Driving style estimation via inertial measurements

Manzoni, Vincenzo; Corti, Andrea; Luca, Pietro De; Savaresi, Sergio M.

doi:10.1109/itsc.2010.5625113

Cited by 57 publications

(38 citation statements)

References 14 publications

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“…Hence, energy-saving strategies which provide drivers with a consistent usable range and which enhance the maximum range are of primary importance for EVs. It is well known that the driver's behavior has a significant effect on energy consumption; then, important energy savings can be achieved through systems that help promote an economical style of driving [14], [15].…”

Section: The Driving Style Dynamic Applicationmentioning

confidence: 99%

“…Driving Aggressiveness Index : 1 c it stems from the consideration that the best behavior to reduce energy consumption consists in proceeding at a constant speed and, when braking and accelerating, doing so gradually [14]. Thus, we defined 1 c as the high-pass filter of , Pwheel evaluating the smoothness of the power profile chosen by the driver.…”

Section: ) the Indexesmentioning

confidence: 99%

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Green Move: A Platform for Highly Configurable, Heterogeneous Electric Vehicle Sharing

Bianchessi

Cugola

Formentin

et al. 2014

IEEE Intell. Transport. Syst. Mag.

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Vehicle sharing in urban areas has the potential to be the answer to some of the main issues that hinder the spreading of electric vehicles, in particular for what concerns the high upfront costs of the vehicles, combined with their still limited range, which can induce phenomena such as range anxiety.\ud For its potential to be realized, vehicle sharing must be tailored to the multiform needs of its users by offering a wide range of support services that can be selected based on the user preferences.\ud In this paper we present the platform for vehicle sharing developed in the Green Move project, which allows services to be dynamically loaded and unloaded on vehicles, and describe a pair of prototype applications to illustrate its benefits

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Section: The Driving Style Dynamic Applicationmentioning

confidence: 99%

Section: ) the Indexesmentioning

confidence: 99%

Green Move: A Platform for Highly Configurable, Heterogeneous Electric Vehicle Sharing

Bianchessi

Cugola

Formentin

et al. 2014

IEEE Intell. Transport. Syst. Mag.

Self Cite

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“…Recent studies indicate that fuel consumption and emissions can be reduced in existing vehicles by as much as 30% by altering the driver's driving style [2], [3]. One of the most promising approaches involves providing immediate information to the driver about the effect of driving behavior on fuel consumption.…”

Section: Introductionmentioning

confidence: 99%

An Optimization Framework for Driver Feedback Systems

Malikopoulos

Aguilar

2013

IEEE Trans. Intell. Transport. Syst.

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Modern vehicles have sophisticated electronic control units that can control engine operation with discretion to balance fuel economy, emissions, and power. These control units are designed for specific driving conditions (e.g., different speed profiles for highway and city driving). However, individual driving styles are different and rarely match the specific driving conditions for which the units were designed. In the research reported here, we investigate driving-style factors that have a major impact on fuel economy and construct an optimization framework to optimize individual driving styles with respect to these driving factors. In this context, we construct a set of polynomial metamodels to reflect the responses produced in fuel economy by changing the driving factors. Then, we compare the optimized driving styles to the original driving styles and evaluate the effectiveness of the optimization framework. Finally, we use this proposed framework to develop a real-time feedback system, including visual instructions, to enable drivers to alter their driving styles in response to actual driving conditions to improve fuel efficiency.

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“…Previous results showed that improvements in the driving style can enable savings of up to 30% of the total energy expenses, see e.g., [4], [5]. Driving style systems can be classified in two subcategories: active and passive.…”

Section: Introductionmentioning

confidence: 99%

“…By doing so, the same system can be used on different types of vehicles just by adapting few parameters of a fixed algorithm. The first attempts in assessing the driving style via inertial measurements can be found in [4], [12], where a method was designed to evaluate the performance of public bus drivers. However, in such works the feedback is generated only when the vehicle stops, and thus long periods of time may elapse without interaction between vehicle and driver, thus possibly undermining the energy savings.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Driving Style Estimation for Energy-Oriented Applications in Road Vehicles

Corti

Ongini

Tanelli

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Energy-consumption in ground transportation systems is a major cause of CO2 emissions and related environmental concerns. A relevant part of such an energy use can be saved by devising systems that can help promoting an economical driving style. To address this challenging issue, this paper presents a method and system that allow providing a quantitative description the driving style and illustrating it to the driver by means of realtime visual feedback. The driving style assessment is based on the definition of appropriate energy-oriented cost functions that can be evaluated based on inertial measurements only, thereby providing a vehicle-independent methodology. The effectiveness of the approach, and the savings enabled by the interaction with the driver are assessed with an experimental campaign carried out on urban and extra-urban routes by different drivers.

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Driving style estimation via inertial measurements

Cited by 57 publications

References 14 publications

Green Move: A Platform for Highly Configurable, Heterogeneous Electric Vehicle Sharing

Green Move: A Platform for Highly Configurable, Heterogeneous Electric Vehicle Sharing

An Optimization Framework for Driver Feedback Systems

Quantitative Driving Style Estimation for Energy-Oriented Applications in Road Vehicles

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