A method of determining whether a vehicle has experienced a collision by use of a collision detection facility implemented in a monitoring device the vehicle is equipped with, the monitoring device including a three-axis accelerometer indicating acceleration of the monitoring device over time, the collision detection facility being communicatively coupled to a remote server associated with an organization via a communications network, and comprising a processor and a storage medium having encoded thereon executable instructions that, when executed by the processor, cause the processor to carry out the method. which method comprises: obtaining information regarding a potential collision between a vehicle and an object by determining a magnitude of total acceleration experienced by the three-axis accelerometer of the monitoring device and determining whether the magnitude of total acceleration at a time exceeds a threshold, and, if so, the time at which the magnitude of total acceleration exceeds the threshold is a time of a potential collision; defining a time period that spans a time before and after the time at which the magnitude of total acceleration exceeded the threshold; obtaining data describing movements of the vehicle during the time period a times defined by a time interval, the movement datas include acceleration data indicative of an acceleration of the vehicle in the three-axis of the accelerometer, wherein a magnitude of total acceleration is determined, and include speed data of the vehicle at each time for which acceleration data is obtained; analyzing the obtained movement data with a Convolutional Neural Networks based trained classifier by comparing the obtained movement data to each of a plurality of classes defined by the classifier for different collision scenarios, wherein each class of the plurality of classes is defined by different movement data and defines whether a collision occurred and/or characteristics of collisions, and determining a probability indicating a level of match for each class of the plurality of classes, the classifier being trained with input data comprising a time series with five dimensions, namely acceleration in x, y, z, magnitude of total acceleration, and speed; selecting one or more classes of the plurality of classes which have the highest probabilities;; determining on the basis of the determined one or more classes whether the potential collision is likely to have been a collision; and, if so, reporting the potential collision to the organization. obtaining information regarding a potential collision between a vehicle and an object by determining a magnitude of total acceleration experienced by the three-axis accelerometer of the monitoring device and determining whether the magnitude of total acceleration at a time exceeds a threshold, and, if so, the time at which the magnitude of total acceleration exceeds the threshold is a time of a potential collision; defining a time period that spans a time before and after the time at which the magnitude of total acceleration exceeded the threshold; obtaining data describing movements of the vehicle during the time period a times defined by a time interval, the movement datas include acceleration data indicative of an acceleration of the vehicle in the three-axis of the accelerometer, wherein a magnitude of total acceleration is determined, and include speed data of the vehicle at each time for which acceleration data is obtained; analyzing the obtained movement data with a Convolutional Neural Networks based trained classifier by comparing the obtained movement data to each of a plurality of classes defined by the classifier for different collision scenarios, wherein each class of the plurality of classes is defined by different movement data and defines whether a collision occurred and/or characteristics of collisions, and determining a probability indicating a level of match for each class of the plurality of classes, the classifier being trained with input data comprising a time series with five dimensions, namely acceleration in x, y, z, magnitude of total acceleration, and speed; selecting one or more classes of the plurality of classes which have the highest probabilities;; determining on the basis of the determined one or more classes whether the potential collision is likely to have been a collision; and, if so, reporting the potential collision to the organization.