Research on Platoon Dynamic Dispatching at Unsignalized Intersections in Intelligent and Connected Transportation Systems

Li, Qingyang; Zhang, Jian; Ji, Yaping; Wu, Kunrun; Yu, Tongshan; Wang, Jian

doi:10.4271/2020-01-5199

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…The author of [105] listed out some of these limitations could be overcome by using I2V and V2V communication. For instance, I2V communication could help AVs detect each other in time when occlusions or traffic violations occur at intersections [109][110][111][112]. Likewise, V2V communication could enable AVs to coordinate their lane changes and avoid potential hazards [113,114].…”

Section: G Safety Challenges When Poor or No Connectivity Occursmentioning

confidence: 99%

A Comprehensive Review on Limitations of Autonomous Driving and Its Impact on Accidents and Collisions

Chougule,

Chamola,

Sam

et al. 2024

IEEE Open J. Veh. Technol.

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The emergence of autonomous driving represents a pivotal milestone in the evolution of the transportation system, integrating seamlessly into the daily lives of individuals due to its array of advantages over conventional vehicles. However, selfdriving cars pose numerous challenges contributing to accidents and injuries annually. This paper aims to comprehensively examine the limitations inherent in autonomous driving and their consequential impact on accidents and collisions. Using data from the DMV, NMVCCS, and NHTSA, the paper reveals the key factors behind self-driving car accidents. It delves into prevalent limitations faced by self-driving cars, encompassing issues like adverse weather conditions, susceptibility to hacking, data security concerns, technological efficacy, testing and validation intricacies, information handling, and connectivity glitches. By meticulously analyzing reported accidents involving self-driving cars during the period spanning 2019 to 2022, the research evaluates statistical data pertaining to fatalities and injuries across diverse accident classifications. Additionally, the paper delves into the ethical and regulatory dimensions associated with autonomous driving, accentuating the legal complexities that arise from accidents involving self-driving vehicles. This review assists researchers and professionals by identifying current autonomous driving limitations and offering insights for safer adoption. Addressing these limitations through research can transform transportation systems for the better.

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Section: G Safety Challenges When Poor or No Connectivity Occursmentioning

confidence: 99%

A Comprehensive Review on Limitations of Autonomous Driving and Its Impact on Accidents and Collisions

Chougule,

Chamola,

Sam

et al. 2024

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…In previous studies, the control of CAVs at signal‐free intersections can be categorized into individual CAV control (Buzachis et al., 2020; Čakija et al., 2019; Dresner & Stone, 2008) and CAV platoon control (Bashiri et al., 2018; Kumaravel et al., 2021; Q. Li et al., 2020). Platooning is a method for grouping vehicles with similar attributes into an indivisible queue (Cao et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Compared with individual CAV control, fewer studies have focused on CAV platoon control at intersections. However, platooning can provide more benefits to the control of signal‐free intersection involving CAVs as it has following characteristics: (1) the communication burden can be reduced as the intersection manager (IM) only communicates with the platoon leader instead of all individual CAVs (Kumaravel et al., 2021); (2) the road capacity and operational efficiency of an intersection can be increased due to the close and insert‐forbidden vehicle‐following behaviors (Lioris et al., 2017); and (3) energy consumption can be decreased due to lower aerodynamic drag (Sun & Yin, 2019) as well as smoother longitudinal acceleration (He & Wu, 2018; Q. Li et al., 2020; Z. Wang et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Modeling adaptive platoon and reservation‐based intersection control for connected and autonomous vehicles employing deep reinforcement learning

Zhu

et al. 2022

Computer aided Civil Eng

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As a cutting-edge strategy to reduce travel delay and fuel consumption, platooning of connected and autonomous vehicles (CAVs) at signal-free intersections has become increasingly popular in academia. However, when determining optimal platoon size, few studies have attempted to comprehensively consider the relations between the size of a CAV platoon and traffic conditions around an intersection. To this end, this study develops an adaptive platoonbased autonomous intersection control model, named INTEL-PLT, which adopts deep reinforcement learning technique to realize the optimization of multiple dynamic objectives (e.g., efficiency, fairness, and energy saving). The framework of INTEL-PLT has a two-level structure: The first level employs a reservationbased policy integrated with a nonconflicting lane selection mechanism to determine the lanes' releasing priorities; and the second level uses a deep Qnetwork algorithm to identify the optimal platoon size based on real-time traffic conditions (e.g., traffic density, vehicle movement, etc.) of an intersection. The model is validated and examined on the simulator Simulation of Urban Mobility. It is found that the proposed model exhibits superior performances on both travel efficiency and fuel conservation as compared with state-of-the-art methods in three typical traffic conditions. Moreover, several in-depth insights learned from the simulations are provided in this paper, which could better explain the relation between platoon size and traffic condition.

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COOR-PLT: A hierarchical control model for coordinating adaptive platoons of connected and autonomous vehicles at signal-free intersections based on deep reinforcement learning

Zhu

Chen

et al. 2023

Transportation Research Part C: Emerging Technologies

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Research on Platoon Dynamic Dispatching at Unsignalized Intersections in Intelligent and Connected Transportation Systems

Cited by 3 publications

References 14 publications

A Comprehensive Review on Limitations of Autonomous Driving and Its Impact on Accidents and Collisions

A Comprehensive Review on Limitations of Autonomous Driving and Its Impact on Accidents and Collisions

Modeling adaptive platoon and reservation‐based intersection control for connected and autonomous vehicles employing deep reinforcement learning

COOR-PLT: A hierarchical control model for coordinating adaptive platoons of connected and autonomous vehicles at signal-free intersections based on deep reinforcement learning

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