A Coevolutionary Algorithm for Cooperative Platoon Formation of Connected and Automated Vehicles

Deng, Zhiyun; Fan, Jiaxin; Shi, Yufeng; Shen, Weiming

doi:10.1109/tvt.2022.3196366

Cited by 21 publications

(9 citation statements)

References 56 publications

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“…The bi-level optimization framework optimizes traffic operation within a rolling horizon to balance traffic performance and computational efficiency. At each decision step, the optimal scheduling scheme at the upper level can be solved by the CPLEX Constraint Programming solver with sub-second computation time, while the nearoptimal trajectories in the lower level can be generated by the coevolutionary algorithm modified from our previous work [63], [64]. In addition, this paper establishes the quantifiable connection between the makespan of the traffic scheduling scheme and the occurrence of spillbacks, demonstrating that the optimization of the makespan helps to avoid/mitigate spillbacks in normal/saturated traffic states.…”

Section: Discussionmentioning

confidence: 99%

“…In this section, we first revisit the trajectory planning method developed in our previous studies [63], [64], where a Hybrid Evolutionary Algorithm with Cooperative Coevolution (HEA-CC) is proposed and proved to be efficient to deal with the multivehicle trajectory optimization. Furthermore, we formulate a new objective function to fit the setting of this problem, which not only reduces the overall energy consumption of vehicles but also improves the usage of road space to avoid/mitigate spillbacks.…”

Section: Revisit Of the Lower-level Trajectory Planning Modelmentioning

confidence: 99%

“…i denotes the displacement of the front bumper of vehicle i ∈ [1, n k ] at time t ξ , P i (t ξ ) denotes the instantaneous power, For the second term, g c (s t ξ i ) denotes the penalty function with the corresponding weight coefficient σ c , where g 1 (•) measures the violation of traffic regulations, g 2 (•) the mechanical limitation, g 3 (•) the comfort criteria, and g 4 (•) the separation distance. We refer readers to read [63] for the calculation of these energy and penalty functions.…”

Section: Revisit Of the Lower-level Trajectory Planning Modelmentioning

confidence: 99%

See 2 more Smart Citations

Cooperative Platoon Formation of Connected and Autonomous Vehicles: Towards Efficient Merging Coordination at Unsignalized Intersections

Deng¹,

Yang²,

Shen³

et al. 2023

Preprint

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<p>This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles (CAVs) at unsignalized intersections. The constraint-coupled bi-level optimization is operated with a limited view of incoming traffic using the rolling horizon procedure to reduce computational complexity. In each decision step, the platoon formation scheme is incorporated into an upper-level traffic scheduling model as decision variables to pursue an optimal schedule from a systemic view. Meanwhile, the passing sequence and timeslots of vehicles are jointly optimized with the platoon configuration scheme by virtue of real-time traffic states to improve operational efficiency and fairness. After that, a lower-level trajectory planning model will generate dynamically-feasible and energy-efficient trajectories according to the given schedule and coupling constraints with the objective of improving space utilization to prevent spillbacks. Moreover, the quantifiable connection between the makespan of traffic scheduling schemes and the occurrence of spillbacks is established while demonstrating that the cooperative platoon formation strategy is effective in avoiding and mitigating spillbacks in normal and saturated traffic states. Additionally, the proposed algorithm can be extended to mixed traffic scenarios with high penetration market rate of CAVs based on the assumption of tracking errors of Human Driven Vehicles (HDVs) trajectories. Numerical experiments are conducted on extensive scenarios with different traffic densities, where the Constraint Programming (CP) technique is used to produce the optimal schedule. Experimental results indicate the superiority of the proposed approach in optimality and stability with reasonable sub-second computation time for real-life applications.</p>

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Section: Discussionmentioning

confidence: 99%

Section: Revisit Of the Lower-level Trajectory Planning Modelmentioning

confidence: 99%

Section: Revisit Of the Lower-level Trajectory Planning Modelmentioning

confidence: 99%

See 1 more Smart Citation

Cooperative Platoon Formation of Connected and Autonomous Vehicles: Towards Efficient Merging Coordination at Unsignalized Intersections

Deng¹,

Yang²,

Shen³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In this section, we first revisit the trajectory planning method developed in our previous studies [57], [58], where a Hybrid Evolutionary Algorithm with Cooperative Coevolution (HEA-CC) is proposed and proved to be efficient to deal with the multivehicle trajectory optimization. Furthermore, we formulate a new objective function to fit the setting of this problem, which not only reduces the overall energy consumption of vehicles but also improves the usage of road space to avoid/mitigate spillbacks.…”

Section: Revisit Of the Lower-level Trajectory Planning Modelmentioning

confidence: 99%

“…Here, s t ⇠ i denotes the displacement of the front bumper of vehicle i 2 [1, n k ] at time t ⇠ , P i (t ⇠ ) denotes the instantaneous power, For the second term, g c (s t ⇠ i ) denotes the penalty function with the corresponding weight coefficient c , where g 1 (•) measures the violation of traffic regulations, g 2 (•) the mechanical limitation, g 3 (•) the comfort criteria, and g 4 (•) the separation distance. We refer readers to read [57] for the calculation of these energy and penalty functions.…”

Section: Revisit Of the Lower-level Trajectory Planning Modelmentioning

confidence: 99%

Cooperative Platoon Formation of Connected and Autonomous Vehicles: Toward Efficient Merging Coordination at Unsignalized Intersections

Deng

Yang

Shen

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles (CAVs) at unsignalized intersections. The constraint-coupled bi-level optimization is operated with a limited view of incoming traffic using the rolling horizon procedure to reduce computational complexity. In each decision step, the platoon formation scheme is incorporated into an upperlevel traffic scheduling model as decision variables to pursue an optimal schedule from a systemic view. Meanwhile, the passing sequence and timeslots of vehicles are jointly optimized with the platoon configuration scheme by virtue of real-time traffic states to improve operational efficiency and fairness. After that, a lower-level trajectory planning model will generate dynamicallyfeasible and energy-efficient trajectories according to the given schedule and coupling constraints with the objective of improving space utilization to prevent spillbacks. Moreover, the quantifiable connection between the makespan of traffic scheduling schemes and the occurrence of spillbacks is established while demonstrating that the cooperative platoon formation strategy is effective in avoiding and mitigating spillbacks in normal and saturated traffic states. Additionally, the proposed algorithm can be extended to mixed traffic scenarios with high penetration market rate of CAVs based on the assumption of tracking errors of Human Driven Vehicles (HDVs) trajectories. Numerical experiments are conducted on extensive scenarios with different traffic densities, where the Constraint Programming (CP) technique is used to produce the optimal schedule. Experimental results indicate the superiority of the proposed approach in optimality and stability with reasonable sub-second computation time for real-life applications.

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From da Vinci to cybersecurity: tracing the evolution of autonomous vehicles and ensuring safe platooning operations

Hussein

2024

Discov Mechanical Engineering

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Since Leonardo da Vinci’s creation of a self-propelled cart in the 1500s (Palmer. in Significant figures in world history p. 75--7, 2018), the evolution of Autonomous Vehicles (AVs) has aimed to revolutionize transportation. While AVs promise improved safety, traffic efficiency, and industrial optimization by reducing human intervention, ensuring their security remains paramount. This paper provides a thorough literature review spanning from historical milestones to contemporary advancements in AV technology. It delves into the significance of Vehicular Ad-hoc NETworks (VANETs) for safety applications and underscores the critical role of speed harmonization and string stability in safeguarding AV platoons. Furthermore, the paper addresses cybersecurity threats targeting platoon networks, advocating for research into encryption mechanisms, road-side units, control algorithms, hybrid communications, and on-board system security to bolster communication security within platoons. By advocating for a balance between AV technological advancements and robust security measures, this paper facilitates safe and reliable AV platooning operations.

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A Coevolutionary Algorithm for Cooperative Platoon Formation of Connected and Automated Vehicles

Cited by 21 publications

References 56 publications

Cooperative Platoon Formation of Connected and Autonomous Vehicles: Towards Efficient Merging Coordination at Unsignalized Intersections

Cooperative Platoon Formation of Connected and Autonomous Vehicles: Towards Efficient Merging Coordination at Unsignalized Intersections

Cooperative Platoon Formation of Connected and Autonomous Vehicles: Toward Efficient Merging Coordination at Unsignalized Intersections

From da Vinci to cybersecurity: tracing the evolution of autonomous vehicles and ensuring safe platooning operations

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