Impact of Autonomous-Vehicle-Only Lanes in Mixed Traffic Conditions

Yu, Hwapyeong; Tak, Sehyun; Park, Minju; Yeo, Hwasoo

doi:10.1177/0361198119847475

Cited by 50 publications

(20 citation statements)

References 13 publications

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“…Within the mixedautonomy traffic environment, one could consider scalable supervision as a means of supporting dedicated AV lanes on freeways. These lanes have been shown in simulation to improve traffic efficiency while also worsening on-road safety [15]. The potential speed difference between AV-only and standard lanes present a merging problem similar to the case studied here.…”

Section: Introductionmentioning

confidence: 56%

Cooperation for Scalable Supervision of Autonomy in Mixed Traffic

Hickert¹,

Li²,

Wu³

2021

Preprint

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Improvements in autonomy offer the potential for positive outcomes in a number of domains, yet guaranteeing their safe deployment is difficult. This work investigates how humans can intelligently supervise agents to achieve some level of safety even when performance guarantees are elusive. The motivating research question is: In safety-critical settings, can we avoid the need to have one human supervise one machine at all times? The paper formalizes this 'scaling supervision' problem, and investigates its application to the safety-critical context of autonomous vehicles (AVs) merging into traffic. It proposes a conservative, reachability-based method to reduce the burden on the AVs' human supervisors, which allows for the establishment of high-confidence upper bounds on the supervision requirements in this setting. Order statistics and traffic simulations with deep reinforcement learning show analytically and numerically that teaming of AVs enables supervision time sublinear in AV adoption. A key takeaway is that, despite present imperfections of AVs, supervision becomes more tractable as AVs are deployed en masse. While this work focuses on AVs, the scalable supervision framework is relevant to a broader array of autonomous control challenges.

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Section: Introductionmentioning

confidence: 56%

Cooperation for Scalable Supervision of Autonomy in Mixed Traffic

Hickert¹,

Li²,

Wu³

2021

Preprint

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“…The ITTC represents the collision risk of the subject vehicle considering the distance and relative velocity between the subject and the preceding vehicles [ 52 , 53 , 54 , 55 ]. This indicator is often used to estimate the danger of the driver and CAV.…”

Section: Methodsmentioning

confidence: 99%

Safety Monitoring System of CAVs Considering the Trade-Off between Sampling Interval and Data Reliability

Tak

Choi

2022

Sensors

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The safety of urban transportation systems is considered a public health issue worldwide, and many researchers have contributed to improving it. Connected automated vehicles (CAVs) and cooperative intelligent transportation systems (C-ITSs) are considered solutions to ensure the safety of urban transportation systems using various sensors and communication devices. However, realizing a data flow framework, including data collection, data transmission, and data processing, in South Korea is challenging, as CAVs produce a massive amount of data every minute, which cannot be transmitted via existing communication networks. Thus, raw data must be sampled and transmitted to the server for further processing. The data acquired must be highly accurate to ensure the safety of the different agents in C-ITS. On the other hand, raw data must be reduced through sampling to ensure transmission using existing communication systems. Thus, in this study, C-ITS architecture and data flow are designed, including messages and protocols for the safety monitoring system of CAVs, and the optimal sampling interval determined for data transmission while considering the trade-off between communication efficiency and accuracy of the safety performance indicators. Three safety performance indicators were introduced: severe deceleration, lateral position variance, and inverse time to collision. A field test was conducted to collect data from various sensors installed in the CAV, determining the optimal sampling interval. In addition, the Kolmogorov–Smirnov test was conducted to ensure statistical consistency between the sampled and raw datasets. The effects of the sampling interval on message delay, data accuracy, and communication efficiency in terms of the data compression ratio were analyzed. Consequently, a sampling interval of 0.2 s is recommended for optimizing the system’s overall efficiency.

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“…In this study, the team assumed the behavior of human drivers follows Gipps model which is one of the most widely accepted car-following models used by many researchers ( 35 , 36 ). The Gipps model consists of two components (acceleration and deceleration) where the first component (acceleration) indicates the intention of a vehicle to reach a certain desired speed, while the second part (deceleration) creates a limitation of the desired speed imposed by the preceding vehicle.…”

Section: Proposed Methodologymentioning

confidence: 99%

Application of Connected and Automated Vehicles in a Large-Scale Network by Considering Vehicle-to-Vehicle and Vehicle-to-Infrastructure Technology

Rahman

Abdel-Aty

2020

Transportation Research Record: Journal of the Transportation R

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Application of connected and automated vehicles (CAVs) is expected to have a significant impact on traffic safety and mobility. Although several studies evaluated the effectiveness of CAVs in a small roadway segment, there is a lack of studies analyzing the impact of CAVs in a large-scale network by considering both freeways and arterials. Therefore, the objective of this study is to analyze the effectiveness of CAVs at the network level by utilizing both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies. Also, the study proposed a new signal control algorithm through V2I technology to elevate the performance of CAVs at intersections. A car-following model named cooperative adaptive cruise control was utilized to approximate the driving behavior of CAVs in the Aimsun Next microsimulation environment. For the testbed, the research team selected Orlando central business district area in Florida, U.S. To this end, the impacts of CAVs were evaluated based on traffic efficiency (e.g., travel time rate [TTR], speed, and average approach delay, etc.) and safety surrogates (e.g., standard deviation of speed, real-time crash-risk models for freeways and arterials, time exposed time-to-collision). The results showed that the application of CAVs reduced TTR significantly compared with the base condition even with the low market penetration level. Also, the proposed signal control algorithm reduced the approach delay for 94% of the total intersections present in the network. Moreover, safety evaluation results showed a significant improvement of traffic safety in the freeways and arterials under CAV conditions with different market penetration rates.

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Impact of Autonomous-Vehicle-Only Lanes in Mixed Traffic Conditions

Cited by 50 publications

References 13 publications

Cooperation for Scalable Supervision of Autonomy in Mixed Traffic

Cooperation for Scalable Supervision of Autonomy in Mixed Traffic

Safety Monitoring System of CAVs Considering the Trade-Off between Sampling Interval and Data Reliability

Application of Connected and Automated Vehicles in a Large-Scale Network by Considering Vehicle-to-Vehicle and Vehicle-to-Infrastructure Technology

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