A system for the integrated/functional detection of different measurements is described. The system comprises a part wearable by a driver of a vehicle and plurality of sensors integrated in the wearable part and configured to supply the following driver parameters: breathing parameters, heart parameters, perspiration parameters and of the pressure exerted on the vehicle driving wheel. The system also includes a processor for processing the driver parameters and correlating them to a driver psychophysical condition.

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, a computer-implemented method for real-time determination of the status of autonomous operation features of an autonomous or semi-autonomous vehicle may be provided. With the customer's permission, the operation of the autonomous or semi-autonomous vehicle may be monitored to obtain operating data from one or more autonomous operation features. An operating status of the autonomous features may be determined based upon the operating data. After which, a change in the operating status of the autonomous features may be identified, and a report containing information regarding the change in the operating status of the autonomous features may be generated. Insurance discounts may be provided to risk averse customers that maintain their autonomous vehicles, and associated accident avoidance functionality, in good working condition.

A vehicle is provided that includes a bio-signal sensor configured to measure a bio-signal of a user and a feedback device configured to interact with the bio-signal of the user. A controller determines a current emotional state of the user based on the bio-signal and adjusts an output signal of the feedback device based on the current emotional state. The output signal leads to a target emotional state in which the degree of excitability is a reduced emotional state.

One or more braking event detection computing devices and methods are disclosed herein based on fused sensor data collected during a window of time from various sensors of a mobile device found within an interior of a vehicle. The various sensors of the mobile device may include a GPS receiver, an accelerometer, a gyroscope, a microphone, a camera, and a magnetometer. Data from vehicle sensors and other external systems may also be used. The braking event detection computing devices may adjust the polling frequency of the GPS receiver of the mobile device to capture non-consecutive data points based on the speed of the vehicle, the battery status of the mobile device, traffic-related information, and weather-related information. The braking event detection computing devices may use classification machine learning algorithms on the fused sensor data to determine whether or not to classify a window of time as a braking event.

A non-transitory computer-readable recording medium stores a driving support program that causes a computer to execute a process including: collecting vital sign information on a user from a vital sign measuring device; generating a drowsiness occurrence time pattern with respect to the user based on the collected vital sign information; and in response to a request in which a driver is specified from a source of the request, providing the drowsiness occurrence time pattern that is generated with respect to the user corresponding to the driver to the source of the request to the source of request or providing alerting information to the driver that is determined according to the drowsiness occurrence time pattern generated with respect to the user corresponding to the driver to the source of request.

Disclosed is a technique of determining a driver's tension level (tension state degree) in vehicle driving in detail with a simple configuration. According to the technique, a nonlinear analyzing unit 110 of a driver's tension level determining apparatus 100 determining the tension level in driving of a driver acquires the driving operation amounts relating to driving operations of a driver (the operation amounts relating to operations of an accelerator pedal, a brake pedal, a handle, and the like), and then calculates the Lyapunov exponents about the driving operation amounts by performing nonlinear analysis processing. A frequency spectrum analyzing unit 120 calculates the power spectral density of time series data of the Lyapunov exponents, and then calculates an integrated value of a predetermined low frequency band in the calculated power spectral density. A driver's tension level determining unit 130 determines that the driver's tension level is any one of an excessive tension state, a moderate tension state, and an insufficient tension state using the integrated value of the predetermined low frequency band.

One object is to enable a preparation period for switching between modes in automated driving to be provided for a vehicle occupant. A vehicle control system includes an automated driving control unit that automatically controls at least one of acceleration/deceleration and steering of a host vehicle, the automated driving control unit performing automated driving control in any one of a plurality of modes having different degrees of automated driving, a detection unit that detects a state of an occupant in the host vehicle, an output unit that outputs notification information on the automated driving control, and a notification condition changing unit that changes a notification condition for outputting the notification information according to the state of the occupant detected by the detection unit.

A heartbeat sensor obtains a heartbeat of a driver. A concentration level computing unit estimates a concentration level of the driver at a time later than a timing of obtaining the heartbeat by the heartbeat sensor, on the basis of the heartbeat obtained by the heartbeat sensor. A state determining unit controls driving assistance for a vehicle on the basis of a comparison of the concentration level of the driver estimated by the concentration level computing unit with a target concentration level of the driver that is set in association with a position or an environment in which the vehicle driven by the driver is predicted to travel.

A vehicle control method is disclosed. The vehicle control method includes: while controlling the driving of a vehicle taking control of itself, determining if there is a need to hand over the control of driving to a passenger in the vehicle; if it is determined that there is a need to hand over the control of driving to a passenger in the vehicle, selecting at least one of passengers to whom the control of driving may be handed over; determining an order of priority in handing over the control of driving by taking into consideration the at least one selected passenger's occupancy state information; handing over the control of driving to a top priority passenger according to the order of priority; and controlling the driving environment by taking into consideration the top priority passenger's occupancy state information.

A method of predictive navigation control for an ego vehicle includes: comparing a cue node to each of a plurality of episodic memory nodes in an episodic memory structure, wherein the cue node represents a new event representing distances, speeds and headings of one or more newly observed objects about the ego vehicle, and wherein the episodic memory structure includes a network of nodes each representing a respective previously existing event and having a respective node risk and likelihood; determining which of the nodes has a smallest respective difference metric, thus defining a best matching node; consolidating the cue node with the best matching node if the smallest difference metric is less than a match tolerance, else adding a new node corresponding to the cue node to the episodic memory structure; and identifying a likeliest next node and/or a riskiest next node.