About Us
iDrive project
iDrive aims to improve
the driving experience. To achieve this, iDrive takes a step forward
from current approaches. The proposed system will collect real-time data
from the vehicle and the driver using a mobile app and an OBD-type
device. Artificial intelligence algorithms will allow the quantification
and analysis of driver behavior patterns based on the processing of the
two data streams resulting from the two devices (smartphone and OBD).
The foundation of the iDrive project lies in the national NaviEyes project, previously developed within our department. NaviEye's main objective was to reduce car accidents, using both cameras of a smartphone at the same time to detect whether the driver was not attentive to road obstacles or certain types of traffic signs. iDrive improves this paradigm by focusing on using all available sensors (data obtained on the OBD II interface, GPS, accelerometer, gyroscope, and camera) to quantify driving patterns and contextual factors, thus improving the overall driving experience. Through collective user participation, complex models can be developed to capture broader trends and offer personalized suggestions for efficient routes, optimal speeds, and smoother acceleration and braking patterns. Adjacent, iDrive will contribute to increasing traffic safety, monitoring road infrastructure, reducing the carbon footprint of users, as well as increasing the resilience of emergency services.
The foundation of the iDrive project lies in the national NaviEyes project, previously developed within our department. NaviEye's main objective was to reduce car accidents, using both cameras of a smartphone at the same time to detect whether the driver was not attentive to road obstacles or certain types of traffic signs. iDrive improves this paradigm by focusing on using all available sensors (data obtained on the OBD II interface, GPS, accelerometer, gyroscope, and camera) to quantify driving patterns and contextual factors, thus improving the overall driving experience. Through collective user participation, complex models can be developed to capture broader trends and offer personalized suggestions for efficient routes, optimal speeds, and smoother acceleration and braking patterns. Adjacent, iDrive will contribute to increasing traffic safety, monitoring road infrastructure, reducing the carbon footprint of users, as well as increasing the resilience of emergency services.
Mihai Duguleană
Mihai Duguleană
Project leader
Work strategy
The methodology of iDrive revolves around the AI techniques used to construct the application, the models of DS classification and the use of the cooperative data within the system. The main tools used within iDrive will be Rule-Based (RB) algorithms, Fuzzy Logic (FL), model-based algorithms, and learning algorithms.
Research questions
Methodologically speaking, the main questions to be answered by this research are:
Team
Mihai DUGULEANĂ
Project leader
Diana BUZDUGAN
WP1 responsible
Răzvan BOBOC
WP2 responsible
Florin GÎRBACIA
WP3 responsible
Daniel VOINEA
WP4 responsible
Silviu BUTNARIU
Professor
Csaba ANTONYA
Professor
Cristian
POSTELNICU
Associate professor
Eugen BUTILĂ
Researcher