Sentiment-based navigation is provided herein. A method can include extracting features of sensor data captured by a sensor associated with a vehicle, wherein the sensor data is representative of a subject selected from a group of subjects comprising an occupant of the vehicle and an environment in which the vehicle is located, resulting in extracted features. The method can further include determining sentiment data representative of an emotional condition of the occupant of the vehicle based on an analysis of the extracted features, and generating a navigation route for the vehicle from an origin point to a destination point based on the sentiment data.

A driving assist system for a vehicle includes a driver monitoring system that includes a plurality of sensors disposed in a vehicle and sensing driver hand positions of a driver driving the vehicle. A control includes a processor operable to process data sensed by the sensors to determine the driver hand positions of the driver driving the vehicle. The control, responsive to processing of data sensed by the sensors and at least in part responsive to the determined sensed driver hand positions, is operable to determine a level of attentiveness of the driver. The driving assistance system of the vehicle operates to provide driving assistance of the vehicle responsive at least in part to the determined level of attentiveness of the driver.

This technology relates to dynamically detecting, managing and mitigating driver fatigue in autonomous systems. For instance, interactions of a driver in a vehicle may be monitored to determine a distance or time when primary tasks associated with operation of the vehicle or secondary tasks issued by the vehicle computing were last performed. If primary tasks or secondary tasks are not performed within given distance thresholds or time limits, then one or more secondary tasks are initiated by the computing device of the vehicle. In another instance, potential driver fatigue, driver distraction or overreliance on an automated driving system is detected based on gaze direction or pattern of a driver. For example, a detected gaze direction or pattern may be compared to an expected gaze direction or pattern given the surrounding environment in a vicinity of the vehicle.

Systems including one or more sensors, coupled to a vehicle, may detect sensor information and provide the sensor information to another computing device for processing. A system includes one or more sensors, coupled to a vehicle and configured to detect sensor information, and a computing device configured to communicate with one or more mobile sensors to receive the mobile sensor information, communicate with the one or more sensors to receive the sensor information, and analyze the sensor information and the mobile sensor information to identify one or more risk factors.

A driver state guide device includes: a state identification unit identifying a present state of a driver of an own vehicle; a target estimation unit estimating a target state to which the state of the driver is guided; a route prediction unit providing a predicted route of the own vehicle; a situation prediction unit predicting a situation in the predicted route predicted by the route prediction unit; a planning unit planning, as a stimulus plan, a stimulus to be used in the predicted route to guide the state of the driver to the target state; and a stimulus control unit providing the stimulus for the driver according to the stimulus plan determined by the planning unit in the predicted route.

A computer-implemented method for determining awareness data includes determining occlusion information related to a surrounding of a vehicle, determining a viewing direction of an occupant of the vehicle, and determining awareness data representing the occupant's awareness of the surrounding based on the occlusion information and the viewing direction.

To implement a configuration to calculate a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a requested recovery ratio (RRR) for each road section, and issue a manual driving recovery request notification on the basis of the calculated time. A data processing unit is included, which calculates a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a predefined requested recovery ratio (RRR) from automatic driving to manual driving and determines notification timing of a manual driving recovery request notification on the basis of the calculated time. The data processing unit acquires the requested recovery ratio (RRR) for each road section set as ancillary information of a local dynamic map (LDM), and calculates the manual driving recoverable time for each road section scheduled to travel, using learning data for each driver.

A safe driving determination apparatus includes an angle value calculation part that calculates an angle value indicating a face direction angle of a driver with respect to the traveling direction, and a determination part that determines whether or not the driver is in a state of being inattentive to the road ahead on the basis of whether or not an integrated value of angle values during a past predetermined first determination period is equal to or greater than a threshold value, wherein the determination part does not determine that the driver is in a state of being inattentive to the road ahead if the angle value of the driver is less than a second threshold value at the present moment, even if an integrated value of angle values during a past predetermined first determination period is equal to or greater than a first threshold value.

An autonomous vehicle uses machine learning based models such as neural networks to predict hidden context attributes associated with traffic entities. The hidden context represents behavior of the traffic entities in the traffic. The machine learning based model is configured to receive a video frame as input and output likelihoods of receiving user responses having particular ordinal values. The system uses a loss function based on cumulative histogram of user responses corresponding to various ordinal values. The system identifies user responses that are unlikely to be valid user responses to generate training data for training the machine learning mode. The system identifies invalid user responses based on response time of the user responses.

A control device is used in a subject vehicle capable of performing autonomous driving with no obligation for a driver to monitor periphery. The control device determines whether a permission state, in which a specific act other than driving is permitted to the driver, is continued or not when approach of an emergency vehicle to the subject vehicle is detected during an autonomous cruising period in which the autonomous driving is being performed. The control device restricts display of a content provided to the driver when the permission state of the specific act is determined to be not continued.