The disclosed computer-implemented method may include improving safety in operating personal mobility vehicles. The method may track and/or control personal mobility vehicles associated with dynamic transportation networks. The method may improve safety related to PMV operation by taking advantage of the various sources and types of information related to PMV operation that are available in the dynamic transportation network. Other methods, systems, and computer-readable media are disclosed.

Techniques described in this application are directed to determining safe path navigation of an autonomous personal mobility device, including a self-driving electric scooter, using sensors and/or data from other sources. The autonomous personal mobility device is configured to generate maps and transform the maps into tile segments that can be shared with other autonomous personal mobility devices. Techniques further include receiving a request for an autonomous personal mobility device at a location, and enabling the autonomous personal mobility device to self-drive to the location.

A measurement of the rotation speed of an object is made using a time-of-flight sensor configured to detect a passing of one or more of elements of the object through a given position. The time-of-flight sensor is further mounted on a one-person vehicle configured to protect the one-person vehicle against collisions through the making a time-of-flight measurement of a relative speed between the one-person vehicle and an obstacle.

A vehicle speed estimation device of the embodiment includes an attitude angle estimator configured to estimate an attitude angle using an acceleration detected by an acceleration sensor and an angular speed detected by an angular speed sensor, which are attached to a vehicle, an angular speed corrector configured to correct the angular speed using the estimated attitude angle estimation value to calculate a corrected angular speed, a vector term calculator configured to calculate a gravity acceleration vector using the estimated attitude angle estimation value, a model configured to calculate an acceleration model value on the basis of a tire model using a steering angle of the vehicle and wheel speeds of wheels of the vehicle, and a vehicle speed estimator configured to estimate a vehicle speed of the vehicle using the corrected angular speed, the gravity acceleration vector, and the acceleration model value.

A transport vehicle (1) comprising: a heat engine (8) operable at a number of revolutions varying between a minimum number and a maximum number; an adjusting device (52) configured to adjust an infeed flow of air-fuel mixture to the heat engine (8); at least one on-board memory unit (61) in which an acoustic emission map (70) of the transport vehicle (1) is stored, wherein for each data pair comprising a first data item correlated to an operating condition of the adjusting device (52) and a second data item correlated to the number of engine revolutions, the acoustic emission map (70) associates, as a function of the first data item and as a function of the second data item, a third data item correlated to an acoustic emission measurement by at least one acoustic sensor (S1) external to the transport vehicle (1) and arranged at a separation distance from the transport vehicle (1).

The technical solution relates to the field of transport, in particular, to auxiliary devices and methods for collecting data for machine learning using vehicles, such as, for example, personal mobility devices (PMD). A method for determining permissible speed of a vehicle is proposed. There is a need to speed up the preparation of data suitable for machine learning and thus reduce the time it takes to prepare machine learning models trained on big data. The technical result achieved by implementing the claimed technical solution, in addition to the implementation of the product and/or method for its intended purpose, is an increase in the speed of preparing data suitable for machine learning, including an increase in the speed of preparing big data that can be used for machine learning. In some aspects, another technical result achieved is also an increase in road safety.