An approach to arrange sensors needed for automated driving, especially where semitrailer trucks are operating in an autonomous convoy with one automated or semi-automated truck following another. The sensors are fitted to a location adjacent to or within the exterior rearview mirrors, on each of the left- and right-hand side of the tractor. The sensors provide overlapping fields of view looking forward of the vehicle and to both the left and right hand sides at the same time.

An approach to arrange sensors needed for automated driving, especially where semitrailer trucks are operating in an autonomous convoy with one automated or semi-automated truck following another. The sensors are fitted to a location adjacent to or within the exterior rearview mirrors, on each of the left- and right-hand side of the tractor. The sensors provide overlapping fields of view looking forward of the vehicle and to both the left and right hand sides at the same time.

A method for operating an advanced driver assistance (ADAS) system of a motor vehicle includes a vehicle controller receiving, from side and end cameras mounted to the vehicle, camera signals indicative of real-time images of outboard-facing side and end views of the vehicle. The controller determines a region of interest (ROI) inset within each end/side view within which is expected foreign objects and/or hazards. These ROIs are analyzed to detect if a foreign object/hazard is present in the vehicle's end and/or side views. Responsive to detecting the foreign object/hazard, movement of the foreign object/hazard is tracked to determine if the foreign object/hazard moves towards or away from the vehicle's underbody region. If the foreign object/hazard moves to the underbody region, control signals are transmitted to the vehicle's propulsion and/or steering system to automate preventative action that prevents collision of the vehicle with and/or removes the foreign object/hazard from the underbody.

An automatic driving vehicle that is automatically movable, the automatic driving vehicle includes a LIDAR configured to acquire external world information on an automatic movement, and a bracket holding the LIDAR and fixed to the automatic driving vehicle. The bracket is fixed to a rearview mirror which is as another device different from the LIDAR.

A method for determining a state of a road surface in which, a time-series waveform of tire vibration detected by an acceleration sensor is windowed by a windowing means with a time T and a feature vector X.sub.i in each time window is calculated through the extraction of a time-series waveform of the tire-vibration in each time window. Thereafter, in the calculation of a kernel function K.sub.A from the feature vector X.sub.i in each time window and a road surface feature vector Y.sub.Aj that is a feature vector in each time window calculated from a time-series waveform of tire-vibration that has been calculated in advance for each road surface state, the feature vector X.sub.i in each time window and the road-surface feature vector Y.sub.Aj are made to be vibration levels of frequency bands of 500 Hz or greater extracted from the time-series waveform in each time window.

A process includes receiving data indicating position and velocity of a vehicle at time points during a time window; using the data to calculate a path of movement by the vehicle; and determining whether the path indicates a change in a direction of movement of the vehicle. When the calculated path indicates a change in the direction, the location of the vehicle at a future time relative to the time window is estimated using the received position and velocity data at end of the time window, and a classification based on a similarity of the calculated path to at least one vehicle path corresponding to a vehicle preparing to turn and to at least one vehicle path corresponding to a vehicle not preparing to turn, and based on the similarity, classifying the calculated path either as that of a vehicle preparing to turn or a vehicle not preparing to turn.

A driver support system of a vehicle that includes a neuroimaging sensor and a positioning sensor, the neuroimaging sensor detects neurological signals of an occupant and the positioning sensor detects a position of the occupant. The neuroimaging sensor is configured to be positioned within the vehicle distally from the occupant. The system further includes a processor and non-transitory computer-readable medium storing computer-readable instructions executed by the processor to generate a brainwave map based on the neurological signals, calibrate the brainwave map based on the position of the occupant, and determine a mental state of the occupant based on the calibrated-brainwave map. The processor further actuates vehicle support control in response to determining the mental state of the occupant.

A method for operating an advanced driver assistance (ADAS) system of a motor vehicle includes a vehicle controller receiving, from side and end cameras mounted to the vehicle, camera signals indicative of real-time images of outboard-facing side and end views of the vehicle. The controller determines a region of interest (ROI) inset within each end/side view within which is expected foreign objects and/or hazards. These ROIs are analyzed to detect if a foreign object/hazard is present in the vehicle's end and/or side views. Responsive to detecting the foreign object/hazard, movement of the foreign object/hazard is tracked to determine if the foreign object/hazard moves towards or away from the vehicle's underbody region. If the foreign object/hazard moves to the underbody region, control signals are transmitted to the vehicle's propulsion and/or steering system to automate preventative action that prevents collision of the vehicle with and/or removes the foreign object/hazard from the underbody.

Method and device for processing LIDAR sensor data are disclosed. The method includes (i) receiving from the LIDAR sensor a first dataset having a plurality of first data points representative of respective coordinates and associated with respective normal vectors, (ii) determining an uncertainty parameter for a given first data point based on a normal covariance of the normal vector of the given first data point where the normal covariance takes into account a measurement error of the LIDAR sensor when determining the respective coordinates of the given first data point, (iii) in response to the uncertainty parameter being above a pre-determined threshold, excluding the given first data point from the plurality of first data points, (iv) using the filtered plurality of first data points, instead of the plurality of first data points, for merging the first dataset of the LIDAR sensor with a second dataset of the LIDAR sensor.

A vehicle floor assembly includes a floor structure and a capacitive proximity sensor assembly configured to detect a user touch command and a user pressure command. The floor assembly further includes a controller for receiving the user touch command and pressure command and controlling a vehicle related operation based on the detected user input commands.