Ecological Cooperative Look-Ahead Control for Automated Vehicles Travelling on Freeways With Varying Slopes

Zhai, Chunjie; Luo, Fei; Liu, Yonggui; Chen, Ziyang

doi:10.1109/tvt.2018.2886221

Cited by 73 publications

(39 citation statements)

References 48 publications

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“…In the existing studies on vehicle platoon control, the exact linearization is utilized to derive an exact linear model for the upper level longitudinal dynamics of a vehicle. To describe the upper level longitudinal dynamics, some researchers employed second-order differential models [3], [15], [16], while others adopted third-order differential models [5], [26], [27]. In this paper, a second-order differential model will be applied to describe the upper level longitudinal dynamics of vehicles in the platoon.…”

Section: A Longitudinal Dynamics Model Of Vehicle Platoonmentioning

confidence: 99%

“…The band-stop function with compensating factors used in [5] will also be employed in the CIO-LV problem. The band-stop function is given as…”

Section: A Band-stop Functionmentioning

confidence: 99%

“…The Eco-CACC is actually the combination of the pla-tooning technology and the eco-driving technology [5], and it has the advantages of both the platooning technology and the eco-driving one. On the one hand, the Eco-CACC can organize a group of vehicles into a platoon, which can not only alleviate traffic congestion and enhance vehicle safety, but also improve fuel economy and reduce vehicle exhaust emissions thanks to the reduced aerodynamic drag [6], [7], on the other hand, without changing vehicular structure, the Eco-CACC can further decrease fuel consumption by making vehicles operate in a fuel-efficient way [8].…”

Section: Introductionmentioning

confidence: 99%

“…To achieve vehicle safety, passenger comfort, formation control and fuel economy, a switched control strategy for heterogeneous following vehicles with state constraints was proposed in our previous published paper [15], in which the motion trajectory of the leading vehicle is not optimized. Hereafter, considering the influence of continuous variable transmission (CVT) on fuel consumption, we presented an ecological cooperative look-ahead control strategy for automated vehicles travelling on freeways with varying slopes in [5], where the motion trajectories of the following vehicles and the leading one are optimized based on model predictive control (MPC) in a distributed way. More highway-based Eco-CACC strategies can be found in [16]- [20].…”

Section: Introductionmentioning

confidence: 99%

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Ecological Cooperative Adaptive Cruise Control for a Heterogeneous Platoon of Heavy-Duty Vehicles With Time Delays

et al. 2020

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To deal with energy crisis and environmental issues, higher fuel economy standards and more stringent limitations on greenhouse gas emissions for ground vehicles have been made. Ecological cooperative adaptive cruise control (Eco-CACC) has been considered as an effective solution to decrease the fuel consumption and greenhouse gas emissions of a platoon of vehicles, and this paper proposes an Eco-CACC strategy for a heterogeneous platoon of heavy-duty vehicles with time delays. The proposed Eco-CACC strategy consists of distributed control protocols for the following vehicles and a new model predictive controller for the leading one. Firstly, after the distributed control protocols are designed based on the neighboring vehicle information, the sufficient conditions that guarantee the internal stability and string stability are derived, and the upper bound of the time delays under controller parameters is also obtained. Secondly, assuming the vehicle platoon as a rigid body with followers staying at desired positions, the new model predictive controller is designed in order to further improve the overall fuel economy of vehicle platoon. Simulations are conducted to validate the sufficient conditions of internal stability and string stability, and to explore the fuel saving performance of the proposed control strategy. The simulation results demonstrate that, compared with the benchmark, the proposed Eco-CACC strategy can significantly improve the fuel economy of heterogeneous platoon.

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Section: A Longitudinal Dynamics Model Of Vehicle Platoonmentioning

confidence: 99%

“…The band-stop function with compensating factors used in [5] will also be employed in the CIO-LV problem. The band-stop function is given as…”

Section: A Band-stop Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ecological Cooperative Adaptive Cruise Control for a Heterogeneous Platoon of Heavy-Duty Vehicles With Time Delays

et al. 2020

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“…In terms of fuel-efficiency and eco-driving oriented designs for autonomous vehicles, the authors of [23,24] proposed cooperative look-ahead control strategy based on the classic distributed model predictive control (DMPC) approach. The fuel-efficiency of a vehicular platoon was maximized by optimizing the speed and motion trajectories of the autonomous vehicles with the aid of a particle swarm optimization algorithm.…”

Section: A State-of-the-artmentioning

confidence: 99%

Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach

Liu

Yue

et al. 2020

IEEE Trans. Veh. Technol.

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A novel framework is proposed for enhancing the driving safety and fuel economy of autonomous vehicles (AVs) with the aid of vehicle-to-infrastructure (V2I) communication networks. The problem of driving trajectory design is formulated for minimizing the total fuel consumption, while enhancing driving safety (by obeying the traffic rules and avoiding obstacles). In an effort to solve this pertinent problem, a deep reinforcement learning (DRL) approach is proposed for making collision-free decisions. Firstly, a deep Q-network (DQN) aided algorithm is proposed for determining the trajectory and velocity of the AV by receiving real-time traffic information from the base stations (BSs). More particularly, the AV acts as an agent to carry out optimal action such as lane change and velocity change by interacting with the environment. Secondly, to overcome the large overestimation of action values by the Q-learning model, a double deep Q-network (DDQN) algorithm is proposed by decomposing the max-Q-value operation into action selection and action evaluation. Additionally, three practical driving policies are also proposed as benchmarks. Numerical results are provided for demonstrating that the proposed trajectory design algorithms are capable of enhancing the driving safety and fuel economy of AVs. We demonstrate that the proposed DDQN based algorithm outperforms the DQN based algorithm. Additionally, it is also demonstrated that the proposed fuel-economy (FE) based driving policy derived from the DRL algorithm is capable of achieving in excess of 24% of fuel savings over the benchmarks.

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Traffic flow and emissions improvement via Vehicle‐to‐vehicle and vehicle‐to‐infrastructure communication for an intelligent intersection

Namazi

Taghavipour

2021

Asian Journal of Control

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As transportation becomes more demanding in modern communities, the challenges involved such as congestion and emissions grow more concerns. Intelligent transportation system (ITS) is one of the most recognizable solution to resolve the problem. One of the components of an ITS is intelligent intersections that use vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications to reduce the waiting time and consequently the idling time and emission of the vehicles. To implement different control strategies to reach the aforementioned objectives, a validated platform is required to simulate the real-world traffic flow. To this end, Simulation of Urban Mobility (SUMO) software along with real data are utilized for developing two control strategies: Single Vehicle Speed Advisory Algorithm (SVSAA) and Platoon Speed Advisory Algorithm (PSAA). The results of real-world simulation demonstrate that the proposed schemes are able to reduce the waiting time, fuel consumption, and emissions significantly compared with the baseline strategy. Moreover, it turns out that PSAA scheme implementation via model predictive controller may totally eliminate the congestion at the intersection.

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Ecological Cooperative Look-Ahead Control for Automated Vehicles Travelling on Freeways With Varying Slopes

Cited by 73 publications

References 48 publications

Ecological Cooperative Adaptive Cruise Control for a Heterogeneous Platoon of Heavy-Duty Vehicles With Time Delays

Ecological Cooperative Adaptive Cruise Control for a Heterogeneous Platoon of Heavy-Duty Vehicles With Time Delays

Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach

Traffic flow and emissions improvement via Vehicle‐to‐vehicle and vehicle‐to‐infrastructure communication for an intelligent intersection

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