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.

An intelligent driving system with an embedded driver model. The system includes a driver model module that adjusts vehicle performances according to driving characteristics of a driver and road environment. A driver's visual and tactile information may be taken into account when driving a vehicle, so as to tune vehicle performances to allow the vehicle to adapt itself to the individual driver.

Trip is analyzed from a population of drivers in order to determine one or more indicators of one or more driving styles. The trip data may include sensor information obtained from one or more sensor devices which are present in a vehicle of each driver of the population. A driving style is determined for the driver during a monitored trip by analyzing sensor information obtained from one or more sensor devices of the driver during the trip for at least one of the indicators of the one or more driving styles.

A digital cockpit system communicates with a cloud server and outputs an output result of a vehicle's internal system according to a human-machine interface (HMI) output policy optimized for a personal driving tendency of a personal driver by using a driver-customized parameter received from the cloud server.

System, methods, and other embodiments described herein relate to communicating a blending parameter. In one embodiment, a method includes obtaining a blending parameter that is indicative of a degree to which control of a vehicle is shared between an operator of the vehicle and an advanced driver-assistance system (ADAS) of the vehicle. The method further includes determining a feedback parameter based upon the blending parameter and a psychophysical model, wherein the psychophysical model optimizes a relationship between the blending parameter and the feedback parameter such that sensory feedback based upon the feedback parameter is perceived to be proportional to the blending parameter. The method further includes causing a feedback modality of a steering apparatus of the vehicle to provide the sensory feedback based upon the feedback parameter.

Methods and systems for operating a hybrid vehicle having a first power source that uses a rechargeable battery and a second power source that uses a fuel. Preview information relating to upcoming road and traffic is used to generate a speed reference. A transmission torque reference is calculated using the speed reference and upcoming road information. A predictive power split plan is then determined by optimizing use of the first and second power sources to satisfy the transmission torque reference. At least a first sample of the predictive power split plan is then implemented.

Methods and systems for hybrid vehicle control. A high-level controller and a low-level controller are provided. The high-level controller uses preview information and a model of the low-level controller to calculate optimized tuning parameters for the low-level controller. The low-level controller uses driver inputs and current operating states to calculate optimized torque split for the hybrid engine.

A driving model generation device is provided that enables driving evaluation according to a traveling situation of a vehicle. A vehicle is provided with a vehicle state detector, which detects a vehicle state quantity that changes according to a driving operation by a driver. A model generator generates a driving model, which is a reference related to a specific driving operation, based on the vehicle state quantity at the time the driver starts the specific driving operation and the vehicle state quantity at the time the specific driving operation is finished. A driving evaluator evaluates the driving skill of the driver by comparing the driving operation of the driver presented by a detection result of the vehicle state detector to the driving model generated by the model generator.

The present disclosure provides a wakefulness determination method for accurately determining wakefulness. The wakefulness determination method uses a respiration sensor that obtains respiratory data about respiration of a seated occupant, a calculation unit that calculates the respiratory data obtained from the respiration sensor, and a controller including a determination unit that determines a state of the seated occupant. The wakefulness determination method includes: obtaining, by the respiration sensor, respiratory data of the seated occupant; calculating, by the calculation unit, a degree of change in respiration from the obtained respiratory data; and determining, by the determination unit, wakefulness of the seated occupant by using a Bayesian filter where a probability of occurrence of drowsiness in the seated occupant for the degree of change in respiration is taken as a likelihood and the likelihood is multiplied by a prior probability of occurrence of drowsiness.

Trip is analyzed from a population of drivers in order to determine one or more indicators of one or more driving styles. The trip data may include sensor information obtained from one or more sensor devices which are present in a vehicle of each driver of the population. A driving style is determined for the driver during a monitored trip by analyzing sensor information obtained from one or more sensor devices of the driver during the trip for at least one of the indicators of the one or more driving styles.