An onboard device estimates a traveling state of a vehicle that may be influenced by the psychological state of a driver, based on an utterance of the driver without the use of various sensors, and includes: a voice collection unit for collecting a driver's voice; a traveling state collection unit for collecting traveling state information representing a traveling state of a vehicle; a database generation unit for generating a database by associating voice information corresponding to the collected voice with the collected traveling state information; a learning unit for learning an estimation model, with pairs including the voice information and the traveling state information recorded in the generated database being used as learning data; and an estimation unit for estimating the traveling state of the vehicle that may be influenced by a psychological state of the driver by using the estimation model, based on an utterance of the driver.

A system includes a processor configured to receive a user profile responsive to an efficiency determination request for a vehicle model. The processor is also configured to obtain efficiency-affecting data from the user profile. The processor is further configured to compare the efficiency-affecting data to data gathered from drivers of the vehicle model, to determine a correlation between the user profile and similar drivers of the vehicle model. Also, the processor is configured to predict fuel efficiency for the new vehicle model based on efficiency achieved by the similar drivers.

Exhaustive driving analytical methods, systems, are apparatuses are described. The methods, systems, are apparatuses relate to utilizing partially available data associated with driver and/or driving behaviors 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 method of controlling autonomous driving is provided. The method includes collecting driving information of a driver and curvature information of a road and generating a driving pattern of the driver, defined by associating behaviors in longitudinal and lateral directions of the vehicle based on the driving information. The driving pattern is then set to a constraint condition for driving torque and brake pressure and the vehicle is operated based thereon.

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.

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 system for a vehicle includes a memory configured to store modeling parameters, generated using an algorithm that assigns relevance scores to features, that specify information indicative of how configuration data including information indicative of use of features of the vehicle classifies which user is currently using the vehicle. The system also includes a processor programmed to monitor the vehicle for the configuration data, identify a most likely user profile using the modeling parameters according to the configuration data, and apply settings of the user profile to the vehicle.

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 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.

A driving assistance control apparatus of a vehicle includes a blind area detector, a driving operation detector, and an electronic control unit. The blind area detector is configured to detect the presence or absence of a blind area as seen from the vehicle in a traveling direction of the vehicle. The driving operation detector is configured to detect driving operation of the driver. The electronic control unit is configured to perform automatic deceleration control of the vehicle based on detection of the presence of the blind area by the blind area detector. The electronic control unit is configured to start the automatic deceleration control, by referring to the driving operation of the driver after detection of the presence of the blind area by the blind area detector.