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 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...

Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection

“…Mensing utilized the dynamic programming approach to optimize the speed trajectories of vehicles [29]. Nunzio adopted graph discretizing approach to identify the ecological spatial-temporal trajectories of vehicles [30]. Madruga applied the multi-agent system to eco-driving training [31].…”

A Real-Time Optimal Eco-driving Approach for Autonomous Vehicles Crossing Multiple Signalized Intersections

“…where J u i is the energy cost during the interval [τ i−1 , τ i ], which can be obtained as (17) and (18). The continuous acceleration constraint (6) is ensured by (19) and (20). The constraint (21) is needed to ensure the right order of the intersection crossing times of the autonomous vehicle driven by the optimal acceleration profile.…”

“…The work in [18] devises the optimal speed profile given the feasible target time, which is within some green light interval. A velocity pruning algorithm is proposed in [19] to identify feasible green windows, and a velocity profile is calculated numerically in terms of energy consumption. Most existing work solves the eco-driving problem with traffic light constraints numerically, such as using dynamic programming [17], [20], and model predictive control [16].…”

Speed trajectory planning at signalized intersections using sequential convex optimization

“…They estimated the queue clearing time and used pseudospectral method to obtain the optimal speed trajectory. In [15], a discretized solution using Dijkstra's algorithm has been obtained for multiple intersections. They assumed the vehicles cross the intersection only at a specific point in time such as at the beginning, middle or end of a green phase window.…”