compared. If one or more differences between the measured and stored ADV states exceeds a threshold, then one or more control input parameters of the ADV is adjusted, such as steering, braking, or throttle. Differences may be attributable to road conditions or to state of servicing of the ADV. Differences between measured and stored states of the ADV can be passed to a service module. Service module can access crowd sourced data to determine whether one or more control input parameters for a driving state of one or more ADVs should be updated.

Data-based control error detection and parameter compensation system

When an ADV is detected to transition from a manual driving mode to an autonomous driving mode, a first pedal value corresponding to a speed of the ADV at a previous command cycle during which the ADV was operating in the manual driving mode is determined. A second pedal value is determined based on a target speed of the ADV at a current command cycle during which the ADV is operating in an autonomous driving mode. A pedal value represents a pedal percentage of a maximum pedal pressure or maximum pedal pressed distance of a throttle pedal or brake pedal from a neutral position. A speed command is generated and issued to the ADV based on the first pedal value and the second pedal value, such that the ADV runs in a similar acceleration before and after switching from the manual driving mode to the autonomous driving mode.

Method and system for controlling autonomous driving vehicle reentering autonomous driving mode

In one embodiment, when speed control command (e.g., throttle, brake commands) is issued based on a target speed, a first feedback parameters is determined based on an expected speed and an actual speed of the ADV in response to the speed control command. A second feedback parameter is determined by applying a speed control parameter adjustment (SCPA) model to a set of input parameters that are captured or measured at the point in time. The set of input parameters represents a driving environment of the ADV at the point in time. One or more control parameters of a speed controller of the ADV is adjusted based on the first feedback parameter and the second feedback parameter, where the speed controller is configured to generate and issue speed control commands. Subsequent speed control commands can be generated based on the adjusted speed control parameters of the speed controller.

Speed control parameter estimation method for autonomous driving vehicles

In one embodiment, a request is received to turn the autonomous driving vehicle (ADV) from a first direction to a second direction. In response to the request, a number of segment masses of a number of segments of the ADV are determined. The segment masses are located on a plurality of predetermined locations within a vehicle platform of the ADV. A location of a mass center for an entire ADV is calculated based on the segment masses of the segments of the ADV, where the mass center represents a center of an entire mass of the entire ADV. A steering control command based on the location of the mass center of the entire ADV for steering control of the ADV.

Automatic steering control reference adaption to resolve understeering of autonomous driving vehicles

In one embodiment, planning data is received, for example, from a planning module, to drive an autonomous driving vehicle (ADV) from a starting location and a destination location. In response, a series of control commands are generated based on the planning data, where the control commands are to be applied at different points in time from the starting location to the destination location. A cost is calculated by applying a cost function to the control commands, a first road friction to be estimated in a current trip, and a second road friction estimated during a prior trip from the starting location to the destination location. The first road friction of the current trip is estimated using the cost function in view of a prior termination cost of the prior trip, such that the cost reaches minimum.

Method and system for determining road frictions of autonomous driving vehicles using learning-based model predictive control

In one embodiment, a lane departure detection system detects at a first point in time that a wheel of an ADV rolls onto a lane curb disposed on an edge of a lane in which the ADV is moving. The system detects at a second point in time that the wheel of the ADV rolls off the lane curb of the lane. The system calculates an angle between a moving direction of the ADV and a lane direction of the lane based on the time difference between the first point in time and the second point in time in view of a current speed of the ADV. The system then generates a control command based on the angle to adjust the moving direction of the ADV in order to prevent the ADV from further drifting off the lane direction of the lane.

Lane curb assisted off-lane checking and lane keeping system for autonomous driving vehicles

Described is a system and method that provides the ability for an autonomous driving vehicle (ADV) to determine (or estimate) one or more control characteristics for the ADV. In order to determine these control characteristics, the system may perform one or more driving maneuvers such as an acceleration or deceleration maneuver, and a constant velocity maneuver. By performing these maneuvers using various known forces, the system may then perform various calculations to obtain one or more unknown characteristics. For example, the system may determine as estimated mass of the ADV, and as a result, adjust (or tune) various controls of the ADV based on the estimated mass.

Growing community of inventors

Sunnyvale, CA, United States of America

Xiang Yu