A method for predicting and reducing kinetosis-induced disturbances of an occupant in driving of a vehicle includes detecting the occupant by a vehicle camera, determining a characteristic figure that indicates a probability of onset of kinetosis-induced disturbances as a function of stimuli acting on the occupant, an individual susceptibility of the occupant with respect to kinetosis, and a type of activity carried on while driving, and as a function of the determined characteristic figure determined, recommending at least one individual measure of a package of measures for preventing kinetosis-induced disturbances to the occupant or initiating automatically the at least one individual measure.

The present teaching relates to method, system, medium, and implementation of lane planning in an autonomous vehicle. Sensor data are received that capture ground images of a road the autonomous vehicle is on. Based on the sensor data, a current lane of the road that autonomous vehicle is currently occupying is detected. Lane control for the autonomous vehicle is planned based on the detected current lane and self-aware capability parameters in accordance with a driving lane control model. The self-aware capability parameters are used to predict operational capability of the autonomous vehicle with respect to a current location of the autonomous vehicle. The driving lane control model is generated based on recorded human driving data to achieve human-like lane control behavior in different scenarios.

The present teaching relates to method, system, medium, and implementation of lane planning in an autonomous vehicle. Sensor data are received that capture ground images of a road the autonomous vehicle is on. Based on the sensor data, a current lane of the road that autonomous vehicle is currently occupying is detected. Information indicating presence of a passenger in the vehicle is obtained and used to retrieve a personalized lane control model related to the passenger. Lane control for the autonomous vehicle is planned based on the detected current lane and self-aware capability parameters in accordance with the personalized lane control model. The self-aware capability parameters are used to predict operational capability of the autonomous vehicle with respect to a current location of the autonomous vehicle. The personalized lane control model is generated based on recorded human driving data.

The present teaching relates to method, system, medium, and implementation of lane planning in an autonomous vehicle. Sensor data are received that capture ground images of a road the autonomous vehicle is on. Based on the sensor data, a current lane of the road that autonomous vehicle is currently occupying is detected. Lane control for the autonomous vehicle is planned based on the detected current lane and self-aware capability parameters in accordance with a driving lane control model. The self-aware capability parameters are used to predict operational capability of the autonomous vehicle with respect to a current location of the autonomous vehicle. The driving lane control model is generated based on recorded human driving data to achieve human-like lane control behavior in different scenarios.

The present teaching relates to method, system, medium, and implementation of lane planning in an autonomous vehicle. Sensor data are received that capture ground images of a road the autonomous vehicle is on. Based on the sensor data, a current lane of the road that autonomous vehicle is currently occupying is detected. Information indicating presence of a passenger in the vehicle is obtained and used to retrieve a personalized lane control model related to the passenger. Lane control for the autonomous vehicle is planned based on the detected current lane and self-aware capability parameters in accordance with the personalized lane control model. The self-aware capability parameters are used to predict operational capability of the autonomous vehicle with respect to a current location of the autonomous vehicle. The personalized lane control model is generated based on recorded human driving data.

In an approach to predicting physiological and behavioral states utilizing models representing relationships between driver health states and vehicle dynamics data, one or more computer processors capture one or more vehicle motion parameters. The one or more computer processors to capture one or more physiological parameters; identify contextual data associated with the one or more captured vehicle motion parameters and the one or more captured physiological parameters; predict one or more driving behavior parameters by utilizing one or more physical models fed with the one or more vehicle motion parameters and the identified contextual data; predict one or more driver health parameters by utilizing a model trained with the one or more captured physiological parameters and the identified contextual data; generate a risk assessment based on the one or more predicted driving behavior parameters and the one or more predicted driver health parameters.

By way of example, the technology disclosed by this document may be implemented in a method that includes receiving stored sensor data describing characteristics of a vehicle in motion at a past time and extracting features for prediction and features for recognition from the stored sensor data. The features for prediction may be input into a prediction network, which may generate a predicted label for a past driver action based on the features for prediction. The features for recognition may be input into a recognition network, which may generate a recognized label for the past driver action based on the features for recognition. In some instances, the method may include training prediction network weights of the prediction network using the recognized label and the predicted label.

The present teaching relates to method, system, medium, and implementation of human-like vehicle control for an autonomous vehicle. Recorded human driving data are first received, which include vehicle state data, vehicle control data, and environment data. For each piece of recorded human driving data, a vehicle kinematic model based vehicle control signal is generated in accordance with a vehicle kinematic model based on a corresponding vehicle state and vehicle control data of the piece of recorded human driving data. A human-like vehicle control model is obtained, via machine learning, based on the recorded human driving data as well as the vehicle kinematic model based vehicle control signal generated based on vehicle kinematic model. Such derived human-like vehicle control model is to be used to generate a human-like vehicle control signal with respect to a target motion of an autonomous vehicle to achieve human-like vehicle control behavior.

The present teaching relates to method, system, medium, and implementation of human-like vehicle control for an autonomous vehicle. Information related to a target motion to be achieved by the autonomous vehicle is received, wherein the information includes a current vehicle state of the autonomous vehicle. A first vehicle control signal is generated with respect to the target motion and the given vehicle state in accordance with a vehicle kinematic model. A second vehicle control signal is generated in accordance with a human-like vehicle control model, with respect to the target motion, the given vehicle state, and the first vehicle control signal, wherein the second vehicle control signal modifies the first vehicle control signal to achieve human-like vehicle control behavior.

An information processing system includes a processor, and the processor acquires a vehicle driving environment composed of N-dimensional parameters, refers to an ideal driving driver model indicating an ideal driving environment region in an N-dimensional coordinate system, and selects one ideal driving environment included in the ideal driving environment region based on a distance between the ideal driving environment region and the acquired vehicle driving environment in the N-dimensional coordinate system.