Driver Modeling and Implementation of a Fuel-Saving ADAS

Controlling vehicle velocity, by coaching the driver to eco-drive with an advanced driver assistance system (ADAS), is a promising method to decrease fuel consumption and greenhouse gas emissions for combustion engine-driven road vehicles. By using optimal control techniques, such a system may find velocity profiles in real-time that minimize fuel consumption. This is particularly useful to recommend the optimal time to initiate coasting, which is otherwise difficult to estimate by a driver. However, this ADAS should not choose velocities and accelerations that the driver will dislike, such as those that leave too much or too little space to the preceding vehicle, or those that take corners at high speed. To remedy this, we introduce an optimal control model of acceleration that mimics drivers' behavior and combine this with a model of fuel consumption to trade-off driver preferences and fuel savings. We give examples of the velocity profiles recommended in a typical driving scenario to demonstrate the potential fuel savings. Finally, we give details of a prototype system, which has recently been implemented in the driving simulator at the University of Southampton.

[1]  S. Azzi,et al.  Eco-Driving Performance Assessment With in-Car Visual and Haptic Feedback Assistance , 2010, J. Comput. Inf. Sci. Eng..

[2]  Antonio Sciarretta,et al.  Optimal Ecodriving Control: Energy-Efficient Driving of Road Vehicles as an Optimal Control Problem , 2015, IEEE Control Systems.

[3]  Manfred Tscheligi,et al.  Acceptance of future persuasive in-car interfaces towards a more economic driving behaviour , 2009, AutomotiveUI.

[4]  Steven Broekx,et al.  Using on-board logging devices to study the longer-term impact of an eco-driving course , 2009 .

[5]  Neville A. Stanton,et al.  Encouraging Eco-Driving With Visual, Auditory, and Vibrotactile Stimuli , 2017, IEEE Transactions on Human-Machine Systems.

[6]  Hesham A. Rakha,et al.  Eco-driving at signalized intersections using V2I communication , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[7]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[8]  Maria Zarkadoula,et al.  Training urban bus drivers to promote smart driving: A note on a Greek eco-driving pilot program , 2007 .

[9]  Moritz Diehl,et al.  ACADO toolkit—An open‐source framework for automatic control and dynamic optimization , 2011 .

[10]  Leonard Evans Driver Behavior Effects on Fuel Consumption in Urban Driving , 1979 .

[11]  Anders af Wåhlberg,et al.  Long-term effects of training in economical driving: Fuel consumption, accidents, driver acceleration behavior and technical feedback , 2007 .

[12]  Qi Cheng,et al.  A new eco-driving assistance system for a light vehicle: Energy management and speed optimization , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[13]  Patricia Delhomme,et al.  The influence of multiple goals on driving behavior: the case of safety, time saving, and fuel saving. , 2011, Accident; analysis and prevention.

[14]  Rich C. McIlroy,et al.  What do people know about eco-driving? , 2017, Ergonomics.

[15]  J. Barkenbus Eco-driving: An overlooked climate change initiative , 2010 .

[16]  Jonathan M. Gilligan,et al.  Individual Carbon Emissions: The Low-Hanging Fruit , 2008 .

[17]  Arne Höltl,et al.  Perceived usefulness of eco-driving assistance systems in Europe , 2012 .

[18]  Jianqiang Wang,et al.  Minimum Fuel Control Strategy in Automated Car-Following Scenarios , 2012, IEEE Transactions on Vehicular Technology.

[19]  George M. Siouris,et al.  Applied Optimal Control: Optimization, Estimation, and Control , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[20]  Jacques Droulez,et al.  Role of Lateral Acceleration in Curve Driving: Driver Model and Experiments on a Real Vehicle and a Driving Simulator , 2001, Hum. Factors.

[21]  Roberto Lot,et al.  Portable Automobile Data Acquisition Module (ADAM) for naturalistic driving study. , 2017 .

[22]  Kun Zhou,et al.  A Closed-Loop Speed Advisory Model With Driver's Behavior Adaptability for Eco-Driving , 2015, IEEE Transactions on Intelligent Transportation Systems.