A vehicle includes a drive device for traveling, and a control device configured to control the drive device so that the vehicle travels with a target driving force based on an accelerator operation amount. The control device is configured to set the target driving force such that a change in the target driving force with respect to a change in the accelerator operation amount is gentler in a case where steady traveling is desired as compared with a case where the steady traveling is not desired. Therefore, in a case where the steady traveling is desired, a variation of a vehicle speed with respect to a slight variation of the accelerator operation amount can be gentle and continuous steady traveling can be facilitated.

A misfire determination period is set to a predetermined range of a crank angle. A CPU performs: a calculation process of calculating an average value of a torque of an output shaft of an internal combustion engine in the misfire determination period; a misfire determining process of determining that a misfire has occurred when the calculated average value is less than a prescribed threshold value; and a process of setting the whole misfire determination period to a period in a positive torque range which is a range of a crank angle at which the torque of the output shaft is equal to or greater than zero at the time of normal combustion in which a misfire does not occur.

In various embodiments, while a self-driving model operates a vehicle, a user monitoring subsystem acquires sensor data associated with a user of the vehicle and a vehicle observation subsystem acquires sensor data associated with the vehicle. The user monitoring subsystem computes values for a psychological metric based on the sensor data associated with the user. Based on the values for the psychological metric, a feedback application determines a description of the user over a first time period. The feedback application generates a dataset based on the description and the sensor data associated with the vehicle. Subsequently, a training application performs machine learning operation(s) on the self-driving model based on the dataset to generate a modified self-driving model. Advantageously, the dataset enables the training application to automatically modify the self-driving model to account for the impact different driving actions have on the user.

A vehicle power management system (100) for optimising power efficiency in a vehicle (400), by managing a power distribution between a first power source (410) and a second power source (420). A receiver (110) receives a plurality of samples from the vehicle (400), each sample comprising vehicle state data, a power distribution and reward data measured at a respective point in time. A data store (350) stores estimated merit function values for a plurality of power distributions. A control system (200) selects, from the data store (350), a power distribution having the highest merit function value for the vehicle state data at a current time, and transmits the selected power distribution to be implemented at the vehicle (400). A learning system (300) updates the estimated merit function values in the data store (350), based on the plurality of samples.

A transmission system for a vehicle having a belt drive transmission. The belt drive has an adjustable input:output ratio, where the output of the belt drive is provided as a first input to a differential coupling. A further rotating connection is provided as a second input to the differential coupling, so that the output of the differential coupling is arranged as the output of the transmission system. Accordingly, the transmission output is based on the aggregate sum of the rotation of the first and second inputs to the differential coupling, wherein adjustment of the input:output ratio of the belt drive allows for a continuously variable transmission system.

A vehicle drive includes a gear set, a first motor/generator coupled to the gear set, a second motor/generator at least selectively rotationally engaged with the gear set, and an engine at least selectively coupled to the gear set and at least selectively coupled to the second motor/generator. The second motor/generator is electrically coupled to the first motor/generator by an electrical power transmission system. The first motor/generator and the second motor/generator are electrically coupled without an energy storage device configured to at least one of (a) provide electrical energy to the first motor/generator or the second motor/generator to power the first motor/generator or the second motor/generator and (b) be charged by electrical energy from the first motor/generator or the second motor/generator.

A method of organizing and accessing vehicle steering preferences includes identifying first operating configurations from a first list and second operating configurations from a second list. The same file is assigned to each of the first and second operating configurations that are identical to one another. A unique file is assigned to each of the first and second operating configurations that is different from all the other first and second operating configurations. A request is received to operate the vehicle in a driving mode associated with one of the first operating configurations. The vehicle is automatically configured with the first operating configuration associated with the requested driving mode.

An execution device executes an obtaining process that obtains a state of a vehicle, an operating process that operates an electronic device of the vehicle based on the state of the vehicle obtained by the obtaining process and operation data, a performance determining process that determines whether an environmental performance of the vehicle when the electronic device is operated is lower than a determination performance, and a data updating process that updates the operation data so as to increase the environmental performance of the vehicle when the performance determining process determines that the environmental performance of the vehicle is lower than the determination performance.

A heavy hybrid truck is powered by a non-electric powered medium and an electric powered axle. The electric powered axle assists the non-electric powered medium when load changes are detected. The electric powered axle is sourced by a rechargeable battery. The non-electric powered medium is either a fossil fuel combustion engine, a biofuel engine, a hydrogen engine, or a combination.

A vehicle control apparatus for controlling a vehicle through a control program using at least one parameter. The at least one parameter is corrected by respective at least one correction value that is obtained after start of execution of a learning operation. The vehicle control apparatus includes: a learning-data storage portion configured to store, as learning data, the at least one correction value obtained after the start of the execution of the learning operation; and a learning-data rewrite portion configured, when the control program is updated, to execute a rewriting operation for rewriting the at least one correction value as the learning data from a pre-update correction value to a post-update correction value, such that the post-update correction value has the same sign as the pre-update correction value, and an absolute value of the post-update correction value is smaller than an absolute value of the pre-update correction value.