According to one aspect, systems and techniques for adaptive trust calibration may include usage of a driving style predictor, including a memory and a processor. The memory may store one or more instructions and the processor may execute one or more of the instructions stored on the memory to perform one or more acts, actions, or steps, such as receiving a current automated vehicle (AV) driving style, receiving an indication of an event and an associated event type, receiving an indication of a driver takeover, concatenating the current AV driving style and one or more of the event type or the driver takeover to generate an input, and passing the input through a neural network, which may include a gated recurrent unit (GRU), to generate a preference change associated with the AV driving style.

A method for switching between autonomous drive and manual drive of a transportation vehicle, wherein the driver of the transportation vehicle is observed by at least one interior camera relative to a handover intention and/or takeover intention. In response to a handover intention being detected, the steering wheel is moved away from the driver and the drive is switched to autonomous drive. In response to a takeover intention being detected, the steering wheel is moved toward the hands of the driver and the drive is switched to manual drive.

An integrated operation apparatus for an autonomous vehicle, includes a joystick lever used to accelerate, decelerate, and steer the vehicle, an acceleration and deceleration actuator module and a steering actuator module each include a position sensor module to generate signals related to the acceleration, deceleration, and steering of the vehicle when the joystick lever module is operated, and a torque sensor module is used to feedback control a steering motor during steering operation, realizing more precise steering sensation.

Disclosed is a system for vehicle control. The system analyzes a state of each controller of multiple controllers inside a vehicle based on information collected from the controllers, learns the state of the each controller, based on a state analysis result obtained by analyzing the state of the each controller, and determines an abnormal state for at least one of the controllers, based on the state analysis result for the each controller and a learning result obtained by learning the state of the each controller. The system also includes a vehicle controller that manages vehicle control rights, based on the abnormal state for the at least one of the one or more controllers.