A method includes determining, by a vehicle, a failure with a computer or telecommunications system operating in the vehicle and when a failure is detected, activating an acoustic system on the vehicle, detecting a vibration by the vehicle; and transmitting an audible signal responsive to the detecting step. The audible signal may include words in a human vocabulary.

A crash sensor system for a vehicle capable of operating in an autonomous mode includes an electronic control unit (ECU) having a processor circuit. The ECU triggers an occupant restraint system of the vehicle in the event of a severe impact event with the vehicle. Satellite sensors are electrically connected to the ECU and are mounted at the front end, the rear end, the right side and the left side of the vehicle near or on an outer surface thereof to detect low severity impact event with the vehicle that does not cause activation of the occupant restraint system. The processor circuit executes an algorithm to confirm, via data from the plurality of satellite sensors, whether the low severity impact event with the vehicle occurred and if so, the ECU triggers a brake controller and a steering controller to cause the vehicle, while in the autonomous mode, to pull over and stop.

This document describes near-object detection using ultrasonic sensors. Specifically, when an object is in a near-object range or distance from a vehicle, an object-detection system of the vehicle can utilize raw range measurements and various parameters derived from the raw range measurements. The various parameters may include an average, a slope, and a variation of the range. In the near-object range, using the parameters derived from the raw range measurements may lead to increases in the accuracy and performance of a vehicle-based object-detection system. The increased accuracy in near-object detection capability enhances safe driving.

The present teaching relates to apparatus, method, medium, and implementations for simultaneously calibrating multiple sensors of different types. Multiple sensors of different types are first activated to initiate simultaneous calibration thereof based on a 3D construct including a plurality of fiducial marks. Sensors of different types including visual and depth based sensors operate in their respective coordinate systems. Each of the sensors is calibrated by acquiring sensor information of the 3D construct, detecting a feature point on each of the plurality of fiducial markers based on the sensor information, estimating a set of 3D coordinates, with respect to its coordinate system, corresponding to the detected feature points, based on which calibration parameters are generated. Sets of 3D coordinates derived in different coordinate systems are then used to compute at least one transformation matrix for corresponding at least one pair of the plurality of sensors.

A method and a system for avoiding collision of an object with a work tool coupled to a work machine. The method comprising generating an object signal by an object detector, monitoring the object signal in real-time by a processor, processing the object signal to detect an object at least partially buried in the ground surface, determining a distance between the object and a distance threshold, and sending by a controller a control signal to one or more of a machine control system and a work tool control system to modify one or more of a movement of the work tool or the work machine based on the object reaching the distance threshold.

A method for estimating a friction between a road surface and a tire of a steered wheel of a vehicle. The steered wheel being fit with dynamic steering. The vehicle includes a steering wheel and a set of sensors comprising wheel end sensors and steering wheel sensors configured to measure signals corresponding to a set of parameters., The steering wheel parameters comprising at least a steering wheel torque and a steering wheel angle. The method comprising the following steps implemented by the electronic control unit collect the signals, corresponding to the set of parameters, measured by the sensors during a period of time; process, by the signal processing module, the signals collected to provide processed signal data provide the processed signal data as input to the wheel end friction estimation model, the wheel end friction estimation model being configured to output a friction estimation of the friction between the road surface and the tire of the wheel.

The described aspects and implementations enable efficient identification of real and image signs in autonomous vehicle (AV) applications. In one implementation, disclosed is a method and a system to perform the method that includes obtaining, using a sensing system of the AV, a combined image that includes a camera image and a depth information for a region of an environment of the AV, classifying a first sign in the combined image as an image-true sign, performing a spatial validation of the first sign, which includes evaluation of a spatial relationship of the first sign and one or more objects in the region of the environment of the AV, and identifying, based on the performed spatial validation, the first sign as a real sign.

The present disclosure describes a computer-implemented method for controlling a vehicle. In aspects, the computer-implemented method includes acquiring sensor data from a sensor, determining first processed data related to a first area around the vehicle based on the sensor data using a machine-learning method, and determining second processed data related to a second area around the vehicle based on the sensor data using a conventional method. The second area may include a subarea of the first area. In addition, the computer-implemented method includes controlling the vehicle based on the first processed data and the second processed data.

In an embodiment, a method includes: receiving ambient sound; determining if the ambient sound includes a siren; in accordance with determining that the ambient sound includes a siren, determining a first location associated with the siren; receiving a camera image; determining if the camera image includes a flashing light; in accordance with determining that the camera image includes a flashing light, determining a second location associated with the flashing light; 3D data; determining if the 3D data includes an object; in accordance with determining that the 3D data includes an object, determining a third location associated with the object; determining a presence of an emergency vehicle based on the siren, detected flashing light and detected object; determining an estimated location of the emergency vehicle based on the first, second and third locations; and initiating an action related to the vehicle based on the determined presence and location.

Audibility of an outside sound needed for a driver inside an automobile to apprehend a danger and obtain a grasp of a situation necessary for driving is improved. A sound collection and emission apparatus (10) emits, on the basis of an outside acoustic signal which emanates from a sound source outside an automobile (90) and arrives at the automobile (90), an inside acoustic signal which is an acoustic signal derived from the outside acoustic signal to inside the automobile (90). A sound collection unit (M1) collects the outside acoustic signal. A sound emission unit (S1) emits the inside acoustic signal. A danger sound detection unit (11) determines whether the outside acoustic signal has a feature representing a danger defined in advance. A control unit (12) performs control that emits the inside acoustic signal from the sound emission unit (S1) such that a driver of the automobile (90) is capable of perceiving the danger if the outside acoustic signal is determined to represent the danger.