Optimizing the trade-off between fuel consumption and travel time in an unsignalized autonomous intersection crossing

Hadjigeorgiou, Andreas; Timotheou, Stelios

doi:10.1109/itsc.2019.8917530

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…The AV starts its journey at time t o and aims to reach the destination exactly at time t d . As illustrated in Fig.1, such a scenario can occur when an AV aims to pass an unsignalized intersection at a time and speed pre-specified by a central unit coordinating the passing of AVs from the intersection without stopping [15].…”

Section: Problem Formulationmentioning

confidence: 99%

Optimizing the Fuel Consumption of Autonomous Vehicles using Convex-Concave Programming

Hadjigeorgiou

Timotheou

2020

2020 European Control Conference (ECC)

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The recent introduction of electronic, sensing, positioning and information technologies into road transportation systems has allowed safer cruising and faster navigation of vehicles. The next frontier is the transformation of road transport into a highly automated system through the deployment of connected and automated vehicles. One of the important challenges to achieve this, is the trajectory optimization of an autonomous vehicle while traversing a specific road segment in order to minimize some metric of interest. In this context, we formulate the problem of optimal acceleration profile selection to minimize the fuel consumption of an autonomous vehicle as a discrete-time optimal control problem. To estimate fuel consumption, we consider a non-convex empirical metamodel that computes the output as a function of the acceleration and speed profile of the vehicle. As the resulting problem is non-convex, we construct a difference-of-convex functions representation of the fuel consumption metamodel and develop an iterative convex-concave programming procedure to solve the problem. Simulation results for various scenarios demonstrate that the proposed approach is considerably faster and provides better solutions compared to a state-of-practice nonlinear solver.

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Section: Problem Formulationmentioning

confidence: 99%

Optimizing the Fuel Consumption of Autonomous Vehicles using Convex-Concave Programming

Hadjigeorgiou

Timotheou

2020

2020 European Control Conference (ECC)

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“…The assumption made in previous work [10], [11], [15] that the CAVs follow constant speeds within the MZ is removed in the present work. Instead, the proposed framework allows the velocity trajectories of all CAVs to be fully optimized throughout the mission (over the horizon L + S).…”

Section: Introductionmentioning

confidence: 99%

“…However, the minimization of travel time usually implies high levels of vehicle energy consumption. As such, some effort has focused on the co-optimization of energy consumption and travel time [10], [11], where convex and hierarchical optimization approaches are also utilized to speed up otherwise computationally expensive centralized optimization schemes. Computationally efficient alternatives are the heuristic control strategies [12], [13], which however yield non-optimal control solutions in most cases.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Motion Control for Connected and Automated Electric Vehicles at Signal-Free Intersections

Pan

Chen

Evangelou

et al. 2020

2020 59th IEEE Conference on Decision and Control (CDC)

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Traffic congestion is one of the major issues for urban traffic networks. The connected and autonomous vehicles (CAV) is an emerging technology that has the potential to address this issue by improving safety, efficiency, and capacity of the transportation system. In this paper, the problem of optimal trajectory planning of battery-electric CAVs in the context of cooperative crossing of an unsignalized intersection is addressed. An optimization-based centralized intersection controller is proposed to find the optimal velocity trajectory of each vehicle so as to minimize electric energy consumption and traffic throughput. Solving the underlying optimization problem for a group of CAVs is not straightforward because of the nonlinear and nonconvex dynamics, especially when the powertrain model is explicitly modelled. In order to ensure a rapid solution search and a unique global optimum, the optimal control problem (OCP) is reformulated via convex modeling techniques. Several simulation case studies show the effectiveness of the proposed approach and the trade-off between energy consumption and traffic throughput is illustrated.

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“…Bian et al divided the intersection area into four areas according to the distance and present a process containing observation, optimization, and control [18]. Hadjigeorgiou and Timotheou studied the optimization of the travel time and fuel consumption balance of autonomous vehicles through unsignalized intersection [19]. Belkhouche proposed decentralized multiagent control laws, which are derived for conflict resolution between vehicles [20].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Multiagent Deep Deterministic Policy Gradient (CoMADDPG) for Intelligent Connected Transportation with Unsignalized Intersection

Jiang

Zhang

2020

Mathematical Problems in Engineering

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Unsignalized intersection control is one of the most critical issues in intelligent transportation systems, which requires connected and automated vehicles to support more frequent information interaction and on-board computing. It is very promising to introduce reinforcement learning in the unsignalized intersection control. However, the existing multiagent reinforcement learning algorithms, such as multiagent deep deterministic policy gradient (MADDPG), hardly handle a dynamic number of vehicles, which cannot meet the need of the real road condition. Thus, this paper proposes a Cooperative MADDPG (CoMADDPG) for connected vehicles at unsignalized intersection to solve this problem. Firstly, the scenario of multiple vehicles passing through an unsignalized intersection is formulated as a multiagent reinforcement learning (RL) problem. Secondly, MADDPG is redefined to adapt to the dynamic quantity agents, where each vehicle selects reference vehicles to construct a partial stationary environment, which is necessary for RL. Thirdly, this paper incorporates a novel vehicle selection method, which projects the reference vehicles on a virtual lane and selects the largest impact vehicles to construct the environment. At last, an intersection simulation platform is developed to evaluate the proposed method. According to the simulation result, CoMADDPG can reduce average travel time by 39.28% compared with the other optimization-based methods, which indicates that CoMADDPG has an excellent prospect in dealing with the scenario of unsignalized intersection control.

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Optimizing the trade-off between fuel consumption and travel time in an unsignalized autonomous intersection crossing

Cited by 16 publications

References 15 publications

Optimizing the Fuel Consumption of Autonomous Vehicles using Convex-Concave Programming

Optimizing the Fuel Consumption of Autonomous Vehicles using Convex-Concave Programming

Optimal Motion Control for Connected and Automated Electric Vehicles at Signal-Free Intersections

Cooperative Multiagent Deep Deterministic Policy Gradient (CoMADDPG) for Intelligent Connected Transportation with Unsignalized Intersection

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