Disclosed are an apparatus, a system and a method for managing drowsy driving. The apparatus for managing drowsy driving includes a communication device that transmits vehicle information to a server and receives a drowsiness probability of a driver from the server, and a controller that generates a command when the drowsiness probability of the driver exceeds a reference value and determines whether the driver is in a drowsy state based on input information of the driver responding to the command.

An automatic driving control plan creation unit creates a plan of an automatic driving section in which a subject vehicle is automatically driven, and a plan of a driving switching preparation section for switching the subject vehicle from automatic driving to manual driving. For each point in the driving switching preparation section, a driving load calculation unit calculates a driving load applied to a driver when the driver manually drives the subject vehicle. A driving switching permission determination unit permits switching of the subject vehicle from automatic driving to manual driving at a point where the driving load is smaller than a predetermined threshold value, and does not permit switching of the subject vehicle from automatic driving to manual driving at a point where the driving load is equal to or larger than the threshold value. A permission standard relaxation unit makes the driving load difficult to exceed the threshold value.

Systems, methods, and devices are disclosed for predicting behaviors of objects (vehicles, bicycles, pedestrians, etc.) at a location. A model descriptive of a possible object behavior can be received by an autonomous vehicle, where the model provides conditional predictions about a future behavior of an object based on a position of the object in a lane. The autonomous vehicle can detect the position of a specific object in the lane, and the model can then be applied to determine probabilities of a future behavior of the specific object.

In one embodiment, a method of transitioning from autonomous driving (AD) to manual driving (MD) mode for an autonomous driving vehicle (ADV) is disclosed. The method includes determining whether AD-to-MD transition is allowed in a current location of the ADV. The method further includes determining whether a current driving scenario is safe for the AD-to-MD transition. The method further includes in response to determining that the AD-to-MD transition is allowed in the current location and the current driving scenario is safe for the AD-to-MD transition, enabling the AD-to-MD transition. The method further includes determining whether there is a request for the AD-to-MD transition. The method further includes in response to determining there is the request, computing a current vehicle motion trajectory of the ADV. The method further includes comparing the current vehicle motion trajectory with a motion trajectory derived from inputs of a driver of the ADV. The method further includes determining whether to confirm the AD-to-MD transition based on the comparison.

A vehicle control device of the embodiments includes a recognizer, and a driving controller, in which the driving controller includes a lane change controller that changes lanes from a host vehicle traveling lane in which a host vehicle travels to an adjacent lane adjacent to the host vehicle traveling lane, the lane change controller includes a first operation of changing lanes according to a request from an occupant of the host vehicle and a second operation of changing lanes on the basis of a result of recognition, and, when changing lanes is stopped at the time of execution of the first operation, causes the host vehicle to continue traveling in the host vehicle traveling lane and prohibits a lane change by the second operation within a first period including a distance or time from a first time at which changing lanes is stopped.

A vehicle, operating system of a vehicle and a method of operating a vehicle is disclosed. A local electronic control unit is operated at the vehicle in order to control the vehicle. A backup electronic control unit is operated at a remote computing platform for control of the vehicle. A control of the vehicle is transferred from the local electronic control unit to the backup electronic control unit upon occurrence of a fault at the local electronic control unit.

An autonomous driving controller includes a plurality of parallel processors operating on common input data received from the plurality of autonomous driving sensors. Each of the plurality of parallel processors includes communication circuitry, a general processor, a security processor subsystem (SCS), and a safety subsystem (SMS). The communication circuitry supports communications between the plurality of parallel processors, including inter-processor communications between the general processors of the plurality of parallel processors, communications between the SCSs of the plurality of parallel processors using SCS cryptography, and communications between the SMSs of the plurality of parallel processors using SMS cryptography, the SMS cryptography differing from the SCS cryptography. The SCS and/or the SMS may each include dedicated hardware and/or memory to support the communications.

An automotive vehicle includes at least one sensor configured to detect features in a region proximate the exterior of the vehicle, at least one actuator configured to control vehicle steering, propulsion, shifting, or braking, and an automated driving system selectively operable in a nominal mode and in a degraded mode. The automated driving system is configured to generate an actuator control signal for the at least one actuator in response to sensor signals from the at least one sensor. The automated driving system includes a computational accelerator processor. The vehicle further includes a monitor processor in communication with the automated driving system. The monitor processor is configured to provide a test input for processing by the computational accelerator processor, receive a test output from the computational accelerator processor, and in response to the test output not satisfying a validation criterion, control the automated driving system in the degraded mode.

In one embodiment, a method of generating control effort to control an autonomous driving vehicle (ADV) includes determining a gear position (forward or reverse) in which the ADV is driving and selecting a driving model and a predictive model based upon the gear position. In a forward gear, the driving model is a dynamic model, such as a bicycle model, and the predictive model is a look-ahead model. In a reverse gear, the driving model is a hybrid dynamic and kinematic model and the predictive model is a look-back model. A current and predicted lateral error and heading error are determined using the driving model and predictive model, respectively A linear quadratic regulator (LQR) uses the current and predicted lateral error and heading errors, to determine a first control effort, and an augmented control logic determines a second, additional, control effort, to determine a final control effort that is output to a control module of the ADV to drive the ADV.

Control commands in a system and method for autonomously driving a vehicle or partially autonomously driving a vehicle are generated on the basis of an environment representation, which is generated from sensor signal of a sensing means. The environment representation where ambiguous objects are identified is such that information obtained from the driver of the vehicle are added. The system generates an information request. Additional information is extracted and accumulated in the environment representation map. Traffic is then determined and suitable control signals for the vehicles are generated. In case no ambiguous objects are included in the environment representation but the system is not capable of deciding on traffic, the driver is asked to disambiguate the situation or to instruct on dealing with the traffic.