This disclosure relates to a system and method for transitioning vehicle control between autonomous operation and manual operation. The system includes sensors configured to generate output signals conveying information related to the vehicle and its operation. During autonomous vehicle operation, the system gauges the level of responsiveness of a vehicle operator through challenges and corresponding responses. The system determines when to present a challenge to the vehicle operator based on internal and external factors. If necessary, the system will transition from an autonomous operation mode to a manual operation mode.

A method and apparatus for providing a human-machine interface (HMI) mode of a vehicle are provided. The method, performed by the device of the vehicle, for providing a human-machine interface (HMI) mode includes, analyzing a state of an occupant, calculating a confidence score for the vehicle based on the state of the occupant, determining an HMI mode corresponding to the confidence score among a plurality of predefined HMI modes; and providing first guidance information to the occupant based on the determined HMI mode.

A state of a driver operating a vehicle based at least on a rest pattern of the driver and a driver safety score and vehicle characteristic associated with the vehicle are determined. A risk of a planned route is received based on environment information. A safety risk for at least one location along the planned route is determined based on the received risk. Based on the safety risk, a use of available driver attention determined based on the state of the driver, possible modification of the planned route and vehicle state may be planned that minimize the safety risk. It may be determined that the driver is approaching a location along the planned route having a safety risk. Responsive to determining that the driver is approaching a location along the planned route having a safety risk, alert may be caused to be sent to the driver.

In various embodiments, a driver sensing subsystem computes a characterization of a driver based on physiological attribute(s) of the driver that are measured as the driver operates a vehicle. Subsequently, a driver assessment application uses a confidence level model to estimate a confidence level of the driver based on the characterization of the driver. The driver assessment application then causes driver assistance application(s) to modify at least one functionality of the vehicle based on the confidence level. Advantageously, by enabling the driver assistance application(s) to take into account the confidence level of the driver, the driver assessment application can improve driving safety relative to conventional techniques for implementing driver assistance applications that disregard the confidence levels of drivers.

Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having one or more autonomous or semi-autonomous operation features are provided. According to certain aspects, the operating status of the features, the identity of a vehicle operator, risk levels for operation of the vehicle by the vehicle operator, or damage to the vehicle may be determined based upon sensor or other data. According to further aspects, decisions regarding transferring control between the features and the vehicle operator may be made based upon sensor data and information regarding the vehicle operator. Additional aspects may recommend or install updates to the autonomous operation features based upon determined risk levels. Some aspects may include monitoring transportation infrastructure and communicating information about the infrastructure to vehicles.

Among other things, we describe techniques for operation of a vehicle based on measured load characteristics and/or passenger comfort. One or more sensors of the vehicle can measure passenger data and/or load data of the vehicle. The passenger data and/or load data of the vehicle can be used by the vehicle to determine how to navigate within the surrounding environment.

Techniques to detect a non-alert state of a user may include receiving sensor data from an ear-worn electronic device worn by a user, automatically determining whether to initiate an alertness mode of the ear-worn electronic device based on the sensor data, and determining an alertness state of the user in response to determining to initiate the alertness mode.

A drive assist apparatus includes a processor and a storage. The processor includes a line-of-sight detector, an emotion estimator, and a notification processing unit. The line-of-sight detector detects information on a line of sight of one or both of a vehicle occupant present in a vehicle and a traffic participant present around the vehicle. The emotion estimator estimates an emotion of the one or both of the vehicle occupant and the traffic participant. The storage stores, when the estimated emotion is detected as a positive emotion, data on a viewpoint including information on a position of the vehicle on map data and information on the line of sight. The notification processing unit makes, on the basis of the data on the viewpoint stored in the storage, a notification of information on a direction to notice to the vehicle occupant present in the vehicle traveling through a location corresponding to the viewpoint.

A control apparatus includes a controller configured to, upon determining, based on behavior classification, that a behavior of a passenger detected from a captured image or a sound of inside of a vehicle is threatening, transmit a first instruction to increase tension of a seat belt worn by the passenger.

A vehicular cabin monitoring system includes an interior-viewing camera disposed at a vehicle and viewing a driver sitting at a driver seat of the vehicle and viewing at least one other seat of the vehicle. The interior-viewing camera is operable to capture image data. The vehicular cabin monitoring system, via processing of image data captured by the interior-viewing camera, detects an occupant present in the vehicle. The vehicular cabin monitoring system, via processing of image data captured by the interior-viewing camera, monitors the driver sitting at the driver seat of the vehicle. Control of the vehicle is adjusted based at least in part on the monitoring of the driver and the detected occupant.