A vehicle travel control apparatus includes an actuator and an electronic control unit. The electronic control unit is configured to determine whether a driver of a vehicle is in an abnormal state where the driver loses an ability of driving the vehicle. The electronic control unit is also configured to stop the vehicle at an abnormality determination time point onward, and control a vehicle speed by using the actuator such that the vehicle speed does not become lower than a lower limit vehicle speed in a period from the abnormality determination time point to a time point when the vehicle is stopped. The lower limit vehicle speed is set in accordance with a road shape influencing timing when a driver of another vehicle traveling behind the vehicle visually recognizes the vehicle.

A device may receive vehicle operation data associated with operation of a plurality of vehicles, and may process the vehicle operation data to generate processed vehicle operation data. The device may extract multiple features from the processed vehicle operation data, and may train machine learning models, with the multiple features, to generate trained machine learning models that provide model outputs. The device may process the multiple features, with a feature selection model and based on the model outputs, to select sets of features from the plurality of features, and may process the sets of features, with the trained machine learning models, to generate indications of driving behavior and reliabilities of the indications. The device may select a set of features, from the sets of features, based on the indications and the reliabilities, where the set of features may be calculated by a device associated with a particular vehicle.

An automatic driving control plan creation unit creates a plan of an automatic driving section in which a subject vehicle is automatically driven, and a plan of a driving switching preparation section for switching the subject vehicle from automatic driving to manual driving. For each point in the driving switching preparation section, a driving load calculation unit calculates a driving load applied to a driver when the driver manually drives the subject vehicle. A driving switching permission determination unit permits switching of the subject vehicle from automatic driving to manual driving at a point where the driving load is smaller than a predetermined threshold value, and does not permit switching of the subject vehicle from automatic driving to manual driving at a point where the driving load is equal to or larger than the threshold value. A permission standard relaxation unit makes the driving load difficult to exceed the threshold value.

An apparatus for automatically controlling a driving assist function of a vehicle includes a sensing device to sense surrounding information and driving information of the vehicle and a state of a driver, a controller to determine whether an automatic activation condition for automatically activating the driving assist function is satisfied, based on the surrounding information and the driving information of the vehicle, which are acquired from the sensing device, and the sensed driver state, and automatically activate the driving assist function when the automatic activation condition is satisfied.

A method for operating a vehicle having a driver assistance system intervening in a transverse dynamics of the vehicle, comprising the steps: detecting a driver intervention in a driving behavior of the vehicle due to an intervention of at least one actuator triggered by the driver assistance system; interpreting the driver intervention as an override of the intervention of the actuator; and reducing in a defined manner the intervention of the actuator as a function of the interpretation of the driver intervention in such a way that a driving task is returned to the driver in a controlled and defined manner.

A device may receive vehicle operation data associated with operation of a plurality of vehicles, and may process the vehicle operation data to generate processed vehicle operation data. The device may extract multiple features from the processed vehicle operation data, and may train machine learning models, with the multiple features, to generate trained machine learning models that provide model outputs. The device may process the multiple features, with a feature selection model and based on the model outputs, to select sets of features from the plurality of features, and may process the sets of features, with the trained machine learning models, to generate indications of driving behavior and reliabilities of the indications. The device may select a set of features, from the sets of features, based on the indications and the reliabilities, where the set of features may be calculated by a device associated with a particular vehicle.

The technology relates to identifying and addressing aberrant driver behavior. Various driving operations may be evaluated over different time scales and driving distances. The system can detect driving errors and suboptimal maneuvering, which are evaluated by an onboard driver assistance system and compared against a model of expected driver behavior. The result of this comparison can be used to alert the driver or take immediate corrective driving action. It may also be used for real-time or offline training or sensor calibration purposes. The behavior model may be driver-specific, or may be a nominal driver model based on aggregated information from many drivers. These approaches can be employed with drivers of passenger vehicles, busses, cargo trucks and other vehicles.

A vehicle control method and system includes: acquiring a face image of a user currently requesting to use a vehicle; acquiring a feature matching result between the face image and at least one pre-stored face image in a data set of the vehicle, where the data set stores pre-stored face images of at least one pre-recorded user allowed to use the vehicle; and if the feature matching result indicates that the feature matching is successful, controlling actions of the vehicle to allow the user to use the vehicle. The rights of pre-recorded personnel can be guaranteed based on feature matching, and feature matching can be achieved without a network, thereby overcoming the dependency on the network and further improving the safety guarantee of the vehicle.

A vehicle driving control system has an automatic driving mode that enables traveling without a driver's operation. The vehicle driving control system includes a driver monitor that monitors a driver in a vehicle compartment, and detects a driver state, a driver state determining unit that determines, based on the driver state, whether the driver is in a state capable of driving normally, a retreat mode controller that controls a retreat mode that causes an own vehicle to travel for retreat to a place where the own vehicle can safely stop when it is determined, during traveling under the automatic driving mode, that the driver is not in the state capable of driving normally, and a retreat mode canceler that cancels the retreat mode when it is determined, during the travel for retreat, that the driver has returned to the state capable of driving normally.

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.