Baidu USA LLC

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

Baidu Online Network Technology (beijing) Co., Ltd.

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

Yuchang Pan

Fan Zhu

Qi Kong

Zhenguang Zhu

Guang Yu Yang

Jingao Wang

Qi Luo

Xiang Yu

Sen Hu

Wenli Yang

Haoyang Fan

Tianlei Zhang

Xiaoxin Fu

Jiarui He

Haowen Sun

Chunming Zhao

Hui Jiang

Zhongpu Xia

Liangliang Zhang

Jiaming Tao

Xin Xu

Hongye Li

Feiyi Jiang

Weicheng Zhu

Li Zhuang

Zhiyuan Chen

Dayang Hao

Fuxiao Xin

Guang Yang

A method, apparatus and server for real-time learning of a travelling strategy of a driverless vehicle are provided. The method includes: when a first travelling strategy of the driverless vehicle is unable to be generated, recording travelling trajectories of other vehicles on a road; when a number of vehicles on a same travelling trajectory is greater than a preset first number threshold, generating a second travelling strategy using the same travelling trajectory; and controlling the driverless vehicle to travel using the second travelling strategy. A situation in which a driverless vehicle is unable to normally generate a travelling strategy can be improved effectively.

Method, apparatus and server for real-time learning of travelling strategy of driverless vehicle

A method and apparatus for processing a driving reference line, and a vehicle are provided. The method comprises: acquiring position information of at least one obstacle in a range covered by an original driving reference line; determining a safety range parameter based on the position information of the at least one obstacle; adjusting a coefficient of a polynomial curve corresponding to the original driving reference line based on the safety range parameter, to obtain an adjusted polynomial curve; and determining an adjusted driving reference line based on the updated polynomial curve. The safety problem of the driving reference line caused by the insufficient obstacle avoidance ability of the driving reference line is solved.

Method and apparatus for processing driving reference line, and vehicle

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 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

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

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

The present disclosure discloses methods and systems for testing the vehicle. In some embodiments, a method includes receiving, by an emulation server, a test task and a test scenario set required for executing the test task sent from a client; distributing, by the emulation server, each of the test scenarios to first emulation executors respectively, and sending the test task to each of the first emulation executors; acquiring, by the emulation server, a test result of the test task from each of the first emulation executors; and comparing, by the emulation server, the acquired test result with a preset test standard to generate feedback information of the test task, and sending the feedback information to the client.

Method and system for testing 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

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, 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

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

The present invention discloses a vehicle control method and apparatus and a method and apparatus for acquiring a decision-making model. The vehicle control method, comprising: during travel of an unmanned vehicle, acquiring current external environment information and map information in real time; determining vehicle state information corresponding to the external environment information and map information acquired each time according to a decision-making model obtained by pre-training and reflecting correspondence relationship between the external environment information, map information and vehicle state information, and controlling a travel state of the unmanned vehicle according to the determined vehicle state information. Application of the solution of the present invention can improve security and reduce the workload.

Vehicle control method and apparatus and method and apparatus for acquiring decision-making model

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

The present invention discloses a method and apparatus of obtaining feature information of a simulated agent. The method further comprises: for each agent class, respectively obtaining feature information of real agents belonging to the class and freely participating in traffic activities, the number of real agents belonging to each class being greater than one; for each agent class, extracting representative feature information from feature information of each real agent belonging to the class, and taking the extracted feature information as feature information of simulated agents belonging to this class. The solution of the present invention may be applied to improve correctness of testing results of unmanned vehicles.

Method and apparatus of obtaining feature information of simulated agents

Embodiments of real vehicle in-the-loop test systems and methods are disclosed. The system can include a sensor configured to acquire state information and location information of a real vehicle and environment information of an emulation test environment where the vehicle is located; an interaction module configured to acquire a test task in response to the instruction entered by a user, an emulation test environment of the test task comprises a test situation, a map, and an intelligent agent; a vehicle sensing module configured to acquire the state information, the location information, and the environment information from the sensor; and a test task control module configured to receive the test task from the interaction module, the state information and the location information from the vehicle sensing module and load them to the emulation test environment, control the real vehicle to execute the test task, and generate and send the test result.

Growing community of inventors

Beijing, China

Yuchang Pan