Cooperative Critical Turning Point-Based Decision-Making and Planning for CAVH Intersection Management System

Li, Shen; Shu, Keqi; Zhou, Yang; Cao, Dongpu; Ran, Bin

doi:10.1109/tits.2021.3099484

Cited by 16 publications

(10 citation statements)

References 36 publications

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“…To improve the traffic efficiency at intersections, Li et.al. introduced the concept of Cooperative Critical Turning Point in the decision framework of Connected Automated Vehicle Highway, in which Partially Observable Markov Decision Process is adopted to handle the uncertainties due to human drivers [8]. Although MVC is expected to perfectly solve the inter-vehicle interaction problem, it requires more intelligent infrastructure and highly connected and automated vehicles.…”

Section: A Literature Reviewmentioning

confidence: 99%

Autonomous Driving Decision Algorithm for Complex Multi-vehicle Interactions: an Efficient Approach Based on Global Sorting and Local Gaming

Li¹,

Zhang²,

Liu³

2023

Preprint

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<p>For autonomous driving, it is important to develop safe and efficient decision algorithms to handle multi-vehicle interactions. Game theory is suitable to manage the interactive driving decision modelling, however, common approaches of multi-player game formulation is computationally complex for dynamic and intense interactions. To overcome this challenge, a global sorting-local gaming framework, namely GLOSO-LOGA, is proposed to solve the intersection interaction problem for autonomous driving, which can comprehensively consider the advantages of multi-vehicle collaboration and single-vehicle intelligence approaches. Further, an interaction disturbance function is used to quantify the impact of indirect interactions on ego vehicle. Then simulations with single and multiple conflict areas settings in a four-armed intersection are carried out. Simulations and human-in-the-loop simulator experiments show that the proposed algorithm can improve the interaction safety in intensively interactive driving scenarios even in complex and urgent interaction cases. The proposed framework may be potentially applied in handling autonomous vehicle's dynamic interactions with multiple road users.</p>

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Section: A Literature Reviewmentioning

confidence: 99%

Autonomous Driving Decision Algorithm for Complex Multi-vehicle Interactions: an Efficient Approach Based on Global Sorting and Local Gaming

Li¹,

Zhang²,

Liu³

2023

Preprint

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“…where y * h is the expected position of HDVs, and it is calculated by the intelligent driver model (IDM) [14].…”

Section: Time-varying Speed Prediction Of Hdvs Based On Integral Term...mentioning

confidence: 99%

“…Such as, the uncertain maneuver of the HDVs based on the driver behavior model was regarded as disturbance, and a receding horizon merging control strategy for ICVs to address the problems of safety and trafc efciency of the mixed trafc merging was proposed [12]. (3) Other methods, such as, an intersection integrated management system was proposed, which used the partially observable Markov decision process (POMDP) modeling method to estimate the driver intention of HDVs, thereby decreasing the uncertainties in decision-making and planning for ICVs, and then, the trafc efciency was enhanced [14]; in addition, a game theory-based decision-making dynamic was developed to achieve more realistic models of human behavior when making conficting maneuvers at intersections, and incorporate it into ICVs' motion planning algorithms and further to improve the trafc efciency [15]. However, the previous studies mainly focus on improving trafc efciency under mixed trafc fows but have not yet considered the comprehensive improvement of trafc efciency and energy-saving for ICVs and HDVs with timevarying speed characteristics under diferent MPRs and trafc volumes.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized Intersections

Luo

Kong

et al. 2023

Journal of Advanced Transportation

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To improve the driving efficiency and energy-saving characteristics for large-scale mixed traffic flows under different market penetration rates (MPRs) of intelligent and connected vehicles (ICVs) at unsignalized intersections, considering the cooperative eco-driving performance between ICVs and human-driven vehicles (HDVs) with time-varying speed characteristics, the hierarchical and distributed cooperative eco-driving architecture is first established in this paper, consisting of a cloud decision layer and a vehicle control layer. For the cloud decision layer, the multivehicle model-free adaptive predictive cooperative driving (MFAPCD) method is designed by using only the driving data of the HDVs and ICVs formation based on compact form dynamic linearization (CFDL) technique, thereby improving traffic efficiency. Furthermore, the CFDL integral terminal sliding mode predictive control (CFDL-ITSMPC) scheme is utilized to predict the time-varying driving speed of HDVs, and then, the CFDL predictive control (CFDL-PC) scheme is utilized to predict the expected control variables of ICVs formation. For the vehicle control layer, based on the anticipated driving speed obtained from the cloud decision layer, the nonlinear distributed model predictive control (NDMPC) method is utilized for distributed optimal control of each vehicle formation, to achieve optimization in terms of energy saving. Simulation results show that, compared with the signal time assignment strategy, the method can increase the average velocity by about 15.22% and decrease the average fuel consumption by about 36.43% under different MPRs and traffic volumes.

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“…Meanwhile, supported by V2I communication, CAVs are precisely cooperative controlled with infrastructure. More recent studies have increased signalized intersections efficiency by cooperative control CAVs with information from signal timings (Li et al, 2021;Qian et al, 2021) and optimize the trajectory in single-lane, single-phase scenarios (Dong et al, 2021;Jiang et al, 2017;Li et al, 2018;. For example, Qian et al (2021) propose an algorithm that optimized departure time, travel route, and longitudinal trajectories of CAVs and signal timings to reduce queues spillover in multi-intersection segments.…”

Section: Literature Reviewmentioning

confidence: 99%

Rolling‐horizon–based strategy of fully cooperative traffic under signalized intersections

Yao

Zhou

Xia

et al. 2022

Computer aided Civil Eng

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Dedicated left-turn lanes are traditionally used at intersections. This practice may not be optimal where heavy traffic exists from multiple directions. As is well known, the capacity can be increased if vehicles are grouped in the same direction in advance, but the additional infrastructure is usually needed, such as presignal systems. Fortunately, under the development of connected and automated vehicle (CAV) technology, the presignal system can be achieved by coordinating vehicles directly. To this end, we establish a modeling framework and management strategies for pure CAV traffic to achieve direction-based grouping. A rolling time-horizon-based sorting strategy is developed to achieve real-time coordination under uncertain vehicle arrivals. A series of numerical experiments have shown that the improvement of the proposed sorting strategy is significant and robust compared with traditional dedicated left-turn lane intersections without coordination, especially when the traffic is busy.

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Cooperative Critical Turning Point-Based Decision-Making and Planning for CAVH Intersection Management System

Cited by 16 publications

References 36 publications

Autonomous Driving Decision Algorithm for Complex Multi-vehicle Interactions: an Efficient Approach Based on Global Sorting and Local Gaming

Autonomous Driving Decision Algorithm for Complex Multi-vehicle Interactions: an Efficient Approach Based on Global Sorting and Local Gaming

Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized Intersections

Rolling‐horizon–based strategy of fully cooperative traffic under signalized intersections

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