A bi-level cooperative driving strategy allowing lane changes

Xu, Huile; Zhang, Yi; Cassandras, Christos G.; Li, Li; Feng, Shuo

doi:10.1016/j.trc.2020.102773

Cited by 76 publications

(35 citation statements)

References 51 publications

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“…Nevertheless, the paper only considers a game with two players, and the intersection set-up is very simplistic. The authors of [34] approach the lightless intersection topic by addressing the general problem of trajectory planning in conflict areas. The authors developed a bi-level cooperative strategy where the upper-level provides solutions for the right of way of vehicles that do not perform lane changing.…”

Section: Previous Related Workmentioning

confidence: 99%

A Pairing Algorithm for Conflict-Free Crossings of Automated Vehicles at Lightless Intersections

2021

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This paper studies the planning of conflict-free and efficient crossings of antagonistic vehicles’ movements at lightless intersections. A fully automated infrastructure environment is considered, where all vehicles that enter the intersection area are connected and automated (CAVs), i.e., they are equipped with advanced communication and automation technologies. In such a futuristic environment, traffic lights that regulate the right-of-way of different traffic streams are obsolete because of vehicle communication capabilities. The connectivity is utilized to derive vehicle trajectories such that a safe and efficient crossing of lightless intersections is possible. So far, published studies lack the application to complex intersection layouts. To fill this gap, we introduce a control method for CAV pairing allowing for the safe, collision-free crossing of the intersecting area and optimize traffic conditions, i.e., total delays of the system. Simulation results demonstrate the feasibility and applicability of the presented approach, given that all the technical specifications (e.g., communications, velocity actuators) are present. Finally, we conduct a sensitivity analysis for the algorithm’s main parameters, which provides practical insights for the studied experimental scenarios and other existing algorithms in the literature that tackle this problem.

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Section: Previous Related Workmentioning

confidence: 99%

A Pairing Algorithm for Conflict-Free Crossings of Automated Vehicles at Lightless Intersections

2021

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“…Relevant studies pointed out that the key to solving the problem is determining the right-of-way for CAVs approaching the merging area [22][23][24]. In other words, the vehicles can be formulated as a passing sequence in the form of arrays, and the performance of the schedule strategy hinges on the way to generate the best passing order among a large number of possible solutions.…”

Section: Introductionmentioning

confidence: 99%

A Multiobjective Cooperative Driving Framework Based on Evolutionary Algorithm and Multitask Learning

Jiang

Zhang

et al. 2022

Journal of Advanced Transportation

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The development of connected and automated vehicle (CAV) techniques brings an upcoming revolution to traffic management. The control of CAVs in potential conflict areas such as on-ramps and intersections will be complex to traffic management when considering their deployment. There is still a lack of a general framework for dispatching CAVs in these bottlenecks, which is expected to ensure safety, traffic efficiency, and energy consumption in real time. This study aimed to fill the technique gap, and a comprehensive cooperative intelligent driving framework is put forward to study the problem, which can be used in both on-ramp and intersection scenarios. Based on a multi-objective evolutionary algorithm, CAVs are denoted as a sequence to be searched in solution space, while a multitask learning neural network with adaptive loss function is implemented for optimization target feedback to surrogate the simulation test procedure. The simulation results show that the proposed framework can get satisfying performance with low time and energy consumption. It can reduce time consumption by up to 16.51% for the on-ramp scenario and 9.8% for the intersection scenario, while reducing energy consumption by up to 16.39% and 11.39% for the two scenarios. Meanwhile, an analysis of computation time is carried out, illuminating the flexibility and controllability of the new strategy.

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“…Compared with single-vehicle automated control that considers objective of only the controlled vehicle, multi-vehicle coordination gathers information from vehicles and performs global optimization. Typical scenarios of multivehicle control include single-lane platooning [1]- [3], coordinated lane changing [4]- [6], conflict resolution at ramps and bottlenecks [7]- [9], and scheduling at intersections [10]- [12], etc. Existing research reveals that multi-vehicle coordination has great potential to guarantee driving safety, improve traffic efficiency, and reduce energy consumption [13]- [15], compared with single-vehicle control.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Multi-lane Formation Control for Connected and Automated Vehicles in Multiple Scenarios

Cai¹,

Xu²,

Yang³

et al. 2021

Preprint

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Formation control methods of connected and automated vehicles have been proposed to smoothly switch the structure of vehicular formations in different scenarios. In the previous research, simulations are often conducted to verify the performance of formation control methods. This paper presents the experimental results of multi-lane formation control for connected and automated vehicles. The coordinated formation control framework and specific methods utilized for different scenarios are introduced. The details of experimental platform and vehicle control strategy is provided. Simulations and experiments are conducted in different scenarios, and the results indicate that the formation control method is applicable to multiple traffic scenarios and able to improve formationstructure-switching efficiency compared with benchmark methods.

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A bi-level cooperative driving strategy allowing lane changes

Cited by 76 publications

References 51 publications

A Pairing Algorithm for Conflict-Free Crossings of Automated Vehicles at Lightless Intersections

A Pairing Algorithm for Conflict-Free Crossings of Automated Vehicles at Lightless Intersections

A Multiobjective Cooperative Driving Framework Based on Evolutionary Algorithm and Multitask Learning

Experimental Validation of Multi-lane Formation Control for Connected and Automated Vehicles in Multiple Scenarios

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