Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection

Kim, Daeho; Jeong, Ok‐Ran

doi:10.3390/s20010137

Cited by 50 publications

(27 citation statements)

References 34 publications

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“…Refs. [ 52 , 55 ] also implemented the Deep Q-Network (DQN)-based cooperative traffic signal control for multi-intersection. Each intersection was modeled and trained as an agent and attempted to collect the traffic information from the road environment.…”

Section: Cooperative Driving—a Comprehensive Analysis Of the Research Literaturementioning

confidence: 99%

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Malik

Khan

El-Sayed

2021

Sensors

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Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

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Section: Cooperative Driving—a Comprehensive Analysis Of the Research Literaturementioning

confidence: 99%

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Malik

Khan

El-Sayed

2021

Sensors

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“…Zhang et al introduced Generative Adversarial Networks (GAN) to estimate trip travel times the consideration of network-wide spatiotemporal correlations [25]. Kim and Jeong proposed Deep Q-Network (DQN) to predict traffic flow in multiple intersections [26]. Those studies show a promising performance to predict traffic states.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multitask Learning Model for Traffic Flow and Speed Forecasting

Zhang

Zhu

et al. 2020

IEEE Access

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Intelligent Transportation Systems (ITS) research and applications benefit from accurate shortterm traffic state forecasting. To improve the forecasting accuracy, this paper proposes a deep learning based multitask learning Gated Recurrent Units (MTL-GRU) with residual mappings. To enhance the performance of the MTL-GRU, feature engineering is introduced to select the most informative features for the forecasting. Then, based on real-world datasets, numerical results show that the MTL-GRU can well estimate traffic flow and speed simultaneously, and performs better than other counterparts. Experiments also show that the deep learning based MTL-GRU model can overpower the bottleneck caused by enlarging training datasets and continue to gain benefits. The results suggest the proposed MTL-GRU model with residual mappings is promising to forecast short-term traffic state. INDEX TERMS Short-term traffic forecasting, deep learning, multitask learning, feature engineering.

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“…Chen et al [27] applied DRL to left turn CAVs at a signalized intersection. Kim and Jeong [28] applied DRL to control multiple signalized intersections. Additionally, Capasso et al [29] used DRL for an intelligent roundabout.…”

Section: Introductionmentioning

confidence: 99%

An Efficiency Enhancing Methodology for Multiple Autonomous Vehicles in an Urban Network Adopting Deep Reinforcement Learning

Tran

Bae

2021

Applied Sciences

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To reduce the impact of congestion, it is necessary to improve our overall understanding of the influence of the autonomous vehicle. Recently, deep reinforcement learning has become an effective means of solving complex control tasks. Accordingly, we show an advanced deep reinforcement learning that investigates how the leading autonomous vehicles affect the urban network under a mixed-traffic environment. We also suggest a set of hyperparameters for achieving better performance. Firstly, we feed a set of hyperparameters into our deep reinforcement learning agents. Secondly, we investigate the leading autonomous vehicle experiment in the urban network with different autonomous vehicle penetration rates. Thirdly, the advantage of leading autonomous vehicles is evaluated using entire manual vehicle and leading manual vehicle experiments. Finally, the proximal policy optimization with a clipped objective is compared to the proximal policy optimization with an adaptive Kullback–Leibler penalty to verify the superiority of the proposed hyperparameter. We demonstrate that full automation traffic increased the average speed 1.27 times greater compared with the entire manual vehicle experiment. Our proposed method becomes significantly more effective at a higher autonomous vehicle penetration rate. Furthermore, the leading autonomous vehicles could help to mitigate traffic congestion.

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Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection

Cited by 50 publications

References 34 publications

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

A Multitask Learning Model for Traffic Flow and Speed Forecasting

An Efficiency Enhancing Methodology for Multiple Autonomous Vehicles in an Urban Network Adopting Deep Reinforcement Learning

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