Merging control strategies of connected and autonomous vehicles at freeway on-ramps: a comprehensive review

Zhu, Jie; Easa, Said M.; Gao, Kun

doi:10.1108/jicv-02-2022-0005

Cited by 72 publications

(24 citation statements)

References 73 publications

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“…Therefore, certain directions could be developed in the future within the current framework. For instance, merging/lane‐changing also plays an essential role in CAV control (e.g., Q. Li et al., 2022; Yue et al., 2022; Zhu et al., 2022). A more complex scenario such as CAV control for intersections (e.g., Ding et al., 2022; Z. Zhang et al., 2022) could also be studied.…”

Section: Discussionmentioning

confidence: 99%

A 2D‐connected automated vehicle car‐following control algorithm

Ran

Zhou

et al. 2023

Computer aided Civil Eng

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Car‐following prevails in daily traffic under various scenarios. However, most connected automated vehicle (CAV) car‐following algorithms in the literature only apply to one‐dimensional CAV control, which limits the application scenarios. This paper presents a two‐dimensional (2D) CAVs car‐following strategy on curved roads in the spatial domain with vehicle‐to‐vehicle and vehicle‐to‐infrastructure communication to expand the car‐following application scenarios. This control strategy constructs a 2D car‐following equilibrium policy by extending the simplified Newell's car‐following model. Based on that, a constrained optimal CAV car‐following control in the spatial domain is constructed to maintain the inter‐vehicle spacing and speed difference while regulating the lane deviation and avoiding work zones. As demonstrated by numerical results, our proposed algorithm maintains a high degree of consistency between equilibrium spacing and vehicle speed while duplicating the trajectory of the leading vehicle, implying empirical string stability. The results also suggest the robustness of handling different scenarios.

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Section: Discussionmentioning

confidence: 99%

A 2D‐connected automated vehicle car‐following control algorithm

Ran

Zhou

et al. 2023

Computer aided Civil Eng

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“…Traditional traffic control approaches focus on controlling macroscopic traffic variables (e.g. traffic volume, speed and density) based on the traffic information collected by the detector [2][3]. However, these methods tend to cause traffic flow fluctuations arising from only controlling the macroscopic traffic characteristics at the aggregation level resulting in inevitable inaccuracies.…”

Section: Introductionmentioning

confidence: 99%

Multilane freeway merging control via trajectory optimization in a mixed traffic environment

Han

Zhang

Guo

2023

IET Intelligent Trans Sys

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Merging activities at freeway merging areas can cause significant recurrent and non‐recurrent bottleneck congestion due to vehicles’ mandatory lateral conflicts. The Connected and Automated Vehicles (CAVs), with their capabilities of real‐time communication and precise trajectory control, hold great potential to prevent or mitigate such critical conflicts at merging areas. However, the performance of CAVs may be impaired by the imbalance of lane flow distribution, and non‐cooperative movements of Human‐driven Vehicles (HDVs) in the mixed traffic environment (i.e. traffic mixed with CAVs and HDVs). In this paper, a novel two‐level hierarchical traffic control framework for multilane merging areas under the mixed traffic environment is developed. Note that this paper assumes that the merging sequence is determined by the high control level and focuses on the low level of the control framework. The low control level not only establishes a trajectory optimization strategy for CAVs with lane‐changing optimization and cooperative merging control, but includes a human‐like merging strategy for HDVs. First, to balance downstream lane flow distribution and provide sufficient merging space for on‐ramp vehicles, a lane‐changing optimization method is proposed to choose a certain number of designated mainline CAVs to perform early lane changes at the upstream mainline. Second, a cooperative merging control method is presented to optimize the longitudinal trajectories of both mainline and on‐ramp CAVs while accounting for the movement of HDVs. Third, Gipps car‐following model and heuristic control are combined to represent the HDVs’ merging maneuvers. The proposed algorithm simulates and performs merging maneuvers at a typical two‐lane freeway merging area and verifies it in various scenarios considering demand level, demand splits and CAV Penetration Rate (PR). The simulation results show that the proposed algorithm can effectively facilitate merging operations, reduce the Total Travel Time (TTT), and increase the Average Travel Speeds (ATS) compared to other merging algorithms. Specifically, compared to the case of using only cooperative merging control method, the proposed algorithm can further reduce TTT by 25.5% and increase ATS by 33.38%. When PR gradually increased, the control performance of the proposed algorithm can be further improved.

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“…The complexity of highways mainly lies in highway interchanges due to the diverse connection topology and ramp geometry, which significantly affect the decision-making and motion control of AVs. Therefore, the reliability guarantee in highway interchanges becomes the bottleneck of their wide deployment in highway transport systems [19], [24]. To guarantee safety, researchers have designed different planning and control algorithms to ensure AVs move across interchanges successfully and smoothly [1], [4], [8], [21], [23].…”

Section: Introductionmentioning

confidence: 99%

FLYOVER: A Model-Driven Method to Generate Diverse Highway Interchanges for Autonomous Vehicle Testing

Zhou¹,

Lin²,

Tang³

et al. 2023

Preprint

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It has become a consensus that autonomous vehicles (AVs) will first be widely deployed on highways. However, the complexity of highway interchanges becomes the bottleneck for deploying AVs. An AV should be sufficiently tested under different highway interchanges, which is still challenging due to the lack of available datasets containing diverse highway interchanges. In this paper, we propose a model-driven method, FLYOVER, to generate a dataset consisting of diverse interchanges with measurable diversity coverage. First, FLYOVER proposes a labeled digraph to model the topology of an interchange. Second, FLYOVER takes real-world interchanges as input to guarantee topology practicality and extracts different topology equivalence classes by classifying the corresponding topology models. Third, for each topology class, FLYOVER identifies the corresponding geometrical features for the ramps and generates concrete interchanges using k-way combinatorial coverage and differential evolution. To illustrate the diversity and applicability of the generated interchange dataset, we test the built-in traffic flow control algorithm in SUMO and the fuel-optimization trajectory tracking algorithm deployed to Alibaba's autonomous trucks on the dataset. The results show that except for the geometrical difference, the interchanges are diverse in throughput and fuel consumption under the traffic flow control and trajectory tracking algorithms, respectively.

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Merging control strategies of connected and autonomous vehicles at freeway on-ramps: a comprehensive review

Cited by 72 publications

References 73 publications

A 2D‐connected automated vehicle car‐following control algorithm

A 2D‐connected automated vehicle car‐following control algorithm

Multilane freeway merging control via trajectory optimization in a mixed traffic environment

FLYOVER: A Model-Driven Method to Generate Diverse Highway Interchanges for Autonomous Vehicle Testing

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