A motorcycle which provides multi-purpose high-agility hybrid power having an integrated rear-wheel structure in which a tire is mounted on an outer wheel is disclosed. An electric motor and a one-way clutch are mounted on an inner wheel. The motorcycle provides a plug-in mode and provides automatic power conversion to reduce the overload of the electric motor in an automatic driving mode.

In one embodiment, a steering control delay is measured, where the steering delay represents the delay between the time of issuing a steering control command and the time of a response from one or more wheels of an autonomous vehicle. A speed control delay is measured between the time of issuing a speed control command and the time of a response from one or more wheels of the autonomous vehicle or the time of supplying pressure to the gas pedal or brake pedal. In response to a given route subsequently, an overall system delay is determined based on the steering control delay and the speed control delay using a predetermined algorithm. Planning and control data is generated in view of the system delay for operating the autonomous vehicle.

A vehicle, and a method of controlling a vehicle, is capable of selecting a wheel speed that is most appropriate to obtain a speed of the vehicle from among wheel speeds of the vehicle to obtain an accurate speed of the vehicle. The vehicle includes a sensor configured to obtain wheel speed information of at least one wheel. The vehicle also includes a controller configured to select wheel speed information from among the wheel speed information of the at least one wheel based on a driving state of the vehicle and configured to determine a speed of the vehicle based on the selected wheel speed information.

The present disclosure provides a vehicle control device capable of preventing an erroneous operation of an advanced driving assistance system while ensuring safety. A vehicle control device 110 includes a course prediction unit F1, a collision prediction unit F3, a vehicle control unit F5, and a control intervention adjustment unit F4. The course prediction unit F1 predicts a turning course of a vehicle as a steady circular turning course based on a steering angle ?. The collision prediction unit F3 calculates a collision margin time (TTC) between a target detected by an external environment sensor of the vehicle and the vehicle that travels on the steady circular turning course, and calculates a predicted collision lateral position CLL of the target with respect to a vehicle width center position of the vehicle. The vehicle control unit F5 performs collision avoidance control of a vehicle 100 when the collision margin time is shorter than a control intervention threshold value TH. The control intervention adjustment unit F4 adjusts the control intervention threshold value TH. The control intervention adjustment unit F4 reduces the control intervention threshold value when the predicted collision lateral position CLL with respect to the vehicle width center position of the vehicle is in a direction opposite to a direction of a steering angular speed which is a time change rate of the steering angle ?.

Methods and devices for operating a Self-Driving Car (SDC) are disclosed. The method includes generating a first graph-structure having nodes and edges, ranking the edges based on a priority logic into a ranked list of edges, and generating a second graph-structure (i) by iteratively generating attributes for respective ones from the ranked list of edges beginning with a highest priority edge in the ranked list of edges and (ii) until a pre-determined limit is met. The method also includes causing operation of the SDC on the road segment using the second graph-structure.

A signal processing module is provided. The signal processing module includes a function weight table that stores weights for each first sensor for an autonomous driving mode and an ADAS driving mode and selects and outputs only the weight for each first sensor, a first weight applying device that generates a function weight application signal by applying the weight for each first sensor to sensing information of sensors for sensing an object, a road environment determining device that determines a road environment based on the sensing information of the sensors for sensing the object, a road environment weight table that stores weights for each second sensor for a road environment and selects and outputs an weight for each second sensor, and a second weight applying device that outputs a dataset by applying the weight for each second sensor to the function weight application signal.

A lane keeping control apparatus, a vehicle system including the same includes a processor that is configured to calculate a target lateral movement distance based on lane information during lane keeping control. The processor corrects the target lateral movement distance by correcting a heading angle of a vehicle and an offset from a target path before the vehicle reaches the target path and a storage stores data and algorithms driven by the processor.

A system for tuning a trajectory tracking controller for a vehicle includes a trajectory planner configured to generate the planned trajectory and to output one or more planned trajectory components representative of the planned trajectory, a model predictive controller including an internal model and an optimizer, and a tuning neural network configured to receive the one or more planned trajectory components and one or more measured trajectory components and to produce weights for a cost function. The internal model is configured to receive a predicted control input from the optimizer and the one or more measured trajectory components and to produce a predicted output. The optimizer utilizes a cost function and is configured to receive the weights for the cost function and a predicted error and to produce the predicted control input, wherein the predicted error is a selected one of the planned trajectory components minus the predicted output.

A vehicle control apparatus having a meter display ECU and a vehicle speed control ECU. The meter display ECU acquires the GNSS speed of the host vehicle, calculates the gain error of the detected vehicle speed, and calculates a meter display speed based on the detected vehicle speed, the gain error, and the offset value. The meter display ECU calculates an offset value ensure that the meter display speed equals or exceeds both the detected vehicle speed and the GNSS speed, based on past gain error calculations. The vehicle speed control ECU calculates the meter display speed using the gain error and offset value acquired from the meter display ECU and the detected vehicle speed acquired from the vehicle speed sensor.

A control device includes control circuits of a plurality of systems. When a transition condition is established, the control circuits of the systems make transition of a driving mode from a first driving mode to a second driving mode, and when a return condition is established in a state in which the driving mode has transitioned to the second driving mode, the control circuits of the systems make transition of the driving mode from the second driving mode to the first driving mode while gradually changing the current command values of the control circuits' own systems toward the current command values before the adjustment.