The invention is a method of characterizing a road for at least one route travelled by at least one road user, with sensors: a three-axis accelerometer (ACC) and a geolocation sensor (GPS). The method comprises a measurement step (MES), a measurement processing step for disregarding (AFF) the effects related to the road user's speed in order to determine vibrations due to the road roughness, and an analysis of the vibrations to characterize (CAR) the condition of the road.

A system that includes an accelerometer configured to measure acceleration of a vehicle, a gyroscope configured to measure orientation of the vehicle, a memory having computer readable instructions, and a processor for executing the computer readable instructions. The computer readable instructions include performing at intervals: receiving acceleration data from the accelerometer; receiving orientation data from the gyroscope; combining the acceleration data and the orientation data to generate speed fluctuation and slope data for the vehicle; and transmitting the fluctuation and slope data to a controller of the vehicle. The controller utilizes the speed fluctuation and slope data to modulate an engine throttle of the vehicle.

A control system (10, 19, 185C) for a vehicle (100), the system comprising a processing means (10, 19) arranged to receive, from terrain data capture means (185C) arranged to capture data in respect of terrain ahead of the vehicle by means of one or more sensors, terrain information indicative of the topography of an area extending ahead of the vehicle (100), wherein the terrain information comprises data defining at least one 2D image of the terrain ahead of the vehicle, wherein the processing means (10, 19) is configured to: perform a segmentation operation on image data defining one said at least one 2D image and identify in the image data edges of a predicted path of the vehicle; calculate a 3D point cloud dataset in respect of the terrain ahead of the vehicle based on the terrain information; determine the 3D coordinates of lateral edges of the predicted path of the vehicle by reference to the point cloud dataset, based on the coordinates of edges of the predicted path identified in the 2D image, to determine a 3D predicted path of the vehicle; and control the direction of travel of the vehicle in dependence at least in part on the 3D predicted path.

A system and method to perform lane position sensing and tracking in a vehicle include using a first camera with a first field of view of a first side of the vehicle and a second camera with a second field of view on a second side of the vehicle to capture images. The method includes determining if a first lane line on a first side of the vehicle and a second lane line on the second side of the vehicle are both visible, and based on the first lane line and the second lane line being visible, determining, a position of the vehicle in a lane between the first lane line and the second lane line and determining a trajectory of the first lane line and the second lane line and of the vehicle.

A method for ascertaining a driving behavior of a driver of a vehicle includes: acquiring a three-dimensional signal of an acceleration sensor, the three-dimensional signal including a respective acceleration value in each of independent spatial directions; calculating a characteristic variable of the three-dimensional signal; and outputting the driving behavior based on the characteristic variable, via an output device, the characteristic variable being a measure of an aggressiveness of a driving behavior, and the characteristic variable including a fractal dimension of an embedding of the three-dimensional signal and/or a Kolmogorov entropy of the three-dimensional signal.

There is provided a measurement device including a data acquisition unit that acquires pieces of first to third data output from first to third sensors provided on a structure, an abnormality determination unit that determines whether or not each of the sensors is abnormal, a moving object detection unit that detects a moving object based on at least one of the first data and the second data, and a displacement amount calculation unit that calculates a displacement amount of the structure based on the third data, in which, when the first sensor provided on a main girder closest to an i-th lane of the structure or a main girder second closest to the i-th lane is not abnormal, the moving object detection unit detects the moving object moving on the i-th lane based on the first data output from the first sensor.

A system controls steering of a motor vehicle in case of an imminent collision with an obstacle. The vehicle includes a system to locate the vehicle relative to its lane and to determine a lateral deviation from the lane center, a determination unit for determining the presence of obstacles, a gyrometer for measuring the rotation speed of the vehicle, and a steering device, the steering angle of which can be controlled based on the measurement of a steering angle sensor or the steering torque of which can be controlled. The control system includes a perception device for determining the maximum lateral distance available for movement of the vehicle relative to obstacles, a decision device for transmitting a correction request based on the trajectory of the vehicle and on the lateral maximum distance, and an intervention device for controlling the steering device to correct the trajectory of the vehicle.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

A method of detecting a roadway shoulder in an at least partially autonomous vehicle, during a minimum risk maneuver, comprising sensing a vibration within a vehicle, identifying a frequency of the vibration, and determining whether the vehicle is in contact with rumble strips in response to the identified frequency, when at least one or more sensors in a typical known sensor system is compromised.

The disclosed embodiments include a onboard driver distraction determination system. The determination system includes a onboard sensing and computing system(s), which includes inertial sensor(s), internal sensor(s), and external sensor(s). The onboard system samples data from the sensor(s) during a driving session to determine steering activity metrics and driver behavior. A steering activity metric is a representation of the steering inputs by the driver during the driving session. Driver behavior is a representation of how distracted the driver is during the driving session. By performing the above mentioned steps, the system can provide an analysis of driver distraction and optionally, take control of the vehicle to avoid aberrant behavior.