A vehicle may be operated in a driving mode with autonomous lateral control and in a driving mode with manual lateral control, where the vehicle has a steering lever with a steering lever range, and where the steering lever range represents a circular space which is delimited about 360° by a rotational movement of the steering lever about a steering lever axis of rotation. A method associated with the vehicle may include one or more of the following steps: operating the vehicle in a driving mode with autonomous lateral control; detecting an intrusion in the steering lever range and/or a breaching of a predefined distance from the steering lever range by the vehicle occupant or another object with a sensor unit without the vehicle occupant coming into contact with the steering lever; and signaling the intrusion and/or the breaching of the distance to the vehicle occupant.

Systems and techniques are described for event detection. A described system includes a vehicle body containing body regions, sensors, processor, and memory. The sensors can include at least one sensor positioned about at least one body region of the body regions. The at least one body region can be associated with a region event type. The memory can store instructions thereon that, when executed by the processor, cause the processor to perform operations which can include obtaining data associated with at least one sensor measurement from the at least one sensor, determining that the at least one sensor measurement is associated with the region event type, and determining that an event associated with the region event type occurred based on determining that the at least one sensor measurement is associated with the region event type and that the at least one body region is associated with the region event type.

A robotic cart platform with a navigation and movement system that integrates into a conventional utility cart to provide both manual and autonomous modes of operation. The platform includes a drive unit with drive wheels replacing the front wheels of the cart. The drive unit has motors, encoders, a processor and a microcontroller. The system has a work environment mapping sensor and a cabled array of proximity and weight sensors, lights, control panel, battery and on/off, “GO” and emergency stop buttons secured throughout the cart. The encoders obtain drive shaft rotation data that the microcontroller periodically sends to the processor. When in autonomous mode, the system provides navigation, movement and location tracking with or without wireless connection to a server. Stored destinations are set using its location tracking to autonomously navigate the cart. When in manual mode, battery power is off, and back-up power is supplied to the encoders and microcontroller, which continue to obtain shaft rotation data. When in autonomous mode, the shaft rotation data obtained during manual mode is used to determine the present cart location.

A road surface determination apparatus includes an acceleration detector and a road surface determination unit. The acceleration detector is configured to detect an acceleration of a vehicle body or a vibration transmission member configured to transmit vibration from a tire to the vehicle body. The road surface determination unit is configured to determine a condition of a surface of a road, using a determination value obtained by extracting a component of a predetermined frequency band from the acceleration detected by the acceleration detector and integrating the component.

A system for navigating a host vehicle may include memory and at least one processor configured to receive a plurality of images acquired by a camera onboard the host vehicle; generate, based on analysis of the plurality of images, a road geometry model for a segment of road forward of the host vehicle; determine, based on analysis of at least one of the plurality of images, one or more indicators of an orientation of the host vehicle; and generate, based on the one or more indicators of orientation of the host vehicle and the road geometry model for the segment of road forward of the host vehicle, one or more output signals configured to cause a change in a pointing direction of a movable headlight onboard the host vehicle.

A system for navigating a host vehicle may include memory and at least one processor configured to receive a plurality of images acquired by a camera onboard the host vehicle; generate, based on analysis of the plurality of images, a road geometry model for a segment of road forward of the host vehicle; determine, based on analysis of at least one of the plurality of images, one or more indicators of an orientation of the host vehicle; and generate, based on the one or more indicators of orientation of the host vehicle and the road geometry model for the segment of road forward of the host vehicle, one or more output signals configured to cause a change in a pointing direction of a movable headlight onboard the host vehicle.

An autonomous vehicle configured for active sensing may also be configured to weigh expected information gains from active-sensing actions against risk costs associated with the active-sensing actions. An example method involves: (a) receiving information from one or more sensors of an autonomous vehicle, (b) determining a risk-cost framework that indicates risk costs across a range of degrees to which an active-sensing action can be performed, wherein the active-sensing action comprises an action that is performable by the autonomous vehicle to potentially improve the information upon which at least one of the control processes for the autonomous vehicle is based, (c) determining an information-improvement expectation framework across the range of degrees to which the active-sensing action can be performed, and (d) applying the risk-cost framework and the information-improvement expectation framework to determine a degree to which the active-sensing action should be performed.

An electronic system for controlling vibrations and/or inertial forces occurring at a plurality of areas of interest within an operating vehicle, the electronic device comprising circuitry configured to: receive input data comprising sensor data from one or more environment sensors (12) and/or one or more internal sensors (14); convert, by means of a machine learning system (18), the input data into actuator settings; and transmit the actuator settings to one or more actuators (20) to control vibrations and/or inertial forces occurring at each of the plurality of areas of interest within the vehicle.

Among other things, techniques are described for receiving, from at least one sensor of a vehicle, a sensor measurement indicative of at least one of a sound or a vibration associated with a road element; identifying the road element based on a pattern in the sensor measurement; determining, based on the road element, a vehicle behavior for the vehicle; and controlling the vehicle to operate according to vehicle behavior.

A vehicle designed for an automated driving operation may have a ventilation device for ventilating a vehicle interior. A control unit and at least one pressure sensor assembly may be provided. The control unit may be designed to activate the ventilation device and to determine a setpoint pressure value of the air pressure within the vehicle interior while the vehicle interior is closed, to infer damage to a vehicle shell surrounding the vehicle interior. Further, the control unit may block the automated driving operation if it is inferred that there is damage to the vehicle shell. Methods for detecting damage to the vehicle and for operating a vehicle are also provided. A device for carrying out a method for detecting damage to a vehicle and/or for carrying out a method for operating a vehicle to a computer program and to a machine-readable storage medium are also provided.