The disclosed embodiments are directed to adjusting the operational characteristics of a black box installed in a vehicle. In one embodiment a method is disclosed comprising classifying a mental state of a driver of an automobile, loading at least one setting based on the mental state, the setting defining an operational characteristic of a black box installed in the automobile, configuring the black box using the at least one setting, and recording one or more events by the black box.

Techniques are described that enable automatic emergency braking (AEB) for a path-crossing target when a collision between a host vehicle and the target that is deemed imminent. Based on whether an acceleration of the host vehicle is above a threshold. Based on the acceleration, and, optionally, a location of the target relative to a crossing path (e.g., whether a portion of the target is within a suppression zone), an AEB system of the host vehicle is either activated or not activated, for example, suppressed. This suppression of the AEB system may include gating or nulling an AEB activation signal to prevent an emergency braking event. By managing the AEB system in a path-crossing scenario, many common false-positive AEB events (warnings, alerts, and/or braking) may be avoided. Furthermore, intentional vehicle maneuvers that comply with normal driving etiquette or rules can still be allowed for operator and passenger comfort, without risking safety.

Disclosed is a simulation learning-based drowsy driving simulation platform system for detecting careless driving in conjunction with deep learning, the simulation learning-based drowsy driving simulation platform system comprising: a drive state warning device configured to determine a driver's careless driving from a captured image, determine a driver's careless driving determination level, and output the determined level; a smart cruise control interworking part configured to transmit the driver's careless driving determination level outputted from the drive state warning device; and a smart cruise control processing part configured to control a vehicle according to the driver's careless driving determination level transmitted by the smart cruise control interworking part, during a smart cruise control operation.

A method for adaptive lateral guidance of a motor vehicle method comprises: determining, by a driver state monitoring system of the motor vehicle, an attentiveness level of a driver steering the motor vehicle; and adapting, in case that the attentiveness level of the driver fulfills predefined criteria, a lateral steering configuration of a lateral guidance assistant of the motor vehicle by at least one of i) increasing a lateral steering tolerance for triggering a steering intervention of the lateral guidance assistant, and ii) suppressing a lateral steering intervention of the lateral guidance assistant.

System, methods, and other embodiments described herein relate to adapting notifications according to monitoring states of a vehicle operator. In one embodiment, a method includes acquiring sensor data associated with an operator of a vehicle. The method also includes determining an operator state from the sensor data. The method also includes computing a difference of the operator state to a parameter associated with monitoring a component in the vehicle. The method also includes adapting a notification associated with the component by a controller according to the difference.

A method, a structure, and a computer system for assessing a cognitive state of a driver of a vehicle. The exemplary embodiments may include collecting data from one or more sensors positioned around the vehicle and calculating a distraction value, an engagement value, and a workload value corresponding to the driver of the vehicle based on the data. The exemplary embodiments may further include determining whether the driver exhibits a low cognitive state based on the distraction value and the engagement value, and, based on determining that the driver exhibits the low cognitive state, assuming control of the vehicle.

Systems and methods for improving driver attention awareness are disclosed herein. One embodiment monitors the attention status of the driver of a vehicle; detects, based on the monitoring, the commencement of a distracted-driving incident; records, during the distracted-driving incident, information pertaining to the distracted-driving incident; detects, based on the monitoring, the end of the distracted-driving incident; and reports, at the earliest opportunity after the end of the distracted-driving incident and prior to the conclusion of the current trip, the information to the driver.

A vehicle integrated controller includes an in-vehicle communication module configured to communicate with at least one controller mounted on the vehicle; a collection module configured to collect collection information including controller information, which is information on the at least one controller, through the in-vehicle communication module; and an aging abnormality processor configured to calculate an aged state or an abnormal state of the at least one controller based on the collection information, and when there exists a controller (hereinafter, “control target controller”) in the aged state or abnormal state among the at least one controller, calculate a value of a control parameter for the control target controller to maintain basic performance.

A vehicle-assist system comprising one or more sensors to monitor an environment of a vehicle and an eye-tracking system, including an eye-tracking sensor, to determine a gaze characteristic of a driver of the vehicle. The vehicle-assist system is to detect a hazard, and determine a hazard location of the hazard, in the environment of the vehicle. Based on the hazard location and the gaze characteristic of the driver, the vehicle-assist system is to output an indication of the hazard to the driver.

A first imaging section 201 captures an image of the vehicle exterior, and a second imaging section 202 captures an image of a driver. An image processing section 203 detects the driver's line of sight. An information acquisition section 205 acquires status information indicative of the surrounding environment. A driving parameter information generation section 206 generates driving parameter information by use of, as parameter information, the current position detected by a self-position detection section 204, travel control information generated by a travel control section, information indicative of the driver's line of sight, information indicative of the surrounding environment, and the like. A determination section 209 determines whether there is a danger on the basis of a difference between the driving parameter information indicative of the state of the vehicle or of its driver on one hand, and optimum parameter information indicative of an optimum state of the vehicle or of its driver in the current position and in the current surrounding environment on the other hand. An information presentation section 214 presents driving assistance information on the basis of a result of the determination. On the basis of the determination result, a travel control section 210 provides traveling assistance such as to reduce the difference in parameter information. The present technology enables appropriate driving assistance.