Air pollution and climate change are some of the main problems that humankind is currently facing. The electrification of the transport sector will help to reduce these problems, but one of the major barriers for the massive adoption of electric vehicles is their limited range. The energy consumption in these vehicles is affected, among other variables, by the driving behavior, making range a value that must be personalized to each driver and each type of electric vehicle. In this paper we offer a way to estimate a personalized energy consumption model by the use of the vehicle dynamics and the driving events detected by the use of the smartphone inertial sensors, allowing an easy and non-intrusive manner to predict the correct range for each user. This paper proposes, for the classification of events, a deep neural network (Long-Short Time Memory) which has been trained with more than 22,000 car trips, and the application to improve the consumption model taking into account the driver behavior captured across different trips, allowing a personalized prediction. Results and validation in real cases show that errors in the predicted consumption values are halved when abrupt events are considered in the model.

This paper addresses driver identification and verification using Deep Learning (DL) on tri-axial accelerometer signals from drivers' smartphones. The proposed driver identification architecture includes ResNet-50 followed by two Stacked Gated Recurrent Units (SGRUs). ResNet provides a deep layer model, thanks to shortcut connections, is able to extract rich features from accelerometers, and GRU layers model the dynamics of drivers' behavior. ResNet-50 pre-trained on image classification has been evaluated testing two approaches to map 1D accelerometer signals into 2D images. Siamese Neural Networks and Triplet Loss Training have been proposed for driver verification. The Siamese architecture is built on the same ResNet-50 + GRU model of driver identification, while the Triplet loss has required obtaining embeddings at journey level. Experimental results have been obtained for a dataset of 25 drivers, performing 20,025 daily life journeys with more than 800 per driver. Driver identification achieved top-1 and top-5 accuracies of 71.89% and 92.02%, respectively, and driver verification a F1 score of 74.09%. These results are competitive with state-of-the-art research that have generally tested smaller databases (in many cases based only on predefined routes), and have relied on information sources other than accelerometers, such as gyroscopes, magnetometers and GPS. Therefore, we believe that the proposed DL architectures are suitable for developing efficient driver monitoring applications based on only energy-efficient smartphone accelerometer signals.

Characterization of driving maneuvers or driving styles through motion sensors has become a field of great interest. Before now, this characterization used to be carried out with signals coming from extra equipment installed inside the vehicle, such as On-Board Diagnostic (OBD) devices or sensors in pedals. Nowadays, with the evolution and scope of smartphones, these have become the devices for recording mobile signals in many driving characterization applications. Normally multiple available sensors are used, such as accelerometers, gyroscopes, magnetometers or the Global Positioning System (GPS). However, using sensors such as GPS increase significantly battery consumption and, additionally, many current phones do not include gyroscopes. Therefore, we propose the characterization of driving style through only the use of smartphone accelerometers. We propose a deep neural network (DNN) architecture that combines convolutional and recurrent networks to estimate the vehicle movement direction (VMD), which is the forward movement directional vector captured in a phone’s coordinates. Once VMD is obtained, multiple applications such as characterizing driving styles or detecting dangerous events can be developed. In the development of the proposed DNN architecture, two different methods are compared. The first one is based on the detection and classification of significant acceleration driving forces, while the second one relies on longitudinal and transversal signals derived from the raw accelerometers. The final success rate of VMD estimation for the best method is of 90.07%.