A vehicle system recognizes a situation of surroundings of a host vehicle, automatically controls at least steering of the host vehicle on the basis of the recognized situation of the surroundings to cause the host vehicle to perform lane change, acquires information on a route from a navigation device guiding the route along which the host vehicle travels, and suppresses control for assisting the steering on the basis of an event regarding the route when the event occurs in the navigation device.

In a vehicle remote instruction system, a remote commander issues a remote instruction relating to travel of an autonomous driving vehicle based on sensor information from an external sensor that detects an external environment of the autonomous driving vehicle. The vehicle remote instruction system sets a range of information to be transmitted to the remote commander among the sensor information detected by the external sensor, as a limited information range, based on the external situation or an external situation obtained based on map information and a trajectory of the autonomous driving vehicle.

A vehicle control device includes a plurality of IC units, while maintaining the operational reliability. The vehicle control device includes an IC unit for performing image processing on outputs from cameras; an IC unit for performing recognition processing of an external environment of the vehicle; and an IC unit for performing judgment processing for cruise control of the vehicle. A control flow is provided so as to allow the IC unit to transmit a control signal to the IC units and. The control flow is provided separately from a data flow configured to transmit the output from the cameras, the image data, and the external environment data.

An autonomous vehicle includes sensor subsystem(s) that output a sensor signal. A perception subsystem (i) detects an agent in a vicinity of the autonomous vehicle and (ii) generates a motion signal that describes at least one of a past motion or a present motion of the agent. An intention prediction subsystem processes the sensor signal to generate an intention signal that describes at least one intended action of the agent. A behavior prediction subsystem processes the motion signal and the intention signal to generate a behavior prediction signal that describes at least one predicted behavior of the agent. A planner subsystem processes the behavior prediction signal to plan a driving decision for the autonomous vehicle.

A vehicle includes an engine, a primary axle drivably connected to the engine, and a secondary axle drivably connected to the engine by a clutch. A controller is programmed to, in response to map data indicating that the vehicle is off-road, enable an off-road transfer torque control mode in which temperatures of the clutch are compared to a lower threshold. The controller is further programmed to, in response to the temperature of the clutch exceeding the lower threshold, display a message to a driver indicative of rising temperatures of the clutch, and, in response to a temperature of the clutch exceeding an upper limit, enable a power limiting mode in which torque of the engine is limited to a predefined value.

A system in a vehicle includes an image sensor to obtain images in an image sensor coordinate system and a depth sensor to obtain point clouds in a depth sensor coordinate system. Processing circuitry implements a neural network to determine a validation state of a transformation matrix that transforms the point clouds in the depth sensor coordinate system to transformed point clouds in the image sensor coordinate system. The transformation matrix includes rotation parameters and translation parameters.

A computer device and system for controlling an autonomous vehicle are provided. The computer device comprises a memory and a processor, the computer device configured to be fitted to a vehicle and to communicate with a camera or sensor, the processor being configured to: pre-process an original image from the camera or sensor data from the sensor to produce an input image; present the input image to a neural network stored in the memory; wherein the neural network is trained to classify a feature in an image presented to it, the neural network having an input layer, a hidden layer and an output layer, the output layer including three outputs: a first feedback output for selecting pixels from the input image to input at the input layer at each iteration of the neural network; a second feedback output for selecting a colour channel of the selected pixels to input at the input layer at each iteration; and a third output for outputting an output value indicative of a classification result from the neural network; the processor further configured to obtain the output value from the neural network; and post-process the output value from the neural network to identify a feature of the environment of a vehicle.

A control system of a vehicle, the vehicle including a detection unit for detecting external information related to an outside of surroundings of the vehicle, the external information being used to control a driven state of the vehicle is provided. The control system performs a method comprising: obtaining map information of surroundings of a route on which the vehicle travels based on position information of the vehicle, and specifying, from among pieces of detection range information corresponding to the map information, detection range information corresponding to the detection unit; and controlling the driven state of the vehicle based on the specified detection range information and the external information.

Systems and methods are directed to an autonomy computing system for an autonomous vehicle. The autonomy computing system can include functional circuits associated with a first compute function of the autonomous vehicle. Each of the functional circuits can be configured to obtain sensor data that describes one or more aspects of an environment external to the autonomous vehicle at a current time. Each of the functional circuits can be configured to generate, over a time period and based on the sensor data, a respective output according to the specified order. The autonomy computing system can include monitoring circuits configured to evaluate an output consistency of the respective outputs, and in response to detecting an output inconsistency between two or more of the respective outputs, generate data indicative of a detected anomaly associated with the first autonomous compute function.

Aspects of the present invention relate to a control system for a vehicle. The control system comprises one or more controllers, and is configured to select a relationship between torque and speed based, at least in part, on a determined terrain mode. The control system is further configured to control a drive torque of the vehicle in accordance with the selected relationship between torque and speed when the vehicle is operating in a creep control mode. The vehicle may be a hybrid or electric vehicle and the terrain mode may be determined from a Terrain Response™ switch input or automatically determined.