Analytical and numerical solutions for energy minimization of road vehicles with the existence of multiple traffic lights

Ozatay, Engin; Özgüner, Ü.; Filev, Dimitar; Michelini, John

doi:10.1109/cdc.2013.6761021

Cited by 43 publications

(23 citation statements)

References 11 publications

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“…The objective is to penalize the braking action to indirectly reduce energy consumption and travel time, and to discourage deviations from a suggested speed that allows to cross intersections without stopping. Somewhat similar approach was used in [15]. A preliminary algorithm determines the arrival times at each traffic light by assuming that the closest trajectory to the one of the unconstrained case (i.e.…”

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confidence: 99%

Eco‐driving in urban traffic networks using traffic signals information

Nunzio

Wit

Moulin

et al. 2015

Intl J Robust & Nonlinear

113

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The problem of eco-driving is analyzed for an urban traffic network in presence of signalized intersections. It is assumed that the traffic light timings are known and available to the vehicles via infrastructure-to-vehicle 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 because of 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 light's 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 with 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.Extensive study and experimentation of several adaptive traffic control systems have been conducted over the past three decades. Strategies such as SCOOT [4] reduces traffic delay by about 20% in urban areas; SCATS and TUC [5] have been employed all over the world proving actual benefits in terms of congestion and emissions reduction. However, these strategies present some limitations in terms of traffic conditions responsiveness and sensor failure robustness.The state of the art in wireless communication, the deployment of dedicated communication protocols for vehicular ad-hoc networks, and the decreasing price of GPS receivers allow more and more to rely on communication between the different agents of an urban traffic network for the design of robust and efficient traffic control strategies [6]. Specifically, infrastructure-tovehicle (I2V) and vehicle-to-vehicle (V2V) communication attracted the attention of many because of their potential to enable fast and cheap advanced driving assistance systems (ADAS). Several international projects and groups (Drive C2X, iTetris, COMeSafety2, and PATH), involving both automotive manufacturers and research centers, have been working on the setup of the communication infrastructure and on the assessment of the effect of this technology on traffic management and energy consumption.Speed advisory for the urban environment was already proposed in the 1980s [7,8] as a very energy-efficient traffic management strategy and as a pioneer for the modern ADAS. The rather simple initial idea of placing a roadside sign upstream from an intersection, ind...

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mentioning

confidence: 99%

Eco‐driving in urban traffic networks using traffic signals information

Nunzio

Wit

Moulin

et al. 2015

Intl J Robust & Nonlinear

113

View full text Add to dashboard Cite

show abstract

“…The optimization goal may be to minimize travel time, reduce acceleration peaks, idling at red lights, or directly minimize energy consumption. Dynamic programming is used in [180,181,182], while Dijkstra's shortest path algorithms is used in [183,184], MPC in [185,186,187,188] and a genetic algorithm in [189]; in [190], the authors derive an analytical solution for minimum energy driving through a corridor of 3 intersections. [191] is, to the best of our knowledge, the only work reporting experimental results, in the case of a speed advisory implementation.…”

Section: Literature Reviewmentioning

confidence: 99%

Control of connected and automated vehicles: State of the art and future challenges

Guanetti

Kim

Borrelli

2018

Annual Reviews in Control

498

260

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Autonomous driving technology pledges safety, convenience, and energy efficiency. Challenges include the unknown intentions of other road users: communication between vehicles and with the road infrastructure is a possible approach to enhance awareness and enable cooperation. Connected and automated vehicles (CAVs) have the potential to disrupt mobility, extending what is possible with driving automation and connectivity alone. Applications include real-time control and planning with increased awareness, routing with micro-scale traffic information, coordinated platooning using traffic signals information, eco-mobility on demand with guaranteed parking. This paper introduces a control and planning architecture for CAVs, and surveys the state of the art on each functional block therein; the main focus is on techniques to improve energy efficiency. We provide an overview of existing algorithms and their mutual interactions, we present promising optimization-based approaches to CAVs control and identify future challenges.

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“…The experimentally obtained variables P 0 , P 1 , P 2 , P 3 are reported in table 1. Ozatay et al (2013b) have designed a fuel consumption rate estimation model based on Willan's line approximation (Guzzella and Onder, 2010), however, we have observed that especially at high speed regions the model behaved poorly. In this work, we extend the model by increasing its degree of freedom with the addition of v 2 term.…”

Section: Fuel Consumption Modelmentioning

confidence: 99%