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Eco-Driving in Urban Traffic Networks Using Traffic Signals Information

Abstract : The problem of eco-driving is analyzed for an urban traffic network in presence of signalized intersections. It is assumed that the traffic lights timings are known and available to the vehicles via infrastructure-to-vehicle (I2V) communication. This work provides a solution to the energy consumption minimization, while traveling through a sequence of signalized intersections and always catching a green light. The optimal control problem is non-convex due to the constraints coming from the traffic lights, therefore a sub-optimal strategy to restore the convexity and solve the problem is proposed. Firstly, a pruning algorithm aims at reducing the optimization domain, by considering only the portions of the traffic lights green phases that allow to drive in compliance with the city speed limits. Then, a graph is created in the feasible region, in order to approximate the energy consumption associated with each available path in the driving horizon. Lastly, after the problem convexity is recovered, a simple optimization problem is solved on the selected path to calculate the optimal crossing times at each intersection. The optimal speeds are then suggested to the driver. The proposed sub-optimal strategy is compared to the optimal solution provided by Dynamic Programming, for validation purposes. It is also shown that the low computational load of the presented approach enables robustness properties, and results very appealing for online use.
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Contributor : Giovanni de Nunzio Connect in order to contact the contributor
Submitted on : Thursday, January 7, 2016 - 1:00:23 PM
Last modification on : Wednesday, October 27, 2021 - 11:01:50 AM
Long-term archiving on: : Friday, April 8, 2016 - 1:22:39 PM


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Giovanni de Nunzio, Carlos Canudas de Wit, Philippe Moulin, Domenico Di Domenico. Eco-Driving in Urban Traffic Networks Using Traffic Signals Information. International Journal of Robust and Nonlinear Control, Wiley, 2016, Special issue : Recent Trends in Traffic Modelling and Control, 26 (6), pp.1307-1324. ⟨10.1002/rnc.3469⟩. ⟨hal-01297629v2⟩



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