Effects of Automated Vehicles on Traffic Operations at Roundabouts

Mohebifard, Rasool; Hajbabaie, Ali

doi:10.1109/itsc45102.2020.9294563

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…The values of the parameters are different also according to the kind of vehicles involved in the simulation. In [115], a roundabout with a mixed traffic situation is studied. Two car-following models are used: conventional vehicles were modelled using Wiedemann 74 with the default parameters set in VISSIM® simulator (software for traffic simulations, [116]), while for CAVs, Wiedemann 99 was preferred, using parameters calibrated with real world-data collected in [117].…”

Section: Car-following Modelsmentioning

confidence: 99%

Roundabouts: Traffic Simulations of Connected and Automated Vehicles—A State of the Art

Campi,

Mastinu,

Previati

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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The paper deals with traffic simulation within roundabouts when both "connected and automated vehicles" (CAVs) and human-driven cars are present. The aim is to present the past, current and future research on CAVs running into roundabouts within the Cooperative, Connected and Automated Mobility (CCAM) framework. Both microscopic traffic simulations and virtual reality simulations by dynamic driving simulators will be considered. The paper is divided into five parts. At first, the literature is analysed using the Systematic Literature Review (SLR) methodology based on Scopus database. Secondly, the influence of CAVs on roundabout-specific design features and configuration is analysed. Gap-acceptance models used to define the capacity of the roundabout, one of its most important key performance indicators, are also presented. Third, the most common simulation software are described and analysed in terms of traffic demand implementation. Then the communication approaches and path management algorithms are studied. An example is proposed on the integration of microscopic traffic simulations and dynamic driving simulators virtual reality simulations. Finally, car following models suitable for roundabout traffic are discussed. There is still a gap between simulations and actual experience. There are reasonable doubts on how modelling and optimizing CAVs' behaviour into roundabouts in view of CCAM. It seems that Cooperative, Connected and Automated Vehicles (CCAVs), more than simply Connected and Automated Vehicles (CAVs), could optimise traffic flow, safety and driving comfort within the roundabout. A very promising technology for traffic simulation within the roundabout seems the one based on dynamic driving simulators.

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Section: Car-following Modelsmentioning

confidence: 99%

Roundabouts: Traffic Simulations of Connected and Automated Vehicles—A State of the Art

Campi,

Mastinu,

Previati

et al. 2024

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…It is anticipated that autonomous vehicles will gradually replace conventional vehicles in the future. In addition to performing traditional vehicle functions, such as acceleration, deceleration, braking, and motion control, autonomous vehicles also exhibit AI-driven behaviors including environment perception, task planning, path planning, vehicle control mechanisms, and intelligent obstacle avoidance [1][2][3][4]. The exploration and implementation of autonomous vehicles have the potential to alleviate road traffic congestion issues while reducing fuel consumption and carbon dioxide emissions, and thereby enhances the overall road efficiency.…”

Section: Introductionmentioning

confidence: 99%

Research on Behavioral Decision at an Unsignalized Roundabout for Automatic Driving Based on Proximal Policy Optimization Algorithm

Gan,

Zhang,

Liu

2024

Applied Sciences

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Unsignalized roundabouts have a significant impact on traffic flow and vehicle safety. To address the challenge of autonomous vehicles passing through roundabouts with low penetration, improve their efficiency, and ensure safety and stability, we propose the proximal policy optimization (PPO) algorithm to enhance decision-making behavior. We develop an optimization-based behavioral choice model for autonomous vehicles that incorporates gap acceptance theory and deep reinforcement learning using the PPO algorithm. Additionally, we employ the CoordConv network to establish an aerial view for spatial perception information gathering. Furthermore, a dynamic multi-objective reward mechanism is introduced to maximize the PPO algorithm’s reward pool function while quantifying environmental factors. Through simulation experiments, we demonstrate that our optimized PPO algorithm significantly improves training efficiency by enhancing the reward value function by 2.85%, 7.17%, and 19.58% in scenarios with 20, 100, and 200 social vehicles, respectively, compared to the PPO+CCMR algorithm. The effectiveness of simulation training also increases by 11.1%, 13.8%, and 7.4%. Moreover, there is a reduction in crossing time by 2.37%, 2.62%, and 13.96%. Our optimized PPO algorithm enhances path selection during autonomous vehicle simulation training as they tend to drive in the inner ring over time; however, the influence of social vehicles on path selection diminishes as their quantity increases. The safety of autonomous vehicles remains largely unaffected by our optimized PPO algorithm.

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“…Despite real-world experiments, the fleet data for AVs are not readily available to assess their safety benefits (Mohebifard and Hajbabaie, 2020). Therefore, a technique based on microsimulation is the only alternative technique to estimate AVs' safety benefits for different penetration rates (Almobayedh, 2019).…”

Section: Introductionmentioning

confidence: 99%

Micro-Simulation Insights into the Safety and Operational Benefits of Autonomous Vehicles

Sekar,

Malaghan,

Pawar

2023

J. Int. Con. Veh.

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Several past studies showed that Autonomous Vehicles (AVs) can reduce crash risk, stop-and-go traffic, and travel time. To analyze the safety benefits of AVs, most of the researchers proposed algorithms and simulation-based techniques. However, these studies have not assessed the safety benefits of AVs for different vehicle types under heterogeneous conditions. With this opportunity, this study focuses on the benefits of AVs in terms of safety for different penetration rates under heterogeneous conditions. This study considered three driving logics during peak hour conditions to assess the performance of AVs in terms of safety. In VISSIM, default driving behavior models for AVs were adopted to consider cautious and all-knowing driving logic and the third driving logic (Atkins) was modeled in VISSIM using parameters adopted from the previous studies. To this end, using VISSIM, the travel time output results were obtained. Also, using Surrogate Safety Assessment Model (SSAM), conflicts were extracted from output trajectory files (VISSIM). The results suggest that "cautious driving logic" reduced travel time and crash risk significantly when compared to the other two driving logics during peak hour conditions. Furthermore, the statistical analysis clearly demonstrated that "cautious driving logic" differs significantly from the other two driving logics. When Market Penetration Rates (MPR) were 50% or greater, the "cautious driving logic" significantly outperforms the other two driving logics. The results highlight that adopting "cautious driving logic" at an expressway may significantly increase safety at higher AV penetration rates (above 50%).

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Effects of Automated Vehicles on Traffic Operations at Roundabouts

Cited by 7 publications

References 25 publications

Roundabouts: Traffic Simulations of Connected and Automated Vehicles—A State of the Art

Roundabouts: Traffic Simulations of Connected and Automated Vehicles—A State of the Art

Research on Behavioral Decision at an Unsignalized Roundabout for Automatic Driving Based on Proximal Policy Optimization Algorithm

Micro-Simulation Insights into the Safety and Operational Benefits of Autonomous Vehicles

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