Name
Abstract | ||
|---|---|---|
In the context of behavioral change for a more sustainability mobility, we designed and implemented an in-vehicle human machine interface for electric vehicles, on the basis of an approach we propose that exploits gamification and machine learning techniques. Our main goal is to equip the driver with instant and accurate eco\\-driving strategies, obtaining an optimization of the energy consumption. More specifically, we have developed a prototype that collects data related to the driver's braking style and makes use of a machine learning model to forward-predict the resulting energy gain. It then accordingly fosters custom eco-driving behaviour by means of gamified interactions provided on an infotainment dashboard on the car. We have conducted some tests and this paper presents the preliminary and promising results we obtained. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3038450.3038455 | SmartObject@IUI |
DocType | Citations | PageRank |
Conference | 2 | 0.37 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pietro Di Lena | 1 | 2 | 0.37 |
| Silvia Mirri | 2 | 232 | 43.66 |
| Catia Prandi | 3 | 155 | 34.78 |
| Paola Salomoni | 4 | 389 | 55.88 |
| Giovanni Delnevo | 5 | 10 | 12.81 |