Personalized driving behavior monitoring and analysis for emerging hybrid vehicles

Authors:
Kun Li;Man Lu;Fenglong Lu;Qin Lv;Li Shang;Dragan Maksimovic
Affiliations:
Department of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, CO;Department of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, CO;Department of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, CO;Department of Computer Science, University of Colorado, Boulder, CO;Department of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, CO;Department of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, CO
Venue:
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Year:
2012

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Abstract

Emerging electric-drive vehicles, such as hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs), hold the potential for substantial reduction of fuel consumption and greenhouse gas emissions. User driving behavior, which varies from person to person, can significantly affect (P)HEV operation and the corresponding energy and environmental impacts. Although some studies exist that investigate vehicle performance under different driving behaviors, either directed by vehicle manufacturers or via on-board diagnostic (OBD) devices, they are typically vehicle-specific and require extra device/effort. Moreover, there is no or very limited feedback to an individual driver regarding how his/her personalized driving behavior affects (P)HEV performance. This paper presents a personalized driving behavior monitoring and analysis system for emerging hybrid vehicles. Our design is fully automated and non-intrusive. We propose phone-based multi-modality sensing that captures precise driver---vehicle information through de-noise, calibration, synchronization, and disorientation compensation. We also provide quantitative driver-specific (P)HEV analysis through operation mode classification, energy use and fuel use modeling. The proposed system has been deployed and evaluated with real-world user studies. System evaluation demonstrates highly-accurate (0.88-0.996 correlation and low error) driving behavior sensing, mode classification, energy use and fuel use modeling.