A method for increasing the share of automated driving in an at least partially automated vehicle involves monitoring the conditions for automated driving. The number of manual takeovers by the driver is reduced in order to utilize the advantages of automated driving optimally designed for traffic flow, traffic safety and energy balance, when conditions for automated driving are met, it is predicted by a model that automated driving will be ended by manual vehicle control. In the event that a takeover by the driver is predicted, information is output to the driver which informs the driver that automated driving is reliably in control of the driving situation.

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.

In one embodiment, an apparatus for adaptively interacting with a driver via voice interaction is provided. The apparatus includes a computational models block and an adaptive interactive voice system. The computational models block is configured to receive driver related parameters, vehicle related parameters, and vehicle environment parameters from a plurality of sensors. The computational models block is further configured to generate a driver state model based on the driver related parameters and to generate a vehicle state model based on the vehicle related parameter. The computational models block is further configured to generate a vehicle environment state model based on the vehicle environment parameters. The adaptive interactive voice system is configured to generate a voice output based on a driver's situation and context as indicated on information included within at least one of the driver state model, the vehicle state model, and the vehicle environment state model.

A system and method for path planning of autonomous vehicles based on gradient are disclosed. A particular embodiment includes: generating and scoring a first suggested trajectory for an autonomous vehicle; generating a trajectory gradient based on the first suggested trajectory; generating and scoring a second suggested trajectory for the autonomous vehicle, the second suggested trajectory being based on the first suggested trajectory and a human driving model; and outputting the second suggested trajectory if the score corresponding to the second suggested trajectory is within a score differential threshold relative to the score corresponding to the first suggested trajectory.

A vehicle drive assistance system is provided, which includes a general driver model learning engine configured to build a general driver model to be applied for a plurality of vehicle drivers based on driving data of the plurality of drivers, and an individual driver model learning engine configured to build an individual driver model particular to a specific vehicle driver based on the driving data of the specific driver received from a specific vehicle of the specific driver. The individual driver model learning engine includes a first synchronization engine configured to provide, to the general driver model learning engine, the driving data obtained by executing a first data conversion processing on the driving data of the specific driver.

A vehicle drive assistance system is provided, which includes one or more processors configured to execute a general driver model learning engine configured to build a general driver model to be applied to a plurality of drivers based on driving data of the drivers, an individual driver model learning engine configured to build an individual driver model unique to a specific driver based on driving data of the driver, and an on-board controller provided in a vehicle operated by the driver. The individual driver model learning engine includes a vehicle control updating program configured to cause the on-board controller to update vehicle control processing based on the general and individual driver models. The vehicle control updating program acquires the driver models and, according to a given condition, determines a driver model based on which the vehicle control processing is updated, between the general and individual driver models.

A vehicle drive assistance system is provided, which includes one or more processors configured to execute a general driver model learning engine configured to build a general driver model to be applied to a plurality of vehicle drivers based on driving data of the plurality of vehicle drivers, an individual driver model learning engine configured to build an individual driver model unique to a specific driver based on driving data of the specific driver and the general driver model, and a vehicle control changing program configured to change vehicle control processing executed by a specific vehicle operated by the specific driver based at least on the individual driver model.

A vehicle drive assistance system is provided, which includes a processor configured to execute an individual driver model learning engine configured to build an individual driver model unique to a vehicle driver based on driving data of the driver, and an on-board controller provided in a vehicle of the driver and configured to perform particular vehicle control processing. The individual driver model learning engine analyzes a current state of the driver based on the driving data, determines recommending processing that corresponds to the analyzed state based on the individual driver model, and instructs the on-board controller to perform the recommending processing.

A driving support device as an example of a data processor executes processing for estimating a driving behavior of a vehicle by using a driving behavior model trained based on detection results by a sensor. A detected-information input unit acquires detected information including the detection results. From the detection results included in the detected information input to the detected-information input unit, a selection unit selects a detection result that falls within predetermined selection range narrower than a range detectable by the sensor. A processing unit executes the processing, based on the detection result selected by the selection unit.

In a method for detecting driver fatigue in a vehicle having a memory device in which a fatigue model is stored, a sensor which detects at least one activity of the driver, and a sensor which detects the ambient brightness, the activity information is analyzed in the fatigue model and the brightness information is used for weighting the model.