The present disclosure relates to an apparatus for assisting driving of a host vehicle including: a camera mounted to the host vehicle and having a field of view outside of the host vehicle, the camera configured to obtain front image data; and a controller configured to process the front image data, obtain collision time with a surrounding vehicle and weather information based on the image data, and control a braking device provided in the vehicle to start braking at a first braking time point based on the collision time and weather information.

A system for monitoring an acoustic scene outside a vehicle; the system including: a vehicle with wheels and a trunk, an acoustic sensor disposed in the trunk, a control unit operatively connected to the acoustic sensor, and at least one neural network operatively connected to the control unit, and trained in such a way to correlate the characteristics of an audio signal with types of road surfaces; the control unit is configured in such a way to receive an audio signal detected by the acoustic sensor while the vehicle is traveling, extract the characteristics of the audio signal and input said characteristics of the audio signal to the neural network in order to identify the type of road surface covered by the vehicle wheels.

The present disclosure relates to an apparatus (100) for estimating a road condition. The apparatus (100) comprises an input interface (110) configured to receive input data (112) derived from one or more sensors, wherein each sensor is configured to measure a physical quantity related to a vehicle (200) or its environment, wherein the input data (112) features a current driving status of the vehicle, a machine learning processor (120) configured to map the input data (112) to an estimated road condition (122), and a validation processor (130) configured to validate the estimated road condition (122) based on a measurement (132) of the road condition obtained at the current driving status of the vehicle. Validation information from the validation processor (130) can be transmitted to a cloud server (170) for further processing and producing validation swarm knowledge for a whole vehicle fleet.

A method for estimating a friction coefficient of a roadway by a transportation vehicle, wherein a control device of the transportation vehicle receives a first estimated value of a maximum horizontal force from a traction control system that is transmitted to the roadway by a wheel of the transportation vehicle. A control device receives a second estimated value of a wheel contact-patch force of the wheel from a damper controller and calculates the friction coefficient as a vehicle-independent friction coefficient based on the estimated values from the wheel contact-patch force and the horizontal force.

A method of controlling and optimizing braking and directional control of a vehicle operated on a contaminated, compliant, or non-compliant surface. The method includes steps of: collecting data from a plurality of sensors, the data being indicative of a condition of the contaminated, compliant, or non-compliant surface; sending the data to a neural controller having an algorithm configured to process the data. The algorithm includes: determining optimum braking and directional control instructions for the vehicle, generating warnings and alerts based on the calculated optimum braking and directional control instructions, and sending the optimum braking and directional control instructions to a braking and steering system of the vehicle and the warnings and alerts to an alert and warning system of the vehicle. The method further includes adjusting the steering and directional control of the braking and steering system in accordance with the optimum braking and directional control instructions provided by the neural controller.

The present disclosure relates to an apparatus for assisting driving of a host vehicle including: a camera mounted to the host vehicle and having a field of view outside of the host vehicle, the camera configured to obtain front image data; and a controller configured to process the front image data, obtain collision time with a surrounding vehicle and weather information based on the image data, and control a braking device provided in the vehicle to start braking at a first braking time point based on the collision time and weather information.

A system for monitoring an acoustic scene outside a vehicle; the system including: a vehicle with wheels and a trunk, an acoustic sensor disposed in the trunk, a control unit operatively connected to the acoustic sensor, and at least one neural network operatively connected to the control unit, and trained in such a way to correlate the characteristics of an audio signal with types of road surfaces; the control unit is configured in such a way to receive an audio signal detected by the acoustic sensor while the vehicle is traveling, extract the characteristics of the audio signal and input said characteristics of the audio signal to the neural network in order to identify the type of road surface covered by the vehicle wheels.

A method for estimating a friction coefficient of a roadway by a transportation vehicle, wherein a control device of the transportation vehicle receives a first estimated value of a maximum horizontal force from a traction control system that is transmitted to the roadway by a wheel of the transportation vehicle. A control device receives a second estimated value of a wheel contact-patch force of the wheel from a damper controller and calculates the friction coefficient as a vehicle-independent friction coefficient based on the estimated values from the wheel contact-patch force and the horizontal force.

A fuzzy-based control system in a motor vehicle for controlling a speed comprises a brake pressure measurement unit, a signal processing unit and a control unit. The brake pressure measurement unit is adapted as a finite state machine to measure a current brake pressure of a brake of a wheel of the motor vehicle dependent on a trigger. The signal processing unit is adapted to estimate a current adhesion value between a tyre associated with the wheel and the current ground, based on the current brake pressure of the brake and further measurement values. The estimating comprises an inference based on fuzzy rules and a fuzzification, a subsequently a defuzzification of the inference. The control unit is adapted to control a speed of the motor vehicle or the brake pressure of the brake, based on the estimated current adhesion value .

Systems, methods, and computer-readable storage media for using neural networks to ensure the braking capabilities of articulated vehicles are adapted to prevent jackknifing. A system can receive operational parameters associated with both a vehicle and a trailer being towed by the vehicle. The system can also identify a current surface condition and predict, using a neural network, road conditions for a portion of the road which the vehicle is approaching. When distinctions between current and predicted, future conditions are defined, and the system can cause modification of the operational parameters of the vehicle and/or the trailer.