Intersection control with connected and automated vehicles: a review

Wu, Jin−Jei; Qu, Xiaobo

doi:10.1108/jicv-06-2022-0023

Cited by 43 publications

(10 citation statements)

References 73 publications

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“…With the aim to assigning a central role of public transit [7], policy makers and operators should provide a reliable and accessible service in their cities [11]. To achieve the same target, innovative mobility solutions, such as connected autonomous vehicles [2,3], could contribute to automatically prioritizing public transport means on the road. Furthermore, the results confirmed that escooter sharing could be an effective mobility option that can complement public transport as a last-mile solution [40], thus fostering its adoption within an MaaS ecosystem.…”

Section: Discussionmentioning

confidence: 99%

“…The widespread diffusion of digital technologies and recent shifts toward serviceoriented approaches, as opposed to asset-oriented approaches, have prompted deep impacts in several sectors, including mobility [1][2][3]. From this perspective, Mobility as a Service (MaaS) is considered as an innovative ecosystem that will lead to disruptive changes in the concept of mobility [4].…”

Section: Introductionmentioning

confidence: 99%

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MaaS Adoption and Sustainability for Systematic Trips: Estimation of Environmental Impacts in a Medium-Sized City

et al. 2023

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Mobility as a Service (MaaS) is often seen as a promising solution to address societal and environmental challenges. Despite the importance of quantifying its potential benefits, few previous works have focused on the impacts on the environment, and all of them considered large cities. This study aims to forecast the diffusion of MaaS in a medium-sized city and quantify the consequent reduction in pollutant emissions for commuting trips. Answers from a mobility survey administered to employees of the Municipality of Padua (Italy) were used to calibrate a model predicting MaaS adoption, which was applied to real working trips to estimate daily vehicle emissions savings in future scenarios with different MaaS bundles. The results indicated that the opportunity to have multimodal mobility options providing door-to-door travel is a fundamental element to ensure wide MaaS diffusion. Furthermore, public transport was confirmed to be the backbone of such a system. Compared to the current scenario, we observed up to a 41% reduction in pollutant emissions. The analysis pointed out that MaaS adoption is highly dependent on the characteristics of the proposed bundles, thus highlighting the importance of a proper design of the service and ex ante evaluation of emission savings.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MaaS Adoption and Sustainability for Systematic Trips: Estimation of Environmental Impacts in a Medium-Sized City

et al. 2023

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“…Tan et al [13] proposed a real-time predictive distributed CACC control framework that addresses time delays, actuator lag, and utilizes intent-sharingbased distributed computing to improve string stability under various traffic dynamics by formulating a Kalman-filter-based real-time current driving state prediction model, thus solving the problem using a sequential Kalman filter update process and implementing a real-time distributed MPC-based CACC controller with delay-compensated predicted initial conditions. Existing research indicates that, when compared with noncooperativebehavior-based models, cooperative-behavior-based CACC methods can provide smoother acceleration and deceleration behaviors, thereby ultimately enhancing the stability and safety of CAV platoon systems [14].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of Connected and Automated Vehicle Platooning Cooperative Control with Measurement Errors

Luo,

Li,

et al. 2023

Sensors

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This paper presents a cooperative control method for connected and automated vehicle (CAV) platooning, thus specifically addressing the challenge of sensor measurement errors that can disrupt the stability of the CAV platoon. Initially, the state-space equation of the CAV platooning system was formulated, thereby taking into account the measurement error of onboard sensors. The superposition effect of the sensor measurement errors was statistically analyzed, thereby elucidating its impact on cooperative control in CAV platooning. Subsequently, the application of a Kalman filter was proposed as a means to mitigate the adverse effects of measurement errors. Additionally, the CAV formation control problem was transformed into an optimal control decision problem by introducing an optimal control decision strategy that does not impose pure state variable inequality constraints. The proposed method was evaluated through simulation experiments utilizing real vehicle trajectory data from the Next Generation Simulation (NGSIM). The results demonstrate that the method presented in this study effectively mitigates the influence of measurement errors, thereby enabling coordinated vehicle-following behavior, achieving smooth acceleration and deceleration throughout the platoon, and eliminating traffic oscillations. Overall, the proposed method ensures the stability and comfort of the CAV platooning formation.

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“…Intersections are the bottleneck that affects the traffic efficiency of urban road networks (Wu and Qu, 2022;Yu et al, 2021). Under saturated flow conditions, congestion and queue overflow are more likely to occur, resulting in greater loss of traffic efficiency (Vahidi and Sciarretta, 2018).…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Objective Control of AVs at a Saturated Signalized Intersection with Multi-Agent Deep Reinforcement Learning Approach

Lin,

Hu,

Wang

2023

J. Int. Con. Veh.

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Reinforcement learning (RL) can free automated vehicles (AVs) from the car-following constraints and provide more possible explorations for mixed behavior. This study uses deep RL as AVs' longitudinal control and designs a multilevel objectives framework for AVs' trajectory decision-making based on multi-agent DRL. The saturated signalized intersection is taken as the research object to seek the upper limit of traffic efficiency and realize the specific target control. The simulation results demonstrate the convergence of the proposed framework in complex scenarios. When prioritizing throughputs as the primary objective and emissions as the secondary objective, both indicators exhibit a linear growth pattern with increasing market penetration rate (MPR). Compared with MPR is 0%, the throughputs can be increased by 69.2% when MPR is 100%. Compared with linear adaptive cruise control (LACC) under the same MPR, the emissions can also be reduced by up to 78.8%. Under the control of the fixed throughputs, compared with LACC, the emission benefits grow nearly linearly as MPR increases, it can reach 79.4% at 80% MPR. This study employs experimental results to analyze the behavioral changes of mixed flow and the mechanism of mixed autonomy to improve traffic efficiency. The proposed method is flexible and serves as a valuable tool for exploring and studying the behavior of mixed flow behavior and the patterns of mixed autonomy.

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Intersection control with connected and automated vehicles: a review

Cited by 43 publications

References 73 publications

MaaS Adoption and Sustainability for Systematic Trips: Estimation of Environmental Impacts in a Medium-Sized City

MaaS Adoption and Sustainability for Systematic Trips: Estimation of Environmental Impacts in a Medium-Sized City

Modeling and Optimization of Connected and Automated Vehicle Platooning Cooperative Control with Measurement Errors

Multi-Level Objective Control of AVs at a Saturated Signalized Intersection with Multi-Agent Deep Reinforcement Learning Approach

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