Novel Decision-Making Strategy for Connected and Autonomous Vehicles in Highway On-Ramp Merging

Kherroubi, Zine El Abidine; Aknine, Samir; Bacha, Rebiha

doi:10.1109/tits.2021.3114983

Cited by 36 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tey generally concentrate on the decision details of each vehicle and accurately update its position and car-following gap distance at each discrete interval. Mainstream lane-changing models include the Cellular Automata Model [17,18], the Gipps model employing multiple factors [19,20], game theory models between target and infuenced vehicles [21,22], the MOBIL model based on safety-incentive dual criteria [23], and artifcial intelligence models represented by fuzzy logic and neural networks [24]. Additionally, some studies have incorporated more realistic factors, such as diferences in driving tendencies based on varying speed profles and the impact of roadway conditions [25,26].…”

Section: Lane Changing Models In Cav Environmentsmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Mandatory Lane-Changing Location Planning for CAV Based on Cell Transmission Model

Gao,

Huang,

et al. 2024

Journal of Advanced Transportation

View full text Add to dashboard Cite

If dedicate a lane to connected autonomous vehicle (CAV) on a multilane road, the traffic congestion and safety risks remain a major problem but in a different style. Random and disorderly mandatory lane-changing behaviour before approaching the next ramp or intersection would have a disturbing effect on the following vehicles of the traffic flow. This paper mainly establishes the optimal mandatory lane-changing location matching model for each target vehicle in the dedicated CAV lane environment. The aim is to minimizing the total travel time, which could take the disturbing effect into account. This model nests the cell transmission model (CTM) to describe vehicle running. The constraints include the relation between target CAV lane-changing cell and the corresponding behaviour start time, the updating of the flow, and occupancy for varied cells. We use the Ant Colony Optimization (ACO) algorithm to solve the problem. Through the case study of a basic two-lane road scenario in Ningbo, we acquire the convergence results based on the ACO algorithm. Our optimal lane-changing location matching scheme can save 5.9% total travel time when compared to the near-end location lane-changing scheme. We test our model by increasing the total number of upstream input vehicles with 4%, 11%, 15%, and the mandatory lane-changing vehicles with 60%, 200%, respectively. The testing results prove that out optimization method could deal with varied road traffic flow situations. Specifically, when the traffics and mandatory lane-changing vehicles increase, our method could perform better.

show abstract

Section: Lane Changing Models In Cav Environmentsmentioning

confidence: 99%

“…Te initial position sequences of the expansion MLC vehicles. , 143, 138, 133, 127, 120, 111, 105, 100, 94, 87, 81, 76, 70 1, 2, 4,5,7,9,11,15,17,18,21,22,23,24,27 …”

mentioning

confidence: 99%

Optimal Mandatory Lane-Changing Location Planning for CAV Based on Cell Transmission Model

Gao,

Huang,

et al. 2024

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Kherroubi et al. [57] developed an Artificial Neural Network (ANN) to predict the intentions of HDVs within the sensing range of the ego‐vehicle. This prediction further served as an input state to train a Deep Reinforcement Learning (DRL) agent, which aims to learn a safe and cooperative merging strategy for CAVs.…”

Section: Related Workmentioning

confidence: 99%

“…The highlights of this simulation framework are: (i) the CAVs are optimally coordinated to minimize fuel consumption, and (ii) the interaction of CAVs with HDVs under different traffic demands is captured. Kherroubi et al [57] developed an Artificial Neural Network (ANN) to predict the intentions of HDVs within the sensing range of the egovehicle. This prediction further served as an input state to train a Deep Reinforcement Learning (DRL) agent, which aims to learn a safe and cooperative merging strategy for CAVs.…”

Section: Cav-based Merging Control In the Mixed Traffic Environmentmentioning

confidence: 99%

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

Han

Zhang

Guo

2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

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.

show abstract

“…For overtaking scenarios, a method based on artificial potential field method combined with formation control [22] and a cooperative avoidance scheme based on distance estimation strategy [23] are applied to keep vehicles safe. In addition, to deal with on-ramp coordination, artificial neural network (ANN) combined with Deep Reinforcement Learning (DRL) were proposed to calculate longitudinal acceleration [24], Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) were integrated to realize decentralized control [25] and the SAT (Satisfiability) solver [26] were tested with good results. However, most of the above methods are designed for specific traffic scenes and cannot be applied to generic multivehicle interaction environment.…”

Section: B Multi-vehicle Coordinated Motion Planningmentioning

confidence: 99%

Multi-Vehicle Coordinated Motion Planning Based on Interactive Primitive Tree

Wang

Zhang

et al. 2021

2021 IEEE International Conference on Unmanned Systems (ICUS)

View full text Add to dashboard Cite

Multi-vehicle coordinated motion planning has always been challenged to safely and efficiently resolve conflicts under non-holonomic dynamic constraints. Constructing spatialtemporal corridors for multi-vehicle can decouple the highdimensional conflicts and further reduce the difficulty of obtaining feasible trajectories. Therefore, this paper proposes a novel hierarchical method based on interactive spatio-temporal corridors (ISTCs). In the first layer, based on the initial guidance trajectories, Mixed Integer Quadratic Programming is designed to construct ISTCs capable of resolving conflicts in generic multivehicle scenarios. And then in the second layer, Non-Linear Programming is settled to generate in-corridor trajectories that satisfy the vehicle dynamics. By introducing ISTCs, the multivehicle coordinated motion planning problem is able to be decoupled into single-vehicle trajectory optimization problems, which greatly decentralizes the computational pressure and has great potential for real-world applications. Besides, the proposed method searches for feasible solutions in the 3-D (x, y, t) configuration space, preserving more possibilities than the traditional velocity-path decoupling method. Simulated experiments in unsignalized intersection and challenging dense scenarios have been conduced to verify the feasibility and adaptability of the proposed framework.

show abstract

Novel Decision-Making Strategy for Connected and Autonomous Vehicles in Highway On-Ramp Merging

Cited by 36 publications

References 23 publications

Optimal Mandatory Lane-Changing Location Planning for CAV Based on Cell Transmission Model

Optimal Mandatory Lane-Changing Location Planning for CAV Based on Cell Transmission Model

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

Multi-Vehicle Coordinated Motion Planning Based on Interactive Primitive Tree

Contact Info

Product

Resources

About