A system includes a camera configured to capture image data of an environment, a monitoring system configured to generate a gaze sequences of a subject, and a computing device communicatively coupled to the camera and the monitoring system. The computing device is configured to receive the image data from the camera and the gaze sequences from the monitoring system, implement a machine learning model comprising a convolutional encoder-decoder neural network configured to process the image data and a side-channel configured to inject the gaze sequences into a decoder stage of the convolutional encoder-decoder neural network, generate, with the machine learning model, a gaze probability density heat map, and generate, with the machine learning model, an attended awareness heat map.

A system for facilitating transfers of control includes an input module configured to acquire a set of user preferences relating control states to contexts, the control states including at least a manual control state, and an autonomous and/or semi-autonomous control state. The system includes a processing device configured to automatically perform, during operation of a semiautonomous system, generating a transfer of control (TOC) policy based on the set of user preferences and a current context state, the TOC policy prescribing transitions between control states in response to actions. The processing device is also configured to perform, based on an action performed by the user or a control system, determining whether to perform a transition from a current control state to a second control state, and in response to the TOC policy prescribing the transition, transitioning from the current control state to the second control state.

An emotion inference device and an emotion inference system that are capable of inferring a user's emotion with higher precision. A motorcycle includes an individual personality that is configured on the basis of information on a user from a plurality of products associated with the user, connected to a communication network, and including the motorcycle, an automobile, a rice cooker, a vacuum cleaner, a television receiver, and a refrigerator, the individual personality forms a base personality, and the motorcycle includes an emotion detecting section that detects an emotion.

A method for transferring a motor vehicle from an autonomous driving mode, in which the motor vehicle is guided autonomously, into a manual driving mode, in which the motor vehicle is guided by a vehicle driver. In the method, pieces of information for supporting the transfer are ascertained with the aid of a cognitive model of the vehicle driver, the cognitive model describing at least one perception process of the vehicle driver with respect to a driving situation and at least one decision-making process of the driver with respect to an action option. A device configured for executing the method is also described.

A motor vehicle cruise control system includes: an arithmetic unit configured to calculate a physical momentum of a traveling device for achieving a target motion of a motor vehicle that is traveling along a traveling route generated, based on an output from a vehicle exterior information acquisition device; and a device controller configured to generate, and output, an actuation control signal for the traveling device in the motor vehicle, based on an arithmetic result obtained by the arithmetic unit. Driving operation information on an operation performed by a driver is input to both the arithmetic unit and the device controller in parallel. The arithmetic unit is configured to reflect the driving operation information in a process of determining the target motion. The device controller is configured to reflect the driving operation information in the control of the actuation of the traveling device.

Systems and methods for personalizing adaptive cruise control in a vehicle are disclosed herein. One embodiment collects vehicle-following-behavior data associated with a particular driver; trains a Gaussian Process (GP) Regression model using the collected vehicle-following-behavior data to produce a set of adaptive-cruise-control (ACC) parameters pertaining to the particular driver, the set of ACC parameters modeling learned vehicle-following behavior of the particular driver; generates an acceleration command for the vehicle based, at least in part, on the set of ACC parameters; applies a predictive safety filter to the acceleration command to produce a certified acceleration command that has been vetted for safety; and controls acceleration of the vehicle automatically in accordance with the certified acceleration command to regulate a following distance between a lead vehicle and the vehicle in accordance with the learned vehicle-following behavior of the particular driver.

Systems and methods are provided for communicating a blending parameter via tactile feedback at a driver's seat of a vehicle (examples of tactile feedback may comprise vibrations and temperature/heat applied through the driver's seat). The blending parameter may represent the ratio between the driver's level of authority and an autonomous driving system's level of authority in performing a driving task (e.g., lateral steering). By communicating changes to a blending parameter over time, examples can help a driver form a mental picture of how the vehicle/autonomous driving system is operating. This feedback/understanding may by advantageous for various purposes such as driver coaching and helping drivers become more comfortable with autonomous driving systems.

Exhaustive driving analytical methods, systems, are apparatuses are described. The methods, systems, are apparatuses relate to utilizing partially available data associated with driver and/or driving behaviors to determine safety factors, identify times to react to events, and contextual information regarding the events. The methods, systems, and apparatuses described herein may determine, based on a systematic model, reactions and reaction times, compare the vehicle behavior (or lack thereof) to the modeled reactions and reaction times, and determine safety factors and instructions based on the comparison.

In a method for driving maneuver assistance of a vehicle, a predefined neural network is provided, which is designed to determine whether a predefined driving maneuver is probably possible. A predefined driver model is provided, which is designed to predict a probable future behavior of a vehicle. A current driving situation of the vehicle is determined. Depending on the determined driving situation, the driver model and the neural network, it is determined whether a predefined driving maneuver is possible. Depending on the determination as to whether the driving maneuver is possible, a driver assistance function for the driving maneuver is carried out and/or the driving maneuver is carried out autonomously.

A system includes a processor configured to receive a user profile responsive to an efficiency determination request for a vehicle model. The processor is also configured to obtain efficiency-affecting data from the user profile. The processor is further configured to compare the efficiency-affecting data to data gathered from drivers of the vehicle model, to determine a correlation between the user profile and similar drivers of the vehicle model. Also, the processor is configured to predict fuel efficiency for the new vehicle model based on efficiency achieved by the similar drivers.