A vehicle having a control element for the speed, acceleration or direction of the vehicle, multiple identical or redundant computing devices (e.g., each implemented as a system on chip (SoC)) to separately generate driving commands in parallel during autonomous driving of the vehicle, and a command controller coupled between the control element and the computing devices. The commands may have one or more matching groups, where commands within each respective group agree with each other and thus vote for a candidate command representing the group. The computing device outputs a candidate command that represents the largest group for execution by the control element.

A target trajectory generation device generates and outputs target trajectories each including a target position and a target speed of a vehicle. A first target trajectory is intended to perform at least one of steering, acceleration, and deceleration of the vehicle. A second target trajectory is intended to decelerate and stop the vehicle. When a malfunctioning device does not exist, a vehicle traveling control device executes vehicle traveling control based on the first target trajectory. When the malfunctioning device exists, the vehicle traveling control device stops the vehicle by executing the vehicle traveling control based on the second target trajectory output before the malfunction occurs, or based on the second target trajectory output from the target trajectory generation device other than the malfunctioning device.

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 method includes identifying sensor data associated with corresponding distal ends of one or more axles of an autonomous vehicle (AV). The method further includes determining, based on the sensor data, mass distribution data of the AV. The mass distribution data is associated with a first load proximate a first distal end of a first axle of the AV and a second load proximate a second distal end of the first axle of the AV. The method further includes causing, based on the mass distribution data, performance of a corrective action associated with the AV.

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 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.

An autonomous vehicle, a control system for remotely controlling the same, and a method thereof may include an autonomous driving control apparatus including a processor for determining whether remote control of the autonomous vehicle is required according to at least one of occurrence of failure of the vehicle during autonomous driving thereof, occurrence of an accident, a region where the autonomous driving is not possible, reliability of positioning data of the vehicle, a stopping time of the vehicle, or a response point of a hand signal, and for requesting the remote control to a control system when the remote control of the vehicle is required.