Empirical Study of Phantom Traffic Jams on Urban Expressway

Dong, Zhenning; Wang, Yujing; Su, Yuelong; Li, Zhiheng

doi:10.1109/icicip.2018.8606713

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Unequal spacing is usually caused by tailgating, thereby, allowing the driver to misjudge its decisions based on the minimum required safe distance between two vehicles. Another factor of phantom traffic jams is the frequent lane changing because leading vehicles do not give way to faster trailing cars [3]. To mitigate the phantom traffic jam phenomenon, car manufacturers developed autonomous vehicles as well as technologies like adaptive cruise control (ACC) [4], lane keep assist [5] and automatic braking [6] which are designed to intervene with the driver's inputs when necessary.…”

Section: Introductionmentioning

confidence: 99%

Development and testing of braking and acceleration features for vehicle advanced driver assistance system

Marasigan

Mayuga

Magsino

2022

IJECE

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<span lang="EN-US">Traffic congestion is a constant problem for cities worldwide. The human driving inefficiency and poor urban planning and development contribute to traffic buildup and travel discomfort. An example of human inefficiency is the phantom traffic jam, which is caused by unnecessary braking, causing traffic to slow down, and eventually coming to a stop. In this study, a brake and acceleration feature (BAF) for the advanced driver assistance system (ADAS) is proposed to mitigate the effects of the phantom traffic phenomenon. In its initial stage, the BAF provides a heads-up display that gives information on how much braking and acceleration input is needed to maintain smooth driving conditions, i.e., without sudden acceleration or deceleration, while observing a safe distance from the vehicle in front. BAF employs a fuzzy logic controller that takes distance information from a light detection and ranging (LIDAR) sensor and the vehicle’s instantaneous speed from the engine control unit (ECU). It then calculates the corresponding percentage value of needed acceleration and braking in order to maintain travel objectives of smooth and safe-distance travel. Empirical results show that the system suggests acceleration and braking values slightly higher than the driver’s actual inputs and can achieve 90% accuracy overall.</span>

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Section: Introductionmentioning

confidence: 99%

Development and testing of braking and acceleration features for vehicle advanced driver assistance system

Marasigan

Mayuga

Magsino

2022

IJECE

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“…With the newly available V2V communication performance bounds, we propose an intelligent strategy for the control of inter-vehicle distance for cooperative autonomous driving. Furthermore, we propose a Markov Chain based algorithm to predict the parameter and a safe distance mapping method, which are utilized to enable smooth vehicular speed changes [9].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Intelligent Inter-Vehicle Distance Control for 6G Enabled Cooperative Autonomous Driving

Chen¹,

Leng

He³

et al. 2020

Preprint

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Research on the sixth generation cellular networks (6G) is gaining huge momentum to achieve ubiquitous wireless connectivity. Connected autonomous driving (CAV) is a critical vertical envisioned for 6G, holding great potentials of improving road safety, road and energy efficiency. However the stringent service requirements of CAV applications on reliability, latency and high speed communications will present big challenges to 6G networks. New channel access algorithms and intelligent control schemes for connected vehicles are needed for 6G supported CAV. In this paper, we investigated 6G supported cooperative driving, which is an advanced driving mode through information sharing and driving coordination. Firstly we quantify the delay upper bounds of 6G vehicle to vehicle (V2V) communications with hybrid communication and channel access technologies. A deep learning neural network is developed and trained for fast computation of the delay bounds in real time operations. Then, an intelligent strategy is designed to control the inter-vehicle distance for cooperative autonomous driving. Furthermore, we propose a Markov Chain based algorithm to predict the parameters of the system states, and also a safe distance mapping method to enable smooth vehicular speed changes. The proposed algorithms are implemented in the AirSim autonomous driving platform. Simulation results show that the proposed algorithms are effective and robust with safe and stable cooperative autonomous driving, which greatly improve the road safety, capacity and efficiency.

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“…With the newly available V2V communication performance bounds, we propose an intelligent strategy for the control of intervehicle distance for cooperative autonomous driving. Furthermore, we propose a Markov chain-based algorithm to predict the parameter and a safe distance mapping method, which are utilized to enable smooth vehicular speed changes [9].…”

mentioning

confidence: 99%

Deep-Learning-Based Intelligent Intervehicle Distance Control for 6G-Enabled Cooperative Autonomous Driving

Chen

Leng

et al. 2021

IEEE Internet Things J.

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Research on the sixth-generation cellular networks (6G) is gaining huge momentum to achieve ubiquitous wireless connectivity. Connected autonomous vehicles (CAVs) is a critical vertical application for 6G, holding great potentials of improving road safety, road and energy efficiency. However, the stringent service requirements of CAV applications on reliability, latency, and high speed communications will present big challenges to 6G networks. New channel access algorithms and intelligent control schemes for connected vehicles are needed for 6G-supported CAV. In this article, we investigated 6G-supported cooperative driving, which is an advanced driving mode through information sharing and driving coordination. First, we quantify the delay upper bounds of 6G vehicle-to-vehicle (V2V) communications with hybrid communication and channel access technologies. A deep learning neural network is developed and trained for the fast computation of the delay bounds in real-time operations. Then, an intelligent strategy is designed to control the intervehicle distance for cooperative autonomous driving. Furthermore, we propose a Markov chain-based algorithm to predict the parameters of the system states, and also a safe distance mapping method to enable smooth vehicular speed changes. The proposed algorithms are implemented in the AirSim autonomous driving platform. Simulation results show that the proposed algorithms are effective and robust with safe and stable cooperative autonomous driving, which greatly improve the road safety, capacity, and efficiency.

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Empirical Study of Phantom Traffic Jams on Urban Expressway

Cited by 5 publications

References 4 publications

Development and testing of braking and acceleration features for vehicle advanced driver assistance system

Development and testing of braking and acceleration features for vehicle advanced driver assistance system

Deep Learning Based Intelligent Inter-Vehicle Distance Control for 6G Enabled Cooperative Autonomous Driving

Deep-Learning-Based Intelligent Intervehicle Distance Control for 6G-Enabled Cooperative Autonomous Driving

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