A distributed safety lockout system for a vehicle, such as a recreational vehicle (RV), that includes a plurality of networked devices communicatively coupled via a communications network, and that is configured to implement a method for controlling an electromechanical operation of an electromechanical device based on a safety lockout condition determined through use of a consensus protocol in which a safety lockout status is agreed upon by the networked devices. The safety lockout status may be escalated by a safety lockout condition detection device that detects a safety lockout condition, such as movement of the RV. The safety lockout status may be de-escalated through a network contention mode of the consensus protocol in which each of the networked devices agree to de-escalate the safety lockout status.

Handling input data errors in an autonomous vehicle using predictive inputs, including: determining an error in input data for a model of a plurality of models of an automation system of the autonomous vehicle; generating predicted input data for the model; and generating, based on the predicted input data, output data for the model.

A computer-implemented method is provided for redundancy reduction for driving test scenarios. The method includes receiving an original test set of driving scenarios and a driving model which simulates a vehicle behavior under a driving scenario inputted to the driving model. The method includes, for each driving scenario of the original test set, obtaining vehicle dynamics timeseries data as an output of the driving model. The method includes determining similar driving scenarios by comparing driving model outputs. The method additionally includes creating a new test set of driving scenarios by discarding duplicated ones of the similar driving scenarios from the original test set.

A vehicle management ECU comprises a diagnosis section for diagnosing electronic control units that are to be diagnosed according to a diagnosis scenario defined by a diagnosis application, and a diagnosis scenario determination section for determining whether a diagnosis scenario used for diagnosis by the diagnosis section was appropriate. If the diagnosis scenario determination section specifies that the diagnosis scenario used for the present diagnosis has not been appropriate, the diagnosis section carries out diagnosis according to a diagnosis scenario defined by a new diagnosis application different from the present diagnosis application.

A method for operating an environment sensor system of a vehicle, in particular of an autonomous motor vehicle, and an environment sensor system. The method includes the steps: producing first image data from a first image of a vehicle environment with the aid of a first sensor, performing a setpoint-actual comparison of the first image data with the second image data, and recognizing an operability of the environment sensor system based on the setpoint-actual comparison, the second image data including standard image data and/or image information from a second image of the vehicle environment, and the operability being recognized with the aid of a first artificial intelligence.

A drive control system for a hybrid vehicle configured to prevent backward coasting on an uphill grade even when an engine torque cannot be delivered to drive wheels. When the hybrid vehicle is propelled by delivering engine torque to rear wheels on an uphill grade, the drive control system determines an occurrence of a failure in which the torque cannot be delivered from the engine to the rear wheels. If the occurrence of the failure is determined, the drive control system executes a hill hold control to deliver torque to establish a required drive force from a motor to front wheels while disengaging an engagement device.

In a preferred example embodiment of the present disclosure, a multi-agent control system includes: a malfunctioning-agent detector configured to detect a malfunctioning agent among a plurality of agents based on a malfunction signal received from each of the plurality of agents; and a multi-agent controller configured to control a neighboring agent around the malfunctioning agent to transmit a correction control signal to the malfunctioning agent such that the plurality of agents operate in a platoon.

A fault diagnostic device 80 includes an input unit 81 and a generation unit 82. The input unit 81 receives input of observation data of a vehicle operating at a predetermined speed. The generation unit 82 extracts time series features of the observation data as features indicating a normal condition, and generates a feature master indicating the normal condition of the vehicle based on the extracted features.

According to an embodiment, provided are a method and an apparatus for monitoring an unmanned ground vehicle (UGV), for detecting a failure of a UGV actuator in consideration of terrain information. Accordingly, the accuracy of detecting a failure of the UGV is improved.

A driving assistance system includes a front camera module (FCM). The system, responsive to processing captured image data from the FCM, generates FCM control signals. The system includes a plurality of vehicle sensors capturing sensor data and an advanced driving-assistance system (ADAS) controller. The ADAS controller, responsive to processing the sensor data, generates ADAS control signals. The ADAS controller generates an ADAS status signal indicating a reliability of the generated ADAS control signals. With the ADAS status signal indicating the reliability of the generated ADAS control signals is at or above a threshold ADAS reliability level, the system controls the vehicle using the ADAS control signal and, responsive to determining that the ADAS status signal indicates that the reliability of the generated ADAS control signals is below the threshold, switches from controlling the vehicle based on the ADAS control signals to controlling the vehicle based on the FCM control signals.