A novel framework to promote eco-driving through smartphone-vehicle integration

Abstract

It was not that long ago, just in the first half on the 1990s, when mobile phones were first introduced, being big and expensive. All you could do with them was to make phone calls. Since then mobile devices have experienced a great technological advance: we carry smartphones in our pockets that provide Internet access, having accelerometers that can measure acceleration, a gyroscope that can provide orientation information, different wireless interfaces such as Bluetooth connections, and above all, great computing power. On the other hand, the automobile industry has evolved significantly during the last 10 years. One of the most exciting advances in vehicle development is vehicle-to-vehicle V2V communication, which allows cars to communicate with each other over a dedicated Wi-Fi band, and share information about vehicle speed, route direction, traffic flow, and road and weather conditions. An example of such a system is GM's (General Motors) OnStar, introduced in 1996, and that provides automatic response in case of an accident, stolen-vehicle recovery, remote door unlock, and vehicle diagnostics. Also, the standard On Board Diagnosis (OBD-II), available for several years, allows us to connect to the Electronic Control Unit (ECU) via a Bluetooth OBD-II connector. This connection interface allows connectivity between the smartphone and the vehicle, and can be purchased for just over 15 euros. The spectrum of possibilities that arise when combining the car and the smartphone is unlimited, such as performing the diagnosis of the car by assuming the tasks performed by the car's On Board Unit (OBU), or sending the collected data to a platform where the diagnosis or maintenance of the system can be realized in order to detect possible faults, help you to save gas and reduce environment pollution, and notify you of your car's problems, among other features. The general objective pursued with this doctoral thesis is to help drivers to correct bad habits in their driving. To achieve this we promote the combination between smartphones and vehicular networks to design and develop a platform able to offer useful tips to achieve safer driving and greater fuel economy. It is well-known that intelligent driving can lead to lower fuel consumption, with the consequent positive impact on the environment. The proposal that has been carried out in this doctoral thesis begins with the data capture from the vehicles' OBD-II port and data analysis through the use of graphs, maps, and statistics, both, on the server itself and in the smartphone's application developed. We applied data mining techniques and neural networks to analyze, study and generate a classiffication on driving styles based on the analysis of the characteristics of each specific route used for testing. In a second phase, we demostrate the relationship between fuel consumption and driving style. To achieve that goal, the first thing that we had to realize was how to apply different algorithms for the instantaneous consumption calculation (this parameter cannot be obtained directly from the vehicle ECU). Later, we studied and analyzed all data that was collected from the drivers who shared their monitored data with the server. Although drivers do not recognize themselves as being in a state of anxiety while driving, they are more stressed than in any other daily activity, for example, when trying to stay in the right lane, keeping the car at a certain speed, and starting and stopping the vehicle. In general, drivers are more concentrated than they think, which causes an increase in the heart rate. Many factors influence heart rate while at rest, e.g. stress, medications, medical conditions, even genes play a role. In our study we also investigate how stress and the driving behavior influence the heart rate. So, in the last phase, we demostrate the correlation between heart rate and driving style, showing how the driving style can make the heart rate vary by 3 %.