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

Baidu USA LLC

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

Wenli Yang

Yuchang Pan

Tianlei Zhang

Haowen Sun

Zhenguang Zhu

Fan Zhu

Guang Yu Yang

Qi Luo

Qi Kong

Xiang Yu

Sen Hu

Jingao Wang

Lintao Li

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

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

The present disclosure discloses embodiments of methods and apparatuses for indicating a vehicle moving state. In some embodiments, a method includes receiving a vehicle driving instruction; detecting a driving environment outside the vehicle; determining a driving strategy for executing the vehicle driving instruction in the driving environment; determining a driving track instructed by the driving strategy; and projecting the driving track on a road when the driving environment satisfies a preset condition. This implementation can clearly indicate the position that a vehicle is about to occupy, thereby improving the effect of interaction between the vehicle and other vehicles or pedestrians.

Method and apparatus for indicating vehicle moving state

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

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.

Real vehicle in-the-loop test system and method

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

The present invention provides a local trajectory planning method and apparatus for a smart vehicle, pre-acquiring path planning information from a starting location to a destination; the method comprising: determining a target lane; sampling alternative curves from a current location of the smart vehicle to a target lane according to the path planning information; performing speed planning for the sampled alternative curves according to a current travel environment; selecting one of the alternative curves after the speed planning is performed as a target trajectory. Local trajectory planning of the smart vehicle is achieved through the present invention.

Local trajectory planning method and apparatus for smart vehicles

The present invention discloses a method and an apparatus of constructing a test scenario of an unmanned vehicle. The method comprises: obtaining a scenario attribute set by the user; respectively determining a map and an agent matching with the scenario attribute; generating a test scenario according to the determined map and agent. The solution of the present invention can be used to improve the efficiency of constructing the test scenario.

Method and apparatus for constructing test scenario of unmanned vehicles

In one embodiment, an autonomous driving vehicle (ADV) steering control system determines how much and when to apply a steering control to maneuver obstacles of a planned route. The steering control system calculates a first steering angle based on a target directional angle and an actual directional angle of the ADV, a second steering angle based on a target lateral position and an actual lateral position of the ADV to maneuver a planned route, an object, or an obstacle course. The steering control system determines a target steering angle based on the first steering angle and the second steering angles and utilizes the target steering angle to control a subsequent steering angle of the ADV.

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Beijing, China

Wenli Yang