An active vibration reduction control apparatus for a hybrid electric vehicle includes: a reference signal generator generating a reference signal and a first phase based on a first rotational angle of a first motor; a vibration extractor extracting a vibration signal from a second motor; a coefficient determiner determining a filter coefficient which minimizes a phase difference between the reference signal and the vibration signal; a phase determiner detecting a second phase which corresponds to the phase difference using a first speed signal of the first motor and the filter coefficient; a phase deviation amount detector detecting a third phase for compensating for a phase delay; and a synchronization signal generator generating an antiphase signal of a shape of an actual vibration in order to determine a compensating force of the first motor.

According to an embodiment, provided are a method and an apparatus for monitoring an unmanned ground vehicle (UGV), for detecting a failure of a UGV actuator in consideration of terrain information. Accordingly, the accuracy of detecting a failure of the UGV is improved.

An embodiment driving assistance system for a vehicle includes a driving information provision unit configured to acquire and provide driving information of a traveling vehicle, a control unit configured to generate and output a control signal for driving assistance when it is determined the vehicle travels on a rough road based on the driving information of the vehicle provided by the driving information provision unit and it is determined that the vehicle is currently in a rough road traveling state, and a steering actuator configured to generate and apply a steering assistance force according to a control value of the control signal for the driving assistance output by the control unit to a steering wheel.

An apparatus and a method for controlling an autonomous vehicle depending on weather are provided. The apparatus obtains information including at least one of an image around the autonomous vehicle, sensing information of a Light Detection and Ranging (LiDAR) of the autonomous vehicle, sensing information of a rain sensor of the autonomous vehicle, an operation state of a windshield wiper of the autonomous vehicle, climate information through vehicle to everything (V2X) communication, an acceleration of the autonomous vehicle, or wheel sensor information of the autonomous vehicle and determines whether the climate state is an inclement weather state, based on the information including the at least one of the image around the autonomous vehicle, the sensing information of the LIDAR, the sensing information of the rain sensor, the operation state of the windshield wiper, the climate information through the V2X communication, the acceleration, or the wheel sensor information.

Systems and methods cause a component of a vehicle to activate. The systems and methods receive audio data generated by a microphone of the vehicle, where the audio data represents sound of the component. Based on the audio data, a condition of the vehicle or the component may be determined, and based on the condition, the vehicle may be commissioned for use or decommissioned.

It is advantageous for a vehicle to detect road wetness or related environmental conditions. This is particularly true for self-driving vehicles, which can then adjust the manner of automated operation of the vehicle to increase safety by reducing speed, braking earlier, adjusting internal estimates of road traction parameters, or adjusting autonomous operation in some other manner. It is difficult to directly measure road wetness (e.g., using spectroscopy or other methods directed at the road surface), however, it is possible to indirectly estimate road wetness based on road noise audio signals detected via one or more microphones disposed on the vehicle. The location of the microphones, the type of post-processing applied to the audio signals, or other factors can be adapted to increase the useful road wetness-related content of such audio signals while reducing the presence of engine noise, road noise, or other confounding signals.

A method for monitoring uncertainty for human-like behavioral modulation of trajectory planning includes: retrieving map and agent information of a current driving state of an autonomously operated host automobile vehicle; dividing uncertainty conditions affecting a trajectory of the host automobile vehicle into an expected uncertainty and an unexpected uncertainty; calculating the expected uncertainty in a first operation branch by forming attention zones according to identified portions of lanes which may potentially collide with a planned route of the host automobile vehicle; determining the unexpected uncertainty in a second operation branch by calculating an anomaly score for any other vehicles in a surrounding area of the host automobile vehicle positioned in the lanes which may potentially collide with the planned route of the host automobile vehicle; and modulating trajectory operation signals determined for the expected uncertainty if the unexpected uncertainty meets or exceeds a predetermined threshold.

A vehicle control apparatus comprises: a path deviation determination unit configured to output a request signal for requesting driving takeover if a deviation amount of information representing a traveling state with respect to a target track is not less than a first threshold; and an operation monitoring unit configured to monitor processing of the path deviation determination unit. The operation monitoring unit determines that an abnormality has occurred in the path deviation determination unit if the deviation amount of the information representing the traveling state with respect to the target track is not less than a second threshold larger than the first threshold, and a state in which the request signal is not output continues for a predetermined period.

Provided are methods for autonomous driving mode engagement. Some systems described receive at least one autonomous vehicle operation task output associated with driving a vehicle in autonomous driving mode in an environment while the vehicle is operated in manual driving mode in the environment. The at least one autonomous vehicle operation task output is compared to a respective threshold, wherein the respective threshold is adaptive based on a current operational state of the vehicle in the manual driving mode. A transition indicator is set based on the comparison, and a transition from the manual driving mode to the autonomous driving mode is rejected in response to the transition indicator indicating a smooth transition from the manual driving mode to the autonomous driving mode is unavailable. Methods and computer program products are also provided.

A computer includes a processor and a memory storing instructions executable by the processor to identify a first scenario in which a vehicle is operating from a plurality of scenarios, prompt an operator to activate an adaptive cruise control of the vehicle in response to a preference score for the first scenario being above a threshold, refrain from prompting the operator to activate the adaptive cruise control in response to the preference score for the first scenario being below the threshold, and activate the adaptive cruise control in response to receiving an input to activate the adaptive cruise control from the operator. The scenarios indicate at least one characteristic of a road on which the vehicle is traveling. The preference score indicates a preference of the operator for activating the adaptive cruise control in the first scenario.