Examples described herein provide a computer-implemented method that includes receiving, by a processing device, a current state of a vehicle. The method further includes predicting, by the processing device using an output of an artificial intelligence model, a future state of the vehicle based at least in part on the current state of the vehicle. The method further includes calculating, by the processing device using a tunable reward function, a reward associated with the future state of the vehicle, the tunable reward function comprising multiple tunable coefficients. The method further includes training, by the processing device, the artificial intelligence model based at least in part on the reward.

Techniques for cognitive analysis for directed control transfer with autonomous vehicles are described. In-vehicle sensors are used to collect cognitive state data for an individual within a vehicle which has an autonomous mode of operation. The cognitive state data includes infrared, facial, audio, or biosensor data. One or more processors analyze the cognitive state data collected from the individual to produce cognitive state information. The cognitive state information includes a subset or summary of cognitive state data, or an analysis of the cognitive state data. The individual is scored based on the cognitive state information to produce a cognitive scoring metric. A state of operation is determined for the vehicle. A condition of the individual is evaluated based on the cognitive scoring metric. Control is transferred between the vehicle and the individual based on the state of operation of the vehicle and the condition of the individual.

Systems and methods are disclosed for determining a distraction level of a driver. A real-time driver analysis computer may receive sensor data from one or more driver analysis sensors. The real-time driver analysis computer may analyze the sensor data to determine a distraction level of a driver. Based on the distraction level, the real-time driver analysis computer may send one or more control signal to the vehicle and output one or more alerts to a mobile device associated with the driver.

A method for warning a driver of a motor vehicle and to a control device for a motor vehicle are disclosed. The method comprises: steps: receiving, using the vehicle, position data of a first object from a communication device external to the vehicle; detecting a vehicle environment by a detection device of the motor vehicle and detecting an own position of the motor vehicle. In addition, determining if the detection device detects a second object hiding the first object in a field of view between the motor vehicle and the first object, and determining if in a potentially critical situation for the motor vehicle results from the first object. If it is recognized that the first object is hidden and that the potentially critical situation for the motor vehicle results from the first object, a specified warning cascade is initiated.

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.

A distracted driving analysis system for identifying distracted driving events is provided. The system includes a processor in communication with a memory device programmed to: (i) receive driving event records including phone usage by a user that occurred within a time period of a driving event, (ii) divide the driving event records into at least two clusters based at least in part upon common features of one or more driving event records of the plurality of driving event records by processing the driving event records using an unsupervised machine learning algorithm, (iii) generate a trained model based at least in part upon the at least two clusters including cluster labels, (iv) process a new driving event using the trained model, (v) assign the new driving event to one of the at least two clusters using the trained model, and (vi) based at least in part upon the cluster labels for the assigned cluster, determine whether the new driving event is an actual distracted driving event or a passenger event.

An apparatus and a method are configured to control a steering wheel of an autonomous driving vehicle. A state of a driver may be monitored during autonomous driving, and at least one of an upward/downward location, a leftward/rightward tilting angle, or an axial movement of the steering wheel of the autonomous driving vehicle may be controlled based on the state of the driver. As a result, danger information may be delivered to the driver through a dual system during autonomous driving.

A vehicle control device includes a sleep depth estimation unit that estimates a sleep depth of an occupant seated in a seat provided in a vehicle, and a control unit that controls the vehicle such that a magnitude of the external force to be applied to at least one of a plurality of the occupants is equal to or less than a first threshold value when the estimated sleep depth of the occupant is equal to or lower than a predetermined reference depth or when the occupant is awake, and that controls the vehicle such that the magnitude of the external force to be applied to each of all the occupants is equal to or less than a second threshold value larger than the first threshold value when the sleep depth of each of the occupants is greater than the reference depth.

The present disclosure relates to an information processing apparatus, an information processing method, and a program that allow for appropriately pulling over to a safe road shoulder when an emergency occurs during automated driving. On the basis of distance information from a depth sensor or the like, a travelable region available for a vehicle to travel is set like an occupancy map, and image attribute information is generated from an image by semantic segmentation. On the basis of the image attribute information, an evacuation space is set in the travelable region in accordance with the situation of the road surface of the travelable region, and thus an evacuation space map is created. The present disclosure can be applied to a mobile object.

An HMI control device includes an automation level acquisition portion and a display control unit. The automation level acquisition portion acquires an autonomous driving level determined by a driving control device. The display control portion controls an image display operation of an HMI device according to the autonomous driving level acquired by the automation level acquisition portion. When terminating a high autonomous driving level as the autonomous driving level, the display control portion controls the HMI device to provide an action instruction display that instructs the driver to take a low-level associated state so as to handle a low autonomous driving level. The low autonomous driving level is the autonomous driving level under which an in-vehicle system including the driving control device is prohibited from performing at least one of a lateral motion control realized by performing steering and a longitudinal motion control realized by performing acceleration/deceleration.