An information processing system capable of estimating a driving conduct suited to a driver includes: a history obtainer that obtains a personal driving environment history of each of a plurality of drivers, each of the personal driving environment histories indicating one or more vehicle behaviors selected by the driver, and a driving environment associated with each of the one or more behaviors, the driving environment being a driving environment of the vehicle at a point in time of selection of the behavior it is associated with; and a modeler that models, from a driving environment history including the personal driving environment histories of the plurality of drivers, the personal driving environment history of at least one of the plurality of drivers, to build a driver model indicating a relationship between a behavior and a driving environment for the vehicle of the at least one of the plurality of drivers.

An information processing system is provided with a modeling unit. The modeling unit models, based on a degree of similarity among individual travel histories of a plurality of drivers, individual travel histories of at least one or more drivers from among the individual travel histories of the plurality of drivers to construct a driver model indicating a relationship between conditions of the at least one or more drivers and travel environments of at least one or more vehicles.

Exhaustive driving analytical methods, systems, are apparatuses are described. The methods, systems, are apparatuses relate to monitoring driver and/or driving behaviors in view of an exhaustive list of variables to determine safety factors, identify times to react to events, and contextual information regarding the events. The methods, systems, and apparatuses described herein may determine, based on a systematic model, reactions and reaction times, compare the vehicle behavior (or lack thereof) to the modeled reactions and reaction times, and determine safety factors and instructions based on the comparison.

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.

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 notification control system (NCS) is configured to coordinate the delivery of notifications issued from a set of devices to the driver of a vehicle. The NCS registers all interactive devices residing in a vehicle and causes those devices to conform to specific directives generated by the NCS. The NCS analyzes and predicts the cognitive and emotional load on the driver and also estimates the cognitive and emotional impact on the driver potentially caused by any incoming notifications. The NCS then coordinates the delivery of notifications to the driver in a manner that avoids overwhelming the cognitive and/or emotional capacity of the driver. Accordingly, the NCS may avoid situations where device notifications cause the driver to unsafely divert attention away from driving.

The invention relates to a method the assessing the controllability of a vehicle by a driver in a risky or problematic situation. In order to carry out assessment of the controllability of a vehicle early, the following steps are provided: a. modelling the drive train and the movement dynamics of the vehicle, b. modelling situation conditions and environmental conditions, c. selecting a risky or problematic situation, d. selecting a driver capability type, e. modelling the driver's reaction as a function of the selected driver capability type. f. simulating the dynamic vehicle behaviour in the longitudinal and transverse directions of a planned trajectory on the basis of the drive train model and movement dynamics model for the predefined situation and environmental conditions when the selected risky or problematic situation occurs, g. calculating the maximum lateral and longitudinal deviation from the planned trajectory between the occurrence of the risky or problematic situation and the regaining of complete control by the driver, h. evaluating the controllability of the vehicle by the driver in the risky or problematic situation on the basis of the maximum lateral and/or longitudinal deviation.

Provided herein are systems and methods of transferring controls in vehicular settings. A vehicle control unit can have a manual mode and an autonomous mode. An environment sensing module can identify a condition to change an operational mode of the vehicle control unit from the autonomous mode to the manual mode. A behavior classification module can determine an activity type of an occupant based on data from a sensor. A reaction prediction can use a behavior model to determine, based on the activity type, an estimated reaction time between a presentation of an indication to the occupant to assume manual control of vehicular function and a state change of the operational mode from the autonomous mode to the manual mode. A policy enforcement module can apply the action based on the estimated reaction time in advance of the condition to indicate to the occupant to assume manual control.

Embodiments provide a method and system for monitoring user activity in a vehicle. A first sensor in the vehicle acquiring current activity data regarding the user. A second sensor in the vehicle can acquire environment data regarding an environment surrounding the user. A first set of classifiers can generate key point data regarding the user, which indicates body hand/eye movement points of the user. A second set of classifiers can assign the current activity to the activity group. A storage can store response information regarding a user, which includes mappings between activity groups and corresponding response time duration for the user to regain control of the vehicle. The system can generate a take-over request for the user to regain control of the vehicle based on the mapping and current activity.

A system may include a plurality of vehicle sensor and a computer comprising a processor and memory storing instructions executable by the processor. One of the instruction may comprise to determine a driving responsiveness (DR) value using a weighted sum comprising indices of a transition probability matrix (Q), Q being derived from likelihood of transition data () between a plurality of driving modes from a set of interacting multiple model (IMM) instruction.