A method for recognizing the driving style of a driver of a land vehicle, of the type that envisages acquiring information on the dynamics of the vehicle from sensors and calculating, as a function of said information on the dynamics of the vehicle, a class of membership of the driving style of the driver. The method comprises the steps of analysing information on the dynamics of the vehicle to start a procedure of recognition of the event that comprises: reconstructing a manoeuvre performed by the driver; identifying the manoeuvre performed, by comparing said displacement time series with models of time series corresponding to pre-determined manoeuvres stored in a database; defining regions in a cartesian plane having as axes a lateral acceleration and a longitudinal acceleration, in particular manifolds; computing cost functionals for the three driving styles; and recognising the driving style, on the basis of said cost functionals.

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.

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.

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.

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.

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 technology relates to identifying and addressing aberrant driver behavior. Various driving operations may be evaluated over different time scales and driving distances. The system can detect driving errors and suboptimal maneuvering, which are evaluated by an onboard driver assistance system and compared against a model of expected driver behavior. The result of this comparison can be used to alert the driver or take immediate corrective driving action. It may also be used for real-time or offline training or sensor calibration purposes. The behavior model may be driver-specific, or may be a nominal driver model based on aggregated information from many drivers. These approaches can be employed with drivers of passenger vehicles, busses, cargo trucks and other vehicles.

A method for determining an occupant class for a vehicle seat includes obtaining, via first and second seat weight sensors, first and second seat weight indications for the vehicle seat. The first seat weight sensor is located on the lateral side of the vehicle seat at a front location on the vehicle seat. The second seat weight sensor is located on the lateral side of the vehicle seat at a rear location on the vehicle seat. The method also includes obtaining a vehicle acceleration value from a vehicle acceleration sensor. The method also includes determining a raw weight on the vehicle seat as twice the sum of the first and second seat weight indications, and determining a filtered weight based on the raw weight. The method further includes determining the occupant class based on the filtered weight in response to the vehicle acceleration value being less than a predetermined value.