A vehicle control system includes: multiple control units which controls operation of a vehicle including an internal combustion engine, a first electric motor connected to the internal combustion engine, and a second electric motor; and a network connected to the control units such that the control units perform communication with each other. The control units include a first control unit which controls the internal combustion engine, a second control unit which controls the first motor, and a third control unit which controls the second motor, and each detect abnormality in communication via the network among the control units. Upon detection of abnormality in communication between the second control unit and the other control units via the network, the first control unit stops operation of the internal combustion engine, and the third control unit performs control such that the second motor outputs power for the vehicle to travel.

The subject technology is related to autonomous vehicles (AV) and, in particular, to an autonomous driver system controller (ADSC) that is fixed to the AV. The AV comprises an electronic drivetrain configured to move the AV; and an autonomous driver system controller (ADSC) fixed to an interior surface of the AV and configured to control the electronic drivetrain with a processor connected to a plurality of memory integrated circuits (IC) that are fixed to a printed circuit board (PCB). The plurality of memory ICs are mounted on each side of the PCB using a ball grid array (BGA) with a column of pins in the BGA of a top-surface memory IC is longitudinally aligned with a corresponding column of pins in the BGA of a bottom-surface memory IC.

Operating a host vehicle is described as including identifying remote vehicle information indicating a geospatial state and a kinematic state for a remote vehicle and identifying host vehicle information indicating a geospatial state and a kinematic state for the host vehicle. For a sequence of sampling points, a converging time to a converging location within a transportation network is determined based on the remote vehicle information and the host vehicle information. Operation of the host vehicle is modified to a modified operation responsive the converging time, having been above a first threshold, failing below the first threshold, and the modified operation of the host vehicle is maintained until the converging time remains above a second threshold higher than the first threshold for a defined number of contiguous sampling points of the sequence. A method, vehicle, and apparatus are described.

A vehicle system includes: a reprogramming slave device that is an electronic control unit (hereinafter, referred to as ECU) to be a target of updating an update file of a program stored among a plurality of the ECUs; a reprogramming master device that transmits the update file to the reprogramming slave device in response to a request from a terminal operable by a vehicle user to control updating of the program stored in the reprogramming slave device; and a determination unit that determines traveling propriety of a vehicle when the update file is rewritten in the reprogramming slave device. The vehicle device functions as the reprogramming master device, and includes: an obtaining unit that obtains the traveling propriety determined by the determination unit; and a notification command unit that commands a notification medium to notify information of the traveling propriety obtained by the obtaining unit.

A computer-implemented method for switching between a first controller and a second controller for a vehicle includes in a first operation stage, controlling an operational parameter of at least one MSD of the vehicle using the first controller, thereby providing a first tire force at at least one tire associated with the MSD, determining a second tire force for the at least one tire based on a vehicle model associated with the second controller, in a second operation stage, controlling the operational parameter of the at least one MSD using a third controller, comprising determining an intermediate value for the operational parameter based on the first and second tire forces, and in a third operation stage, controlling the operational parameter of the at least one MSD using the second controller, thereby providing the second tire force at the at least one tire.

A control device is configured to generate a control command for controlling a movable body, using measurement results of a plurality of distance measurement devices. The control device includes a point cloud combining unit configured to create combined point cloud data by combining two or more pieces of three-dimensional point cloud data that are obtained by two or more distance measurement devices of the plurality of distance measurement devices. The control device is configured to execute a first estimation process of estimating at least one of the position and orientation of the movable body, using the combined point cloud data.

Apparatus for controlling a land vehicle which is self-driving or partially self-driving, comprising a coarse tuning assembly (1, 2, 3) and a fine tuning assembly (4), the coarse tuning assembly (1, 2, 3) comprising: (a) a sensor interface (1) which measures kinematic parameters including speed and braking, (b) fuzzy descriptions which model guidance, navigation and control of the vehicle, and which include: (i) driver behavior and driving dynamics, (ii) uncertainties due to weather, road conditions and traffic, and (iii) input faults including mechanical and electrical parts, and (c) an adaptive fuzzy logic controller (3), and the fine tuning assembly (4) comprising: (a) inputs from the coarse tuning assembly (1, 2, 3), (b) precognition horizons determining how many future samples of input sensor information are required for an optimum control sequence, (c) a linearized multi-input multi-output regression model extracted from the adaptive fuzzy logic controller (3), and (d) a non-linear dynamic linearized regression controller (4a).

A computer system for controlling at least one motion actuator in an autonomous or semi-autonomous vehicle, the computer system comprising processing circuitry implementing a feedback controller, which is configured to sense an actual motion state of the vehicle and determine a machine-level instruction to the motion actuator for approaching or maintaining a setpoint motion state, and a reinforcement-learning agent, which is trained to perform decision-making regarding the setpoint motion state. The decisions by the reinforcement-learning agent are applied as the setpoint motion state of the feedback controller, and the machine-level instruction from the feedback controller is applied to the motion actuator.

A method is provided for selecting a plurality of program functions for providing repeatedly implemented functions, e.g., in a vehicle, ship or in an aircraft. The method includes determining a first total performance value based on recorded first single performance values and recorded first dependencies, determining a first total performance value based on determined second single performance values and recorded second dependencies, determining a cluster performance from the first total performance value and from the second total performance value, and the cluster performance value or at least one value determined from the cluster performance value is used for selecting the program functions or of other program functions for providing the repeatedly implemented functions.

An example operation includes one or more of determining, by a server, an event associated with a transport, receiving, by the server, atypical data related to the transport from a plurality of devices over various times prior to the event, analyzing, by the server, the atypical data, forming, by the server, a consensus based on the analyzed atypical data to determine a severity of the event, and determining, by the server, an action to take based on the severity.