A Dynamic Adaptive Algorithm for Merging Into Platoons in Connected Automated Environments

Karbalaieali, Sogand; Osman, Osama A.; Ishak, Sherif

doi:10.1109/tits.2019.2938728

Cited by 35 publications

(18 citation statements)

References 21 publications

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“…However, as this strategy prioritizes the mainline traffic flow, the ramp vehicles might get heavily congested due to the traffic signal queue. Utilizing vehicle connectivity to process merging requests in S8 generally outperforms other strategies at LOS D and E in terms of travel time of freeway vehicles, which is comparable with previous studies (Park et al 2011;Scarinci et al 2017;Karbalaieali et al 2019). D r a f t assumed based on a freeway operating speed of 100 km/h.…”

Section: Discussionsupporting

confidence: 84%

“…Simulation results indicated that this strategy could reduce traffic congestion on the mainline and reduce the average travel time for merging vehicles. Similarly, Karbalaieali et al (2019) developed an adaptive algorithm in a fully CAV environment where merging vehicles can communicate with the upstream FRL CAVs to provide merging gaps by encouraging these vehicles to speed up, slow down, or change lane. The simulation results showed that using this algorithm can reduce delay for ramp vehicles and increase the travel time reliability index for the mainline.…”

Section: Introductionmentioning

confidence: 99%

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Accommodation of freeway merging in a mixed traffic environment including connected autonomous vehicles

2022

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The use of connected autonomous vehicles (CAVs) can help reduce gaps between vehicles. However, smaller gaps may cause challenges for on-ramp vehicles merging into the freeway. This study introduces several strategies that allow on-ramp vehicles to merge safely and efficiently to the mainline by providing adequate merging gaps or resolving merging conflicts in a mixed traffic environment. Eight methods are proposed to facilitate merging. These strategies are simulated in a mixed traffic environment and with different traffic characteristics, including different proportions of CAVs. The results indicate that operational performance is affected by CAV penetration rate and traffic management strategy. For relatively low freeway traffic volume, or 100% CAV penetration rate, traffic operations under the do-nothing option performs almost as well as the best traffic management strategy. However, at lower CAV penetration rates and high freeway traffic volumes, the proposed strategies are more viable than the do-nothing option.

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Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Accommodation of freeway merging in a mixed traffic environment including connected autonomous vehicles

2022

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“…In practice, for each CAV j ∈ N (t) the coordinator stores the optimal exit time t f j and the corresponding coefficients a j , b j , c j , d j . It has been shown [48] that there is no duality gap in (15), thus the solution can be derived in real time.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Each time a CAV i enters the control zone, then it first requests the time trajectories of all other CAVs already in the control zone to find its optimal exit time t f i from (15). Then, CAV i transmits its four coefficients and t f i back to the coordinator.…”

Section: Problem Formulationmentioning

confidence: 99%

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A Research and Educational Robotic Testbed for Real-time Control of Emerging Mobility Systems: From Theory to Scaled Experiments

Chalaki¹,

Beaver²,

Mahbub³

et al. 2021

Preprint

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Emerging mobility systems, e.g., connected and automated vehicles (CAVs), shared mobility, mark a paradigm shift in which a myriad of opportunities exist for users to better monitor the transportation network conditions and make optimal operating decisions to improve safety and reduce pollution, energy consumption, and travel delays [1]. Emerging mobility systems are typical cyber-physical systems where the cyber component (e.g., data and shared information through vehicle-to-vehicle and vehicle-to-infrastructure communication) can aim at optimally controlling the physical entities (e.g., CAVs, non-CAVs). The cyber-physical nature of such systems is associated with significant control challenges and gives rise to a new level of complexity in modeling and control [2]. As we move to increasingly complex emerging mobility systems, new control approaches are needed to optimize the impact on system behavior [3] of the interplay between vehicles at different traffic scenarios [4]. It is expected that CAVs will gradually penetrate the market and interact with human-driven vehicles in ways that will improve safety and transportation efficiency over the next several years. However, different levels of vehicle automation in the transportation network can significantly alter transportation efficiency metrics [5] ranging from 45% improvement to 60% deterioration. Moreover, we anticipate that efficient transportation and travel cost reduction might alter human travel behavior causing rebound effects, e.g., by improving efficiency, travel cost is decreased, hence willingness-to-travel is increased. The latter would increase overall vehicle miles traveled, which in turn might negate the benefits in terms of energy and travel time.Several studies have shown the benefits of CAVs to reduce energy and alleviate traffic congestion in specific transportation scenarios [6]-[8]. There have been two major approaches to utilizing connectivity and automation of vehicles, namely, platooning and traffic smoothing. A platoon is defined as a group of closely-coupled vehicles traveling to reduce their aerodynamic drag, especially at high cruising speeds. The concept of platoon formation is a popular systemlevel approach to address traffic congestion, which gained momentum in the 1980s and 1990s

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A Two-Stage Framework for CAV Platoon Formation Transformation

Yang,

Chen,

2024

Int.J Automot. Technol.

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A Dynamic Adaptive Algorithm for Merging Into Platoons in Connected Automated Environments

Cited by 35 publications

References 21 publications

Accommodation of freeway merging in a mixed traffic environment including connected autonomous vehicles

Accommodation of freeway merging in a mixed traffic environment including connected autonomous vehicles

A Research and Educational Robotic Testbed for Real-time Control of Emerging Mobility Systems: From Theory to Scaled Experiments

A Two-Stage Framework for CAV Platoon Formation Transformation

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