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1. A distributionally robust stochastic optimization-based model predictive control with distributionally robust chance constraints for cooperative adaptive cruise control under uncertain traffic conditions

   https://doi.org/10.1016/j.trb.2020.05.001  

   Zhao, S.; Zhang, K. (August 2020, Transportation research)

   Motivated by connected and automated vehicle (CAV) technologies, this paper proposes a data-driven optimization-based Model Predictive Control (MPC) modeling framework for the Cooperative Adaptive Cruise Control (CACC) of a string of CAVs under uncertain traffic conditions. The proposed data-driven optimization-based MPC modeling framework aims to improve the stability, robustness, and safety of longitudinal cooperative automated driving involving a string of CAVs under uncertain traffic conditions using Vehicle-to-Vehicle (V2V) data. Based on an online learning-based driving dynamics prediction model, we predict the uncertain driving states of the vehicles preceding the controlled CAVs. With the predicted driving states of the preceding vehicles, we solve a constrained Finite-Horizon Optimal Control problem to predict the uncertain driving states of the controlled CAVs. To obtain the optimal acceleration or deceleration commands for the CAVs under uncertainties, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e. a special case of data-driven optimization models under moment bounds) with a Distributionally Robust Chance Constraint (DRCC). The predicted uncertain driving states of the immediately preceding vehicles and the controlled CAVs will be utilized in the safety constraint and the reference driving states of the DRSO-DRCC model. To solve the minimax program of the DRSO-DRCC model, we reformulate the relaxed dual problem as a Semidefinite Program (SDP) of the original DRSO-DRCC model based on the strong duality theory and the Semidefinite Relaxation technique. In addition, we propose two methods for solving the relaxed SDP problem. We use Next Generation Simulation (NGSIM) data to demonstrate the proposed model in numerical experiments. The experimental results and analyses demonstrate that the proposed model can obtain string-stable, robust, and safe longitudinal cooperative automated driving control of CAVs by proper settings, including the driving-dynamics prediction model, prediction horizon lengths, and time headways. Computational analyses are conducted to validate the efficiency of the proposed methods for solving the DRSO-DRCC model for real-time automated driving applications within proper settings. 

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2. Platoon Centered Control for Eco-driving at Signalized Intersection Built upon Hybrid MPC System

   https://doi.org/10.1109/ITSC55140.2022.9922270  

   Zhang, Hanyu; Du, Lili (October 2022, 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC))

   Even though extensive studies have developed various eco-driving strategies for vehicle platoon to travel on urban roads with traffic signals, most of them focus on vehicle-level trajectory planning or speed advisory rather than real-time platoon-level closed-loop control. In addition, majority of existing efforts neglect the traffic and vehicle dynamic uncertainties to avoid the modeling and solution complexity. To make up these research gaps, this study develops a system optimal vehicle platooning control for eco-driving (SO-ED), which can guide a mixed flow platoon to smoothly run on the urban roads and pass the signalized intersections without sudden deceleration or red idling. The SO-ED is mathematically implemented by a hybrid model predictive control (MPC) system, including three MPC controllers and an MINLP platoon splitting switching signal. Based on the features of the system, this study uses active set method to solve the large-scale MPC controllers in real time. The numerical experiments validate the merits of the proposed SO-ED in smoothing the traffic flow and reducing energy consumption and emission at urban signalized intersections. 

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3. Computationally Efficient Approach for Evaluating Eco-Approach and Departure for Heavy-Duty Trucks

   https://doi.org/10.1177/03611981241254112  

   Wei, Zhensong; Hao, Peng; Kailas, Aravind; Amar, Pascal; Palmeter, Kyle; Levin, Lennard; Orens, Stephen; Barth, Matthew; Boriboonsomsin, Kanok (December 2024, Transportation Research Record: Journal of the Transportation Research Board)

   Connected vehicle-based eco-driving applications have emerged as effective tools for improving energy efficiency and environmental sustainability in the transportation system. Previous research mainly focused on vehicle-level or link-level technology development and assessment using real-world field tests or traffic microsimulation models. There is still high uncertainty in understanding and predicting the impact of these connected eco-driving applications when they are implemented on a large scale. In this paper, a computationally efficient and practically feasible methodology is proposed to estimate the potential energy savings from one eco-driving application for heavy-duty trucks named Eco-Approach and Departure (EAD). The proposed methodology enables corridor-level or road network–level energy saving estimates using only road length, speed limit, and travel time at each intersection as inputs. This technique was validated using EAD performance data from traffic microsimulation models of four trucking corridors in Carson, California; the estimates of energy savings using the proposed methodology were around 1% average error. The validated models were subsequently applied to estimate potential energy savings from EAD along truck routes in Carson. The results show that the potential energy savings vary by corridor, ranging from 1% to 25% with an average of 14%. 

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4. Overtaking-Enabled Eco-Approach Control at Signalized Intersections for Connected and Automated Vehicles

   https://doi.org/10.1109/TITS.2023.3328022  

   Dong, Haoxuan; Zhuang, Weichao; Wu, Guoyuan; Li, Zhaojian; Yin, Guodong; Song, Ziyou (May 2024, IEEE Transactions on Intelligent Transportation Systems)

   Preceding vehicles typically dominate the movement of following vehicles in traffic systems, thereby significantly influencing the efficacy of eco-driving control that concentrates on vehicle speed optimization. To potentially mitigate the negative effect of preceding vehicles on eco-driving control at the signalized intersection, this study proposes an overtaking-enabled eco-approach control (OEAC) strategy. It combines driving lane planning and speed optimization for connected and automated vehicles to relax the first-in-first-out queuing policy at the signalized intersection, minimizing the host vehicle’s energy consumption and travel delay. The OEAC adopts a two-stage receding horizon control framework to derive optimal driving trajectories for adapting to dynamic traffic conditions. In the first stage, the driving lane optimization problem is formulated as a Markov decision process and solved using dynamic programming, which takes into account the uncertain disturbance from preceding vehicles. In the second stage, the vehicle’s speed trajectory with the minimal driving cost is optimized rapidly using Pontryagin’s minimum principle to obtain the closed-form analytical optimal solution. Extensive simulations are conducted to evaluate the effectiveness of the OEAC. The results show that the OEAC is excellent in driving cost reduction over constant speed and regular eco-approach and departure strategies in various traffic scenarios, with an average improvement of 20.91% and 5.62%, respectively. 

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5. Developing an eco-driving strategy in a hybrid traffic network using reinforcement learning

   https://doi.org/10.1177/00368504241263406  

   Jamil, Umar; Malmir, Mostafa; Chen, Alan; Filipovska, Monika; Xie, Mimi; Ding, Caiwen; Jin, Yu-Fang (July 2024, Science Progress)

   Eco-driving has garnered considerable research attention owing to its potential socio-economic impact, including enhanced public health and mitigated climate change effects through the reduction of greenhouse gas emissions. With an expectation of more autonomous vehicles (AVs) on the road, an eco-driving strategy in hybrid traffic networks encompassing AV and human-driven vehicles (HDVs) with the coordination of traffic lights is a challenging task. The challenge is partially due to the insufficient infrastructure for collecting, transmitting, and sharing real-time traffic data among vehicles, facilities, and traffic control centers, and the following decision-making of agents involved in traffic control. Additionally, the intricate nature of the existing traffic network, with its diverse array of vehicles and facilities, contributes to the challenge by hindering the development of a mathematical model for accurately characterizing the traffic network. In this study, we utilized the Simulation of Urban Mobility (SUMO) simulator to tackle the first challenge through computational analysis. To address the second challenge, we employed a model-free reinforcement learning (RL) algorithm, proximal policy optimization, to decide the actions of AV and traffic light signals in a traffic network. A novel eco-driving strategy was proposed by introducing different percentages of AV into the traffic flow and collaborating with traffic light signals using RL to control the overall speed of the vehicles, resulting in improved fuel consumption efficiency. Average rewards with different penetration rates of AV (5%, 10%, and 20% of total vehicles) were compared to the situation without any AV in the traffic flow (0% penetration rate). The 10% penetration rate of AV showed a minimum time of convergence to achieve average reward, leading to a significant reduction in fuel consumption and total delay of all vehicles. 

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