Systems and methods are provided for predicting blind spot incursions for a host vehicle. In one implementation, a navigation system for a host vehicle may comprise a processor. The processor may be programmed to receive, from an image capture device located on a rear of the host vehicle, at least one image representative of an environment of the host vehicle. The processor may be programmed to analyze the at least one image to identify an object in the environment of the host vehicle and to determine kinematic information associated with the object. The processor may further be programmed to predict, based on the kinematic information, that the object will travel in a region outside of a field of view of the image capture device and perform a control action based on the prediction.

A vehicle control device includes a first control unit that executes, when an abnormality of a driver of a vehicle is detected, stop control, a second control unit that executes, when the vehicle is determined to have a risk of collision, deceleration control, a determination unit that identifies an object around the vehicle as a target candidate of the collision and determines whether or not there is the risk of the collision with the identified target candidate, and a setting unit that sets, when the abnormality is detected, an operation mode of the deceleration control to a special mode from a normal mode, the normal mode provided for cases in which the abnormality is undetected. The determination unit expands a range for identifying the object around the vehicle as the target candidate of the collision in the special mode as compared with the range in the normal mode.

An estimation apparatus of the disclosure includes a determiner that determines whether or not an attention target is visually recognizable from a driver of a vehicle based on information related to a visual field range of the driver; and an estimator that estimates a visual field abnormality of the driver based on a driving operation of the driver in a case where the determiner determines that the attention target is visually recognizable from the driver.

A human machine interface control unit as an information presentation control device is used in a vehicle having an autonomous driving function to perform a driving action on behalf of a driver, and controls an information presentation device configured to present information to the driver. The human machine interface control unit includes a permissible action determination unit configured to determine a permissible action that a driver is permitted to take among actions, other than a driving action, to be possibly taken by the driver when the autonomous driving function is implemented, and an information presentation content control unit configured to cause an information presentation device to present information related to a determination result of the permissible action.

In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

According to an aspect of the present disclosure, a collision warning apparatus of a vehicle may include an information acquisition device that obtains surrounding object information and vehicle information and a controller that generates collision prediction information of a surrounding object based on the surrounding object information and the vehicle information and provides a warning to an outside of the vehicle or generates control information for controlling braking of the vehicle while providing the warning to the outside of the vehicle based on the collision prediction information.

Systems and methods for distracted driving detection are described. A method includes receiving proximate vehicle data about a proximate vehicle proximate to the host vehicle. The method also includes estimating one or more baselines for a predetermined future time for the proximate vehicle from the proximate vehicle data. The method further includes comparing current kinematic data of the proximate vehicle data for the predetermined future time to the one or more baselines. The method includes generating distraction flags associated with the proximate vehicle based on the comparison. The method also includes controlling one or more vehicle systems of the host vehicle based on the generated distraction flags.

A vehicle drive assist apparatus to be applied to a vehicle executes at least emergency braking control for avoiding collision of the vehicle with a recognized object. The vehicle drive assist apparatus includes a surrounding environment recognition device and a traveling control unit. The surrounding environment recognition device includes a recognizer that recognizes a surrounding environment around the vehicle, and a feature information acquirer that acquires feature information of a target object in the recognized surrounding environment. The traveling control unit centrally controls the entire vehicle. The traveling control unit includes a determiner that determines, based on the acquired feature information, whether the target object has a possibility of hindering traveling of the vehicle. The traveling control unit continues normal traveling control for the vehicle when the target object is determined to have no possibility of hindering the traveling of the vehicle.

Systems and methods are provided for predictive assessment of driver perception abilities based on driving behavior personalized to the driver in connection with, but not necessarily, autonomous and semi-autonomous vehicles. In accordance with on embodiment, a method comprises receiving first vehicle operating data and associated first gaze data of a driver operating a vehicle; training a model for the driver based on the first vehicle operating data and the first gaze data, the model indicating driving behavior of the driver; receiving second vehicle operating data and associated second gaze data of the driver; and determining that an ability of the driver to perceive hazards is impaired based on applying the model to the second vehicle operating data and associated second gaze data.

Disclosed is a system and method for generating a vehicle speed alert in a driving simulation system that communicates with a remote driving simulator engine and a remote speed optimization engine. The remote speed optimization engine receives data from the vehicle speed alert system after the vehicle speed alert system has received data from the remote driving simulator engine, including the vehicle's simulated distance to a signalized intersection, the time remaining for the simulated traffic light to change, and the current simulated light status (green, yellow, red) of the traffic light. The vehicle speed alert system then receives from the remote speed optimization engine a recommended speed profile for that given instant, and if the driver's current speed does not fall within some maximum difference with the current recommended speed profile, calculates and transmits an alert to an output device to alert the driver of action necessary to achieve the recommended speed profile.