Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas

Zhu, Jie; Tasic, Ivana; Qu, Xiaobo

doi:10.1016/j.multra.2022.100005

Cited by 38 publications

(6 citation statements)

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“…From this study, several future research elements can be identified: (1) because of the difficulty in data collection and extraction, the scale of data used in this study was limited, which limited the level of sophistication in decision modelling. (2) The lane-changing behaviour in the weaving section could also be divided into overtaking lane-changing behaviour, continuous lane-changing behaviour and other types according to the behaviour characteristics, making the selection of the lane-changing point more complicated. In the future, more in-depth modelling research of choice of lane-changing point under different types of lane-changing behaviours is required.…”

Section: Discussionmentioning

confidence: 99%

“…Among them, the choice of lane-changing point is an important decision that drivers need to make before the start of the lane-changing stage. In particular, in the weaving section with limited lane-changing space, the choice of lane-changing point directly affects the safety of lane-changing behaviour and the efficiency of the weaving section [2].…”

Section: Introductionmentioning

confidence: 99%

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Choice of Lane-Changing Point in an Urban Intertunnel Weaving Section Based on Random Forest and Support Vector Machine

Zhao

Wang

et al. 2023

PROMTT

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Urban intertunnel weaving (UIW) section is a special type of weaving section, where various lane-changing behaviours occur. To gain insight into the lane-changing behaviour in the UIW section, in this paper we attempt to analyse the decision feature and model the behaviour from the lane-changing point selection perspective. Based on field-collected lane-changing trajectory data, the lane-changing behaviours are divided into four types. Random forest method is applied to analyse the influencing factors of choice of lane-changing point. Moreover, a support vector machine model is adopted to perform decision behaviour modelling. Results reveal that there are significant differences in the influencing factors for different lane-changing types and different positions in the UIW segment. The three most important factor types are object vehicle status, current-lane rear vehicle status and target-lane rear vehicle status. The precision of the choice of lane-changing point models is at least 82%. The proposed method could reveal the detailed features of the lane-changing point selection behaviour in the UIW section and also provide a feasible choice of lane-changing point model.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Choice of Lane-Changing Point in an Urban Intertunnel Weaving Section Based on Random Forest and Support Vector Machine

Zhao

Wang

et al. 2023

PROMTT

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“…Utilizing Model Predictive Control (MPC), the LFGC predicts other vehicles' intentions and trajectories, enhancing safety and efficiency in merging. Zhu et al [9] presented a flow-level CAV coordination strategy for multilane freeway merging, incorporating lane-change rules, proactive gap creation, and vehicle platooning to enhance traffic flow and efficiency. Recently, Ji et al [10] developed TriPField, a new three-dimensional potential field model, combining an ellipsoidal potential field with a Gaussian velocity field to refine path-planning for AVs in dynamic, dense settings.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve

Lin

Tsukada

2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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Collision avoidance (CA) has always been the foremost task for autonomous vehicles (AVs) under safety criteria. And path planning is directly responsible for generating a safe path to accomplish CA while satisfying other commands. Due to the real-time computation and simple structure, the potential field (PF) has emerged as one of the mainstream path-planning algorithms. However, the current PF is primarily simulated in ideal CA scenarios, assuming complete obstacle information while disregarding occlusion issues where obstacles can be partially or entirely hidden from the AV's sensors. During the occlusion period, the occluded obstacles do not possess a PF. Once the occlusion is over, these obstacles can generate an instantaneous virtual force that impacts the ego vehicle. Therefore, we propose an occlusion-aware path planning (OAPP) with the responsibility-sensitive safety (RSS)based PF to tackle the occlusion problem for non-connected AVs. We first categorize the detected and occluded obstacles, and then we proceed to the RSS violation check. Finally, we can generate different virtual forces from the PF for occluded and non-occluded obstacles. We compare the proposed OAPP method with other PF-based path planning methods via MATLAB/Simulink. The simulation results indicate that the proposed method can eliminate instantaneous lateral oscillation or sway and produce a smoother path than conventional PF methods.

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“…In addition, the impacts of communication failure (Shi et al, 2022;Wang et al, 2020), number of interacting agents in traffic flow (Chen et al, 2021), falsified information injection (Zhou et al, 2022), and uncertain vehicle dynamics and disturbances from the environment (Wang et al, 2022;Zhou et al, 2022) have been considered in existing studies to enhance the reliability and robustness of CACC operations. Reliable CACC technologies can facilitate the development of cooperative lane changes Zhu et al, 2022), path planning (Huang et al, 2018), automated intersection and roundabout control (Fajardo et al, 2011;Kodupuganti & Pulugurtha, 2023;Mohebifard & Hajbabaie, 2022), managing and monitoring intelligent arterial (Rajput et al, 2022;Yao et al, 2020), and dynamic lane reverse operations (Levin et al, 2019), thereby improving network traffic efficiency and urban mobility under fully connected and autonomous traffic environment in the long-term future. However, in terms of the progression of CAV technologies, before a fully connected and autonomous traffic environment, unconnected human-driven vehicles (HDVs) with no communication capabilities will inevitably coexist with CAVs in the near future, creating a mixed-flow traffic environment.…”

Section: Introductionmentioning

confidence: 99%

Cooperative control of a platoon of connected autonomous vehicles and unconnected human‐driven vehicles

Zhou

Peeta

Wang

2023

Computer aided Civil Eng

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By using kinematic state information obtained through vehicle‐to‐vehicle communications, connected autonomous vehicles (CAVs) can drive cooperatively to alleviate shockwave propagation associated with traffic disturbances. However, during the transition to full autonomy, CAVs and human‐driven vehicles (HDVs) will coexist on the road, creating mixed‐flow traffic. The inherent heterogeneity and randomness in human driving behavior can generate additional disturbances in the traffic flow. Further, HDVs without communication functionality (unconnected HDVs) can cause the control performance of CAVs to degrade by negatively impacting platoon formation. To proactively mitigate the negative impacts of HDVs in mixed‐flow traffic, this study proposes a cooperative control strategy with three components for platoons consisting of CAVs and unconnected HDVs: (i) a number estimator for estimating the number of HDVs between two CAVs, (ii) a kinematic state predictor for predicting the kinematic states of HDVs, and (iii) a multi‐anticipative car‐following controller (i.e., control strategy using kinematic state information of multiple preceding vehicles) for CAVs to maintain string stability and desired time headway. To initialize the proposed strategy, the number estimator is developed using a deep neural network (DNN). Then, a DNN‐based kinematic state predictor predicts the kinematic states of HDVs for CAVs to enable multi‐anticipative car‐following control. The multi‐anticipative car‐following controller is implemented using an extended intelligent driver model‐guided deep deterministic policy gradient algorithm, which ensures safety, string stability, and traffic efficiency. The effectiveness of the proposed control strategy is validated through numerical experiments using NGSIM data. Results indicate that the proposed strategy can produce accurate estimations of the number and the kinematic states of HDVs between CAVs. Further, it can achieve string stability while maintaining smaller time headways, compared to car‐following models used for training guidance under different market penetration rates of CAVs, which significantly improves traffic smoothness and mobility.

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Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas

Cited by 38 publications

References 50 publications

Choice of Lane-Changing Point in an Urban Intertunnel Weaving Section Based on Random Forest and Support Vector Machine

Choice of Lane-Changing Point in an Urban Intertunnel Weaving Section Based on Random Forest and Support Vector Machine

Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve

Cooperative control of a platoon of connected autonomous vehicles and unconnected human‐driven vehicles

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