The present teaching relates to method, system, and medium, for operating a vehicle. The method includes the steps of receiving Real-time data related to the vehicle are received. A current mode of operation of the vehicle is determined. A first risk associated with the current mode of operation of the vehicle is evaluated based on the real-time data in accordance with a risk model. If the first risk satisfies a first criterion, a second risk associated with switching the current mode to a different mode of operation of the vehicle is determined. The vehicle is switched from the current mode to the different mode if the second risk satisfies a second criterion.

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.

Embodiments are directed to a computer-implemented method of operating a driver assistance component (DAC) of a vehicle. The method includes receiving sensed operator state data and sensed vehicle state data that represents a vehicle state of the vehicle. Based at least in part on the sensed operator state data, an operator state model is created, trained, and updated. Based at least in part on the sensed vehicle state data, a vehicle state model is created, trained, and updated. Based at least in part on new sensed operator state data, an operator state model classification output is created. Based at least in part on new sensed vehicle state data, a vehicle state model classification output is created. The operator state model classification output and the vehicle state model classification output are correlated, and operating parameters for the DAC are predicted.

A processor associated with a vehicle receives sensor data from a plurality of sensors in the vehicle. Each sensor is configured to measure a different parameter of a driver of the vehicle. The processor applies a model to the received sensor data, which when applied causes the processor to output a determination, based on the parameters of the driver, of attentiveness of the driver to driving the vehicle. Responsive to the determination indicating the driver is not attentive to driving the vehicle, the processor causes the vehicle to output an alert to the driver or to automatically control a driving function of the vehicle.

The present teaching relates to method, system, and medium, for operating a vehicle. Real-time data related to the vehicle are received. A current mode of operation of the vehicle and a state of the driver present in the vehicle are determined. A first risk associated with the current mode of operation of the vehicle is evaluated based on the real-time data and the state of the driver in accordance with a risk model. In response to the first risk satisfying a first criterion, a second risk associated with switching the current mode to a different mode of operation of the vehicle is determined based on the state of the driver. The vehicle is switched from the current mode to the different mode when the second risk satisfies a second criterion.

The present teaching relates to method, system, and medium, for operating a vehicle. The method includes the steps of receiving Real-time data related to the vehicle are received. A current mode of operation of the vehicle is determined. A first risk associated with the current mode of operation of the vehicle is evaluated based on the real-time data in accordance with a risk model. If the first risk satisfies a first criterion, a second risk associated with switching the current mode to a different mode of operation of the vehicle is determined. The vehicle is switched from the current mode to the different mode if the second risk satisfies a second criterion.

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.

An information processing system receives first travel histories from vehicles that belong to vehicle type A, learns based on the first travel histories to build a first driver model that represents relation between travel situations and behaviors of the vehicles that belong to a first vehicle type, receives second travel histories from vehicles that belong to vehicle type X that is different from vehicle type A, and performs transfer learning in which the second travel histories are used for the first driver model to build a second driver model that represents relation between travel situations and behaviors of the vehicles that belong to vehicle type X.

A control customization system 80 customizes a plant control. A profiler 81 predicts actions of a user depending on situations of the plant or the user. A planner 82 determines an appropriate set of objectives which represent tasks desired by the user, and objective terms representing elements for controlling the plant so as to realize the objectives, and tunes the objective terms based on predictions of the profiler 81.

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.