This information processing device is equipped with: an actual travel data acquisition means that acquires actual travel data, which is travel data obtained by the driving of a vehicle by a driver; a simulated travel data acquisition means that uses travel environment data indicating the travel environment associated with the travel, and a driver model that determines the operation of the vehicle with respect to the travel environment, to acquire simulated travel data, which is travel data obtained from a simulator that simulates the driving of the vehicle by the driver; and a comparison means that compares the values of multiple indices of the actual driving data and the values of multiple indices of the simulated travel data, and that outputs the comparison results.

A system and method for predicting whether a driver of a host vehicle or a remote vehicle intends to make a left or right turn or travel straight through an intersection before the host vehicle or remote vehicle reaches the intersection that relies on a probability model that employs a dynamic Bayesian network. The method includes obtaining a plurality of environmental cues that identify external parameters at or around the intersection, where the environmental cues include position and velocity of the remote vehicle, and obtaining a plurality of host vehicle cues that define operation of the host vehicle. The method then predicts the turning intent of the host vehicle and/or remote vehicle at the intersection using the model based on both the external cues and the vehicle cues using the model. The model can use learned information about previous driver turns at the intersection.

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.

A system may include a plurality of vehicle sensor and a computer comprising a processor and memory storing instructions executable by the processor. One of the instruction may comprise to determine a driving responsiveness (DR) value using a weighted sum comprising indices of a transition probability matrix (Q), Q being derived from likelihood of transition data () between a plurality of driving modes from a set of interacting multiple model (IMM) instruction.

A steer-by-wire steering system may include a steering adjuster that is electronically controlled depending on driver input, a feedback actuator transmitting feedback from a road, and a control unit that actuates the feedback actuator and the steering adjuster. The control unit comprises an estimator including a monitor and a model of the feedback actuator. The estimator may estimate a driver's steering torque based on measurement values of the feedback actuator and with the model and the monitor, then providing the driver's steering torque as a result. The control unit further comprises a filter unit that analyzes the measurement values of the feedback actuator by determining the damping of amplitudes of predetermined frequency ranges and to provide the result. The control unit further comprises a decision unit that decides whether a driver's hand is in contact with a steering wheel by using the results of the filter unit and the estimator.

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.

An information processing system includes a processor, and the processor acquires a vehicle driving environment composed of N-dimensional parameters, refers to an ideal driving driver model indicating an ideal driving environment region in an N-dimensional coordinate system, and selects one ideal driving environment included in the ideal driving environment region based on a distance between the ideal driving environment region and the acquired vehicle driving environment in the N-dimensional coordinate system.

A steer-by-wire steering system may include a steering adjuster that is electronically controlled depending on driver input, a feedback actuator transmitting feedback from a road, and a control unit that actuates the feedback actuator and the steering adjuster. The control unit comprises an estimator including a monitor and a model of the feedback actuator. The estimator may estimate a driver's steering torque based on measurement values of the feedback actuator and with the model and the monitor, then providing the driver's steering torque as a result. The control unit further comprises a filter unit that analyzes the measurement values of the feedback actuator by determining the damping of amplitudes of predetermined frequency ranges and to provide the result. The control unit further comprises a decision unit that decides whether a driver's hand is in contact with a steering wheel by using the results of the filter unit and the estimator.

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.

Systems and methods are provided to enhance the driving performance of a leanable vehicle such as a motorcycle. The system includes a leanable vehicle interface to receive input from a driver (e.g., a human or a robotic driver) and a sensor interface to receive inputs from sensors on the leanable vehicle. The system also includes a computing module to use the sensor data in combination with data from the leanable vehicle interface to calculate the driver behavior to produce a future desired performance, based on a specified aggressiveness, so that the performance of the leanable vehicle is optimized. The calculation may be done using a machine learning method, a rule based method, or both.