Statistics show that the global number of cars on the road will nearly double by the year 2040. The widespread use of cars prompts the search for new technologies and systems to ensure road safety, such as driving assistance systems, driver monitoring devices and driver training programs. For a number of these systems, driving maneuver recognition is a core function indispensable for correct operation. This paper addresses the problem of driving maneuver detection using smartphone sensors, especially accelerometers and gyroscopes. A framework based on a number of deep learning methods for maneuver classification and clustering was introduced. We studied 13 types of maneuvers. Three classifiers, each achieving good performance for recognizing the considered set of events, were selected, and their combination into an optimal set of classifiers was investigated. Our approach was tested on a real-world dataset, and achieved a good detection rate for 7 maneuvers with a balanced accuracy of 0.90 and an average F1 score of 0.71, which outperforms the other state-of-the-art recognition systems.