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.
- Danijela Barić, Goran Zovak, and Marko Perisa. 2013. Effects of eco-drive education on the reduction of fuel consumption and CO2 emissions. PROMET-Traffic&Transportation 25, 3 (2013), 265--272. Google ScholarCross Ref
- Jack N Barkenbus. 2010. Eco-driving: An overlooked climate change initiative. Energy Policy 38, 2 (2010), 762--769. Google ScholarCross Ref
- Matthew Barth and Kanok Boriboonsomsin. 2009. Energy and emissions impacts of a freeway-based dynamic eco-driving system. Transportation Research Part D: Transport and Environment 14, 6 (2009), 400--410. Google ScholarCross Ref
- Maxime Boisvert, D Mammosser, P Micheau, and Alain Desrochers. 2013. Comparison of two strategies for optimal regenerative braking, with their sensitivity to variations in mass, slope and road condition. IFAC Proceedings Volumes 46, 21 (2013), 626--630.Google ScholarCross Ref
- Stefan Diewald, Andreas Möller, Tobias Stockinger, Luis Roalter, Marion Koelle, Patrick Lindemann, and Matthias Kranz. 2015. Gamification-supported exploration and practicing for automotive user interfaces and vehicle functions. In Gamification in Education and Business. Springer, 637--661. Google ScholarCross Ref
- Kevin R Dixon, Carl E Lippitt, and J Chris Forsythe. 2005. Supervised machine learning for modeling human recognition of vehicle-driving situations. In 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 604--609.Google ScholarCross Ref
- Stefano Ferretti, Silvia Mirri, Catia Prandi, and Paola Salomoni. 2016. Automatic web content personalization through reinforcement learning. Journal of Systems and Software (2016).Google Scholar
- Mehrnaz Ghamami, Yu Nie, and Ali Zockaie. 2016. Planning charging infrastructure for plug-in electric vehicles in city centers. International Journal of Sustainable Transportation 10, 4 (2016), 343--353. Google ScholarCross Ref
- V Corcoba Magana and Mario Munoz-Organero. 2015. GAFU: Using a gamification tool to save fuel. IEEE Intelligent Transportation Systems Magazine 7, 2 (2015), 58--70.Google ScholarCross Ref
- Gustavo Marfia, Marco Roccetti, Alessandro Amoroso, and Giovanni Pau. 2013. Safe driving in LA: report from the greatest intervehicular accident detection test ever. IEEE Transactions on Vehicular Technology 62, 2 (2013), 522--535. Google ScholarCross Ref
- Judith Masthoff and Julita Vassileva. 2015. Tutorial on personalization for behaviour change. In Proceedings of the 20th International Conference on Intelligent User Interfaces. ACM, 439--442.Google ScholarDigital Library
- Anthony D May. 2015. Encouraging good practice in the development of Sustainable Urban Mobility Plans. Case Studies on Transport Policy 3, 1 (2015), 3--11. Google ScholarCross Ref
- D.V. McGehee, E.N. Mazzae, and G.H.S. Baldwin. 2000. Driver reaction time in crash avoidance research: Validation of a driving simulator study on a test track. Proceedings of the XIVth Triennial Congress of the International Ergonomics Association and 44th Annual Meeting of the Human Factors and Ergonomics Association, 'Ergonomics for the New Millennium' (2000), 320--323.Google ScholarCross Ref
- Silvia Mirri, Catia Prandi, and Paola Salomoni. 2014. A context-aware system for personalized and accessible pedestrian paths. In High Performance Computing & Simulation (HPCS), 2014 International Conference on. IEEE, 833--840. Google ScholarCross Ref
- Silvia Mirri, Catia Prandi, Paola Salomoni, Franco Callegati, Andrea Melis, and Marco Prandini. 2016. A Service-Oriented Approach to Crowdsensing for Accessible Smart Mobility Scenarios. Mobile Information Systems 2016 (2016).Google Scholar
- Ludovico Antonio Muratori, Paola Salomoni, and Giovanni Pau. 2011. Feeling the pack: Strategies for an optimal participatory system to sense and recognize noise pollution. In 2011 IEEE International Conference on Consumer Electronics-Berlin (ICCE-Berlin). IEEE, 17--21.Google ScholarCross Ref
- J Nadeau, M Boisvert, and P Micheau. 2014. Implementation of a Cooperative Strategy between a Vehicle's Mechanical and Regenerative Brake System. In 2014 IEEE Vehicle Power and Propulsion Conference (VPPC). IEEE, 1--6. Google ScholarCross Ref
- G Pasaoglu, D Fiorello, A Martino, G Scarcella, A Alemanno, A Zubaryeva, and C Thiel. 2012. Driving and parking patterns of European car drivers-a mobility survey. Luxembourg: European Commission Joint Research Centre (2012).Google Scholar
- Dean A Pomerleau. 1991. Efficient training of artificial neural networks for autonomous navigation. Neural Computation 3, 1 (1991), 88--97.Google ScholarCross Ref
- Andry Rakotonirainy, Narelle Haworth, Guillaume Saint-Pierre, and Patricia Delhomme. 2011. Research issues in Eco-driving. Queensland University of Technology and French Institute in science and technology of transport (2011).Google Scholar
- Marco Roccetti, Stefano Ferretti, Claudio E Palazzi, Paola Salomoni, and Marco Furini. 2008. Riding the web evolution: from egoism to altruism. In 2008 5th IEEE Consumer Communications and Networking Conference. IEEE, 1123--1127.Google ScholarCross Ref
- Y Saboohi and H Farzaneh. 2009. Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption. Applied Energy 86, 10 (2009), 1925--1932. Google ScholarCross Ref
- Fabius Steinberger, Ronald Schroeter, Verena Lindner, Zachary Fitz-Walter, Joshua Hall, and Daniel Johnson. 2015. Zombies on the road: a holistic design approach to balancing gamification and safe driving. In Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, 320--327. Google ScholarDigital Library
- Helena Strömberg, Pontus Andersson, Susanne Almgren, Johan Ericsson, MariAnne Karlsson, and Arne Nåbo. 2011. Driver interfaces for electric vehicles. In Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, 177--184.Google ScholarDigital Library
- Tim Triplett, Robert Santos, and Sandra Rosenbloom. 2015. American Driving Survey: Methodology and Year One Results, May 2013--May 2014. (2015).Google Scholar
- Rita Tse and Giovanni Pau. 2016. Enabling street-level pollution and exposure measures: a human-centric approach. In Proceedings of the 6th ACM International Workshop on Pervasive Wireless Healthcare. ACM, 1--4.Google ScholarDigital Library
- Alberto Vanolo. 2013. Smartmentality: The smart city as disciplinary strategy. Urban Studies (2013), 0042098013494427.Google Scholar
- Sandra Wappelhorst, Julia Dobrzinski, Andreas Graff, Josephine Steiner, and Daniel Hinkeldein. 2016. Flexible Carsharing - Potential for the Diffusion of Electric Mobility. In Markets and Policy Measures in the Evolution of Electric Mobility. Springer, 67--84. Google ScholarCross Ref
- Mervyn VM Yeo, Xiaoping Li, Kaiquan Shen, and Einar PV Wilder-Smith. 2009. Can SVM be used for automatic EEG detection of drowsiness during car driving? Safety Science 47, 1 (2009), 115--124.Google ScholarCross Ref
- Maria Zarkadoula, Grigoris Zoidis, and Efthymia Tritopoulou. 2007. Training urban bus drivers to promote smart driving: A note on a Greek eco-driving pilot program. Transportation Research Part D: Transport and Environment 12, 6 (2007), 449--451. Google ScholarCross Ref
- Cheng Zhang, Mitesh Patel, Senaka Buthpitiya, Kent Lyons, Beverly Harrison, and Gregory D Abowd. 2016. Driver Classification Based on Driving Behaviors. In Proceedings of the 21st International Conference on Intelligent User Interfaces. ACM, 80--84. Google ScholarDigital Library
Index Terms
- In-vehicle Human Machine Interface: An Approach to Enhance Eco-Driving Behaviors
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