The present teaching relates to method, system, and medium, for operating a vehicle. The method includes the steps of receiving Real-time data related to the vehicle are received. A current mode of operation of the vehicle is determined. A first risk associated with the current mode of operation of the vehicle is evaluated based on the real-time data in accordance with a risk model. If the first risk satisfies a first criterion, a second risk associated with switching the current mode to a different mode of operation of the vehicle is determined. The vehicle is switched from the current mode to the different mode if the second risk satisfies a second criterion.

A vehicle control device includes a recognizer that is configured to recognize a peripheral situation of a vehicle and a driving controller that is configured to execute automated driving for controlling a speed and steering of the vehicle on the basis of a recognition result of the recognizer. The recognizer is configured to recognize a real-time environment including a situation of a nearby vehicle or a situation of a nearby road on which the vehicle travels, and the driving controller is configured to shunt the vehicle to a shunt location to suspend the automated driving in a case where the real-time environment corresponds to a predetermined condition, and resume the automated driving in a case where the predetermined condition is resolved after the shunt.

A vehicle control apparatus including circuitry configured to set a driving performance request in the given environments; determine whether driving performance of each of plural driving functions of a driver satisfies the driving performance request; and identify the driver's driving function that does not satisfy the driving performance request. On condition that an insufficient function is identified or the driving performance request is not satisfied, to realize the driving performance request, the circuitry is configured to perform at least one of controlling the vehicle such that the vehicle performs the identified insufficient function instead and providing the driver with an appropriate information so that the identified insufficient function close to a level of the driving performance request.

A vehicle control apparatus operates in various modes, e.g., a normal mode in which plural travel functions of a vehicle are controlled by plural driving functions of a driver; an automatic mode in which all driving functions are performed by the vehicle; an optimization mode in which, when a temporary reduction of driving performance of any of the plural driving functions of the driver is detected, physical stimulation and/or information that makes the driver recognize the temporary reduction of the driving performance is provided to the driver; and an assistance mode in which, when a chronic reduction of the driving performance of any of the plural driving functions of the driver is detected, information on assistance with execution of the driving performance is provided to the driver.

There is provided an information processing apparatus including an eyeball behavior analysis unit (300) that analyzes an eyeball behavior of a driver who drives a moving object, in which the eyeball behavior analysis unit dynamically switches an analysis mode according to a driving mode of the moving object.

A vehicle risk analysis system, includes: a historical database, storing the sensing data and accident records acquired from multiple sampled vehicles into big data; a learning unit, connected to the historical database for analyzing the relationship between accident records and driving scenarios, to generate a risk classification model, which can further predict the risk of accidents for a vehicle in-use; and a risk profiling unit, receiving the risk classification model from the learning unit and acquiring the sensing data from the vehicle in-use, to generate a risk profile for the vehicle in-use under different driving modes. The risk profile reports the expected risks of accidents under different driving modes by using the occurrence rates for different driving scenarios and the expected losses of money caused by accidents, wherein each of the driving scenarios can be determined by a combination of the sensing data. The system can suggest an optimal driving mode for the vehicle in-use by using the risk profile, which can be further used to price the insurance premiums under different driving modes.

A system for a lane keeping feature of a vehicle is provided. The lane keeping feature has a predefined safety requirement criterion for keeping the vehicle within bounds while the lane keeping feature is active. The system comprises a road estimation module and a trajectory planning module. The road estimation module is configured to receive sensor data comprising information about a surrounding environment of the vehicle, and to determine a drivable area based on the sensor data. The drivable area comprises a left boundary and a right boundary extending along a direction of travel of the vehicle, wherein each boundary comprises a plurality of points distributed along each boundary, each point being associated with a confidence level. The trajectory planning module is configured to receive the determined drivable area, and to determine a nominal trajectory for the vehicle based on the received drivable area.

The disclosure describes various embodiments for monitoring safety of an autonomous driving vehicle (ADV). In one embodiment, a method includes the operations of receiving, by a vehicle controller, one or more error message from a patrol module, the one or more error messages generated by an autonomous driving system of the ADV operating in an autonomous mode, the patrol module monitoring the autonomous driving system; evaluating a status of the autonomous driving system based on the one or more error messages; and keeping the ADV in the autonomous mode or switching it to a manual mode based on the status of the autonomous driving system.

A method for controlling autonomous driving in an autonomous vehicle includes: determining whether a human driver is in a forward gaze state under an autonomous driving mode, setting a first steering wheel torque threshold and a first torque holding time, based on a result of determining whether the human driver is in the forward gaze state, determining whether human driver intervention has occurred, based on the first steering wheel torque threshold and the first torque holding time, and switching the autonomous driving mode to a manual driving mode when the human driver intervention has occurred.

A system and method for providing adaptive trust calibration in driving automation that include receiving image data of a vehicle and vehicle automation data associated with automated of driving of the vehicle. The system and method also include analyzing the image data and vehicle automation data and determining an eye gaze direction of a driver of the vehicle and a driver reliance upon automation of the vehicle and processing a Markov decision process model based on the eye gaze direction and the driver reliance to model effects of human trust and workload on observable variables to determine a control policy to provide an optimal level of automation transparency. The system and method further include controlling autonomous transparency of at least one driving function of the vehicle based on the control policy.