Methods, computer-readable media, software, and apparatuses include collecting, via one or more sensors and during a first window of time, sensor data associated with an acceleration of the vehicle, processing the sensor data to obtain frequency domain sensor data, analyzing the frequency domain sensor data to identify one or more occurrences of a vehicle mass change event, classifying a use of the vehicle for a shared mobility service during one or more time periods of the first window of time based on the one or more occurrences of a vehicle mass change, and transmitting, to a remote computing device, a notification relating to use of the vehicle for the shared mobility service.

A system includes obtaining a plurality of automobile part sets through classification based on at least one of the following factors an interface type of an interface of an automobile part, a transmission type used by the automobile part to transmit data, a manufacturer of the automobile part, a type of the automobile part, a safety level of the automobile part, a service type of a service to which data transmitted by the automobile part belongs, or a service level of the service to which the data transmitted by the automobile part in the automobile belongs.

A road surface state determination apparatus includes a plurality of tire-side devices each of which detects vibration applied to corresponding tire and produces road surface data indicative of a road surface state based on data of the vibration, and a vehicle-body-side system that determines the road surface state based on the road surface data.

The invention relates to a method for improving localization accuracy of a self-driving vehicle (100). The method comprises steps of receiving from one or more range sensing devices (110) point cloud data related to surface (130) characteristics of an environment of a self-driving vehicle (100), and based on receiving, constructing a modified normal distributions transform (NDT) histogram having a set of Gaussian distributions in a plurality of histogram bins, each of the plurality of histogram bins providing different constraining features, performing subsampling for each histogram bins in the constructed NDT histogram, in which subsampling a number of Gaussian distributions from each histogram bin is removed to construct a vector h.sup.S representing the target height of each histogram bin, and after subsampling, selecting h.sub.i.sup.S Gaussian distributions from the corresponding histogram bins of vector h.sup.S based on the constraining features given by the Gaussian distributions and adding them to the subsample set S in order to localize the self-driving vehicle (100)) with respect to the point cloud data received.

A device for determining hands off of a driver includes a torque sensor for sensing a torque caused by turning of a steering wheel and generating a torque signal, a first frequency filter and a second frequency filter for filtering the torque signal, a representative value generating device for generating a first representative value based on a frequency component of a first filtered signal output by the first frequency filter, and generating a second representative value based on a frequency component of a second filtered signal output by the second frequency filter, and a control module that determines the hands off of the driver based on a ratio of the first representative value and the second representative value.

Method for monitoring operation of a technical object, wherein a) a first orientation of the first acceleration sensor in the form of a position vector is determined, b) the mathematical model for operation of the object is generated and trained, c) the first acceleration sensor is disassembled and a three-axle replacement acceleration sensor is assembled with a new orientation on the object, d) acceleration values are detected, e) respective indicator values are calculated from the temporal course of the detected acceleration values of the replacement acceleration sensor, f) a replacement vector is determined from the indicator values and a differential vector between the replacement vector and the position vector of the first acceleration sensor determined in step b) is determined, and g) the model during operation of the object for the position vector in the orientation of the replacement vector is applied by taking into account the differential vector.

Systems and methods for detecting low impact collisions for a vehicle (100). The system includes at least one sensor (99, 110, 111, 115, 120-123, 125-136, 140, 141) and an electronic controller (150). The electronic controller (150) is configured to receive sensor data from the sensor (99, 110, 111, 115, 120-123, 125-136, 140, 141) and determine one or more features of the sensor data received from the at least one sensor. The electronic controller (150) is further configured to determine if a collision has occurred based upon the one or more features of the sensor data, and take at least one action in response to determining that the collision has occurred.

A vehicle control device includes an information acquisition unit that acquires road surface condition information indicative of a road surface condition determined on the basis of sound data obtained by performing sound source separation with respect to a sound acquired by a microphone array, a storage unit in which there is stored control content in accordance with the road surface condition, and a control unit which controls a drive unit provided on a vehicle, on the basis of the control content in accordance with the road surface condition indicated by the road surface condition information.

A road surface condition assessing device includes: a tire-mounted device; and a vehicle body system. The tire-mounted device includes: a vibration detector that outputs a detection signal of a vibration on a tire; a waveform processor that generates the road surface data; and a first data communication unit. The vehicle body system includes: a second data communication unit; and a road surface evaluation unit that evaluates the road surface condition. The tire-mounted device transmits an advertise signal including the road surface data indicative of a result of a waveform process on the detection signal and a waveform processing value corresponding to the road surface condition. The vehicle body system evaluates the road surface condition based on the waveform processing value.

A vehicle includes microphones and a system for detecting a lifeform in the vehicle, such as a pet or child left behind in a vehicle. Detecting the lifeform includes obtaining sound signals captured by the microphones and performing a spectral response analysis of the sound signals. The spectral response analysis is based on frequency-based attenuation of the vehicle. The lifeform detection system determines, based on the spectral response analysis, whether a source of a sound captured by at least one of the plurality of microphones is located inside the vehicle, and if it is, generates a notification in response to determining that the source of the sound is located inside the vehicle.