A method, an apparatus, a computer storage medium and a terminal for implementing autonomous driving decision-making are disclosed. Image information is processed by the probabilistic object detection to obtain a probabilistic object detection result set containing multiple probabilistic object detection result. An uncertainty in the object detection process is estimated by the probabilistic object detection results contained in the set of the probabilistic object detection result. An environmental state information set is generated from the probabilistic object detection results in the probabilistic object detection result set and the perceptual information, then an optional action set considering the uncertainty is generated using a preset decision-making method, and an action for vehicle driving control is determined according to the optional action set and the environmental state information set.

Apparatuses, methods and systems including dynamic estimations of vehicle mass and road grade estimation are disclosed. One exemplary embodiment is a method including operating a vehicle system to propel a vehicle, determining with a controller a vehicle mass estimate and an uncertainty of the vehicle mass estimate, evaluating with the controller the uncertainty of the vehicle mass estimate relative to at least one criterion, if the uncertainty of the vehicle mass estimate satisfies the criterion, determining with the controller a road grade estimate, and controlling with the controller utilizing the road grade estimate at least one of a vehicle speed and an engine output.

A vehicle motion control device according to the present invention obtains a translation force for causing the position of a vehicle to trace a target travel path, on the basis of a lateral displacement amount which is an amount of displacement of the vehicle in a lateral direction with respect to a target movement point, obtain a rotational force for correcting an orientation of the vehicle with respect to the target travel path, on the basis of an orientation displacement amount which is an amount of displacement of the vehicle in a yaw direction with respect to the target movement point, weight the translation force and the rotational force on the basis of specifications relating to traveling of the vehicle, and output a control command for achieving a target lateral force obtained by adding up the weighted translation force and the weighted rotational force.

The present application discloses a method for determining an automatic driving feature, an apparatus, a device, a medium and a program product, and relates to automatic driving technology in the field of artificial intelligence. A specific solution is: acquiring scenario information of a plurality of driving scenarios and driving behavior information of an automatic driving system in each of the driving scenarios, where the driving behavior information includes a decision made by the automatic driving system and an execution result corresponding to the decision; determining an automatic driving feature of the automatic driving system according to the scenario information of the plurality of driving scenarios and respective driving behavior information. The automatic driving feature determined through the above process can represent a characteristic of an automatic driving strategy adopted by the automatic driving system.

A vehicle includes a system and method of modeling and controlling a traction of a wheel of the vehicle. The system includes an observer, a predictive controller and an online solver. The observer receives a dynamic model parameter of the wheel and determines an estimate of a wheel velocity and an uncertainty in the wheel velocity using a non-linear model of the wheel. The predictive controller determines an average gain and differential gain from the estimate of the wheel velocity and the uncertainty in the wheel velocity. The online solver calculates a motor torque and a wheel brake torque for increasing the traction of the wheel with a road based on the average gain and the differential gain. The motor torque and the wheel brake torque are applied at the vehicle.

A method of curve speed adjustment for a road vehicle includes obtaining data on: current ego velocity; distance and curvature of an upcoming road segment, represented by a set of control points to be negotiated; road property of a road comprising the road segment; environmental properties; and driver properties. The obtained data is continuously streamed to a data processing arrangement arranged to perform a translation to target velocities for the respective control points and, for each respective control point, a translation from target velocity for that control point and distance to that control point and obtained current ego velocity, to a target acceleration to reach that control point at its target velocity. The resulting target accelerations are continuously streamed to a control unit of the road vehicle to adjust the road vehicle acceleration to reach each respective control point at its target velocity.

A method and system for target detection of a vehicle is proposed. In the method and system, when the vehicle is turning, a warning signal according to a risk level of collision is generated at the right timing as a risk level of collision between the host vehicle and the other vehicle behind the host vehicle may be accurately identified by correcting a driving path of the host vehicle and position of the other vehicle on the basis of a driving state of the host vehicle.

A travel control system for a vehicle provided with a drive source, a wheel having a wheel body connected to the drive source via a power transmission member and a tire mounted on the wheel body, and a braking device for braking the wheel includes: an estimation unit configured to estimate a tire torsional stiffness and a road surface friction coefficient based on at least the rotation speed of the drive source, the rotation speed of the wheel body, the vehicle body speed, and the torque applied to the wheel body; and a control unit configured to control at least one of the drive source and the braking device such that the tire does not exceed an adhesion limit derived from the tire torsional stiffness and the road surface friction coefficient.

A method using unsupervised velocity prediction and correction for urban driving from sequences of noisy position estimates includes: performing a vehicle velocity prediction for one or more other vehicles in a vicinity of a host automobile vehicle; calculating a first heuristic based on a uniformity test; calculating a second heuristic based on a vehicle speed of the one or more other vehicles; combining the first heuristic and the second heuristic using a weighted sum; determining an uncertainty mask applying the combined first heuristic and the second heuristic and a heuristic threshold; and applying the uncertainty mask to identify a velocity correction for use by the host automobile vehicle.

A method for processing autonomous driving simulation data. The method includes: determining a type of a message transmitted between a simulation system and an auto driving system (ADS); determining a data acquisition mode based on the type of the message; obtaining a data stream transmitted between the simulation system and the ADS based on the data acquisition mode; and determining performance of the ADS based on the data stream.