A method for evaluating a driver in a vehicle having an advanced driver assistance system according to an embodiment includes collecting vehicle traveling information including the movement distance and movement time of a vehicle, extracting a physical property value on the basis of the vehicle traveling information, receiving a notification from the advanced driver assistance system, calculating a personal characteristic notification index value by non-dimensionalizing the notification according to the physical property value, and calculating a reckless driving index value on the basis of the personal characteristic notification index value, the weight for each notification, and a property correction coefficient.

An automated driving system (ADS) of an autonomous vehicle includes a communication module, a misbehavior detection module, and a processor. The communication module is configured to receive a vehicle-to-vehicle (V2V) message including source vehicle data and receive a fusion data message including fusion data from a mobile edge computing (MEC) system including a roadside unit (RSU). The source vehicle data includes a source vehicle location. The fusion data is based on RSU sensed data and on vehicle sensed data received at the RSU from at least one vehicle. The misbehavior detection module is configured to determine whether a source vehicle is disposed at the source vehicle location based on the fusion data. The processor is configured to manage performance of the autonomous vehicle in accordance with the source vehicle data based at least in part on the determination. Other embodiments are described and claimed.

The present technology is directed to dynamically adjusting an autonomous vehicle (AV) system based on deep learning optimizations. An AV management system can generate a downscaling signal based on a result of comparing a complexity of an environment for an AV to navigate with a predetermined complexity threshold. Further, the AV management system can perform a downscaling of a neural network associated with an AV system based on the downscaling signal and determine a scenario to test the downscaled neural network in a simulation. The AV management system can adjust one or more parameters of the AV system based on simulated outputs and perform the simulation of the AV based on the adjusted one or more parameters of the AV system and the downscaled neural network to generate simulated performance data. Furthermore, the AV management system can compare the simulated performance data with a predetermined performance threshold.

Provided are a method, apparatus, and computer program for modeling a driving route for automatic driving of a vehicle. According to various embodiments, a method of modeling a driving route for automatic driving of a vehicle, which is executed by a computing device, includes setting a plurality of reference points; and generating a driving route for autonomous driving control of the vehicle based on positions of the plurality of set reference points, in which the generated driving route is a set of curves each corresponding to one of a straight section, a curved section and a clothoid section connecting the straight section and the curved section and is expressed as a curvature function according to displacement.

A driving mode control method, apparatus, device, program and storage medium, which relates to the technical field of electronic control, and is for a control platform, the method includes: acquiring a location information sent by a target vehicle, wherein the location information comprises a target location; determining a status data of at least one sample vehicle pre-stored by the control platform based on the target location, wherein the sample vehicle is a vehicle which has travelled through the target location; determining a target driving mode based on the status data of the sample vehicle, and sending the target driving mode to the target vehicle, such that the target vehicle travels in the target driving mode. This disclosure enables the target vehicle to travel in the target driving mode. The driver needs not to manually switch the driving mode, and the accuracy and convenience of controlling the drive mode are improved.

A vehicle for predicting a risk of collision includes a controller configured to: calculate distances between the vehicle and left and right lines of a first lane, respectively, using a position of the vehicle and first lane width information of the first lane, calculate distances between the surrounding vehicle and left and right lines of a second lane, respectively, using a position of the surrounding vehicle and second lane width information of the second lane, calculate a second distance between the vehicle and the surrounding vehicle by reflecting the calculated distances between the vehicle and the left and right lines of the first lane or the calculated distances between the surrounding vehicle and the left and right lines of the second lane to a first distance, and predict a risk of collision between the vehicle and the surrounding vehicle based on the second distance.

A parallel computing method for man-machine coordinated steering control of a smart vehicle based on risk assessment is provided, comprising the following steps: building a lateral kinetic equation model of a vehicle; building a target function by targeting at minimizing an offset distance of a vehicle driving track from a lane center line and making a change in a front wheel steering angle and a longitudinal acceleration as small as possible in a driving process; building a parallel computing architecture of a prediction model and the target function, and employing a triggering parallel computing method; solving and computing a gradient with a manner of back propagation and using a gradient descent method to obtain an optimal control amount of the front wheel steering angle and an optimal control amount of the longitudinal acceleration; and computing a driving weight, obtaining a desired front wheel steering angle and completing real time control.

Provided are a driving force control method and device for a hybrid vehicle, each capable of effectively absorbing torque fluctuation of an engine while suppressing deterioration in energy efficiency. The driving force control device for a hybrid vehicle comprises a PCM configured to: estimate an average torque output by an engine; estimate a torque fluctuation component of the torque output by the engine; set a countertorque for suppressing the estimated torque fluctuation component; and control an electric motor to output the set countertorque, wherein the PCM is operable, under the condition that the average torque output by the internal combustion engine is constant, to set a negative control gain such that, as an engine speed becomes higher, the absolute value of the control gain becomes smaller, and then to set the countertorque based on a product of the estimated torque fluctuation component and the control gain.

A method for controlling a longitudinal position of a vehicle involves a longitudinal positioning control system generating a longitudinal acceleration control signal from a longitudinal dynamic feedforward set point and from longitudinal dynamic control error quantities for a subordinate acceleration control unit acting on a drive device and braking device of the vehicle. A current control reference point corresponding to a current time point and at least one forward control reference point corresponding to a presettable look-ahead time point are determined as control-relevant time points, current or predicted actual/required deviations of a longitudinal position, of a driving speed and of acceleration are determined for each of the control reference points and provide the basis for forming the longitudinal dynamic control error quantities, and required values of an acceleration are determined for each of the control reference points and provide the basis for forming the longitudinal dynamic feedforward set point.

A scene simulation system can use scene data of a scene of a vehicle to generate one or more simulated states and one or more simulated trajectories associated with the one or more simulated states. The system can evaluate the simulated trajectories and select an action for the vehicle based on the evaluation of the simulated trajectories.