Embodiments of the present invention provide a control system for a vehicle, the vehicle (800) being operable in an at least partly autonomous mode and a manual mode, the control system comprising input means (291, 292) for receiving a gaze signal (211) indicative of a direction of a gaze of an occupant of the vehicle (800), and a contact signal (212) indicative of physical contact between the occupant and a control of the vehicle, control means (250, 260, 270, 280, 290) configured to determine an estimate of the occupant's attention to a driving task in the at least partly autonomous mode in dependence on the gaze signal (211) and to determine when the occupant is at least partly obscured relative to the at least one imaging means (150, 220), wherein the control means (250, 260, 270, 280, 290) is configured to determine the estimate of the occupant's attention to the driving task in dependence on the contact signal when it is determined that the occupant is at least partly obscured.

A safe driving assistance system includes a wearable terminal having a terminal identification information, a first and a second sensor acquiring a biometric information of a driver, a safe driving assistance in-vehicle device having a criteria determination unit. The criteria determination unit determines that a blood glucose level is a predetermined number or less and at least one selected the group consisting of that a heart rate is more than a predetermined number, a skin temperature change is more than a predetermined number and an increase in sweat rate is more than a predetermined number, a safe driving assistance in-vehicle device is connected to a vehicle control unit controlling a vehicle driven by the driver, and an operating state of the vehicle is controlled based on a determination result by the criteria determination unit.

A computer-implemented method for operating an autonomous or semi-autonomous vehicle may include identifying a vehicle operator and retrieving an associated vehicle operator profile. Operating data regarding operation of the autonomous or semi-autonomous vehicle may be received that includes data from sensors disposed within the vehicle. When a request to enable an autonomous operation feature is received, (i) autonomous operation risk levels associated with vehicle operation by the autonomous operation feature based upon the received operating data, and (ii) operator risk levels associated with vehicle operation by the vehicle operator based upon the vehicle operator profile are determined. Autonomous operation feature enablement may be allowed based upon a comparison of (i) autonomous operation risk levels with (ii) operator risk levels. As a result, only safe autonomous feature engagement may be facilitated, and risk averse vehicle owners may receive insurance discounts based upon this safe autonomous feature engagement functionality.

A stop assist device is equipped with an operation switch that is operated to activate and deactivate a hazard lamp of a vehicle. The stop assist device starts stop assist control for stopping the vehicle through automatic deceleration of the vehicle and starts activating the hazard lamp from an abnormality detection timing when it is determined that an abnormality condition that is fulfilled when a driver falls into an abnormal state in which the driver is unable to drive the vehicle is fulfilled. The stop assist device continues to perform stop assist control when the operation switch is operated before the lapse of a predetermined invalid time from the abnormality detection timing during the performance of stop assist control, and ends stop assist control when the operation switch is operated after the lapse of the invalid time from the abnormality detection timing during the performance of stop assist control.

A drive planning device has a processor configured to assess whether or not there is a problem with the driver and to assess whether or not a predetermined condition has been met based on information for the surrounding environment of a vehicle, and to create a first driving plan in which the vehicle is moved to the shoulder and stopped when it has been assessed that there is a problem with the driver and the predetermined condition has been met, or to create a second driving plan in which the vehicle is stopped in the traffic lane in which the vehicle is traveling when it has been assessed that there is a problem with the driver and the condition assessment unit has assessed that the predetermined condition has not been met.

An autonomous driving control device is capable of starting an autonomous driving control without an operation of a driver and reducing a possibility that the driver can not start manual driving. An autonomous driving control is switched to manual driving when a determination section determines that the amount of operation by the driver is equal to or greater than a first threshold, before a predetermined time elapses since the autonomous driving control is automatically started. An autonomous driving control is switched to a manual driving when the determination section determines that the amount of operation by the driver is equal to or greater than a second threshold that is greater than the first threshold, after the predetermined time elapses.

A system and method for controlling a vehicle based on a driver status are disclosed. The vehicle control system includes various sensing devices (including a camera, a vehicle dynamics sensor, a vehicle around view monitoring (AVM) camera, a periphery surveillance sensor, and a navigation device) and an electronic control unit (ECU). The ECU may analyze a driver status through the driver's face and pupils recognized by output signals of the sensing devices. If the driver has no intention to drive the vehicle, the ECU may control the vehicle to stop on a road shoulder, resulting in guarantee of safer driving.

The systems and methods disclosed herein provide a risk prediction system that uses trained machine learning models to make predictions that a VRU will take a particular action. The system first receives, in a video stream, an image depicting a VRU operating a micro-mobility vehicle and extract the depictions from the image. The extraction process may be determined by bounding box classifiers trained to identify various VRUs and micro-mobility vehicles. The system feeds the extracted depictions to machine learning models and receives, as an output, risk profiles for the VRU and the micro-mobility vehicle. The risk profile may include data associated with the VRU/micro-mobility vehicle determined based on classifications of the VRU and the micro-mobility vehicles. The system may then generate a prediction that the VRU operating the micro-mobility vehicle will take a particular action based on the risk profile.

Disclosed herein is a driver assistance system including a gripping detection sensor provided on a steering wheel of a vehicle and configured to detect a driver gripping the steering wheel and acquire gripping information, and a controller configured to receive the gripping information from the gripping detection sensor, receive behavior information from a behavior detection device configured to acquire the behavior information of the vehicle, and determine whether the driver is dozing, wherein the controller is configured to match the gripping information according to the behavior information, predict gripping information according to current behavior information based on a result of the matching to generate gripping prediction information, and compare current gripping information with the gripping prediction information according to the current behavior information to determine whether the driver is dozing.

A pattern recognition system receives an image captured by an image capture device, of an operator and the operator is identified. Operator information is accessed, based upon the identified operator, and a control signal is generated to control a mobile machine, based upon the operator information.