Lane density optimisation of automated vehicles for highway congestion control

Ramezani, Mohsen; Ye, Eric

doi:10.1080/21680566.2019.1568925

Cited by 25 publications

(29 citation statements)

References 34 publications

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“…In this phase, the Adapting Planner is proposed as a tool that executes the following steps to plan a change in the traffic light timer: The Adapting Planner saves, in a variable, the text retrieved from the previous phase file. The Adapting Planner performs the following operation [ 21 – 25 ]: “d” is the value read in step 1. “tCross” is the average time a vehicle takes to pass the crossing.…”

Section: The Methodsmentioning

confidence: 99%

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RETRACTED CHAPTER: Optimization of Driving Efficiency for Pre-determined Routes: Proactive Vehicle Traffic Control

Viloria

Lezama

Varela

et al. 2020

Communications in Computer and Information Science

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With the excessive growth of modern cities, great problems are generated in citizen administration. One of these problems is the control of vehicle flow during peak hours. This paper proposes a solution to the problem of vehicle control through a proactive approach based on Machine Learning. Through this solution, a traffic control system learns about traffic flow in order to prevent future problems of long queues at traffic lights. The architecture of the traffic system is based on the principles of Autonomous Computing with the aim of changing the traffic light timers automatically. A simulation of the roads in an intelligent city and a Weka-based tool were created to validate this approach.

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Section: The Methodsmentioning

confidence: 99%

“…The Adapting Planner performs the following operation [ 21 – 25 ]: “d” is the value read in step 1. “tCross” is the average time a vehicle takes to pass the crossing.…”

Section: The Methodsmentioning

confidence: 99%

RETRACTED CHAPTER: Optimization of Driving Efficiency for Pre-determined Routes: Proactive Vehicle Traffic Control

Viloria

Lezama

Varela

et al. 2020

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…However, to tackle the problem complexity, a number of simplifying assumptions were typically made. Other works focused on computing lane-changing commands at vehicle level, which are obtained by sensing the surrounding environment [13], [16]- [21]. The intrinsic complexity of some of these approaches may be an impediment for real-time application while also considering additional options and features offered by emerging systems.…”

Section: Introductionmentioning

confidence: 99%

Feedback-Based Ramp Metering and Lane-Changing Control With Connected and Automated Vehicles

Tajdari

Roncoli

Papageorgiou

2022

IEEE Trans. Intell. Transport. Syst.

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Aiming at operating effectively future traffic systems, we propose here a novel methodology for integrated lanechanging and ramp metering control that exploits the presence of connected vehicles. In particular, we assume that a percentage of vehicles can receive and implement specific control tasks (e.g., lane-changing commands), while ramp metering is available via an infrastructure-based system or enabled by connected vehicles. The proposed approach is designed to robustly maximise the throughput at motorway bottlenecks employing a feedback controller, formulated as a Linear Quadratic Integral regulator, which is based on a simplified linear time invariant traffic flow model. We also present an extremum seeking algorithm to compute the optimal set-points used in the feedback controller, employing only the measurement of a cost that is representative of the achieved traffic conditions. The method is evaluated via simulation experiments, performed on a first-order, multi-lane, macroscopic traffic flow model, also featuring the capacity drop phenomenon, which allows to demonstrate the effectiveness of the developed methodology and to highlight the improvement in terms of the generated congestion. Index Terms-Traffic control, connected and automated vehicles, lane-changing control, ramp metering.

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“…With many researchers' continuous efforts, the autonomous driving has made great progress in the last few decades [1][2][3][4][5]. Compared with many mature lane-keeping technologies, such as adaptive cruise control (ACC) [6,7], there still is room to improve the lanechanging system.…”

Section: Introductionmentioning

confidence: 99%

Dynamic motion planner with trajectory optimisation for automated highway lane‐changing driving

Liu

Liang

Zhang

2020

IET intell. transp. syst

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This study proposes a dynamic motion planner with trajectory optimisation for automated highway lane-changing driving. Owing to the connected and automated vehicles (CAVs) technology that the real-time traffic information can be obtained, alternative trajectories can be generated to satisfy the vehicle kinematic constraints and avoid many types of potential collisions. An optimal control theory is adopted to select an optimal lane-changing path from the finite path set, and the appropriate acceleration and speed for the execution path are also determined. In order to avoid unnecessary motion replanning process, this study puts forward a collision-avoidance monitoring algorithm to reduce the time consumption costs of the motion planner. Moreover, an online planning framework based on 'decision-execution' is explored. Applying this timeline framework can not only help to evaluate the dynamic planner's online performance, but also reduce the deviation between the online calculation and the actual execution caused by the time consumption. The simulations are performed in PreScan-Simulink platform and the experimental results show that the presented dynamic planner can complete the lane-changing manoeuvre safely and effectively in a high-speed environment.

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Lane density optimisation of automated vehicles for highway congestion control

Cited by 25 publications

References 34 publications

RETRACTED CHAPTER: Optimization of Driving Efficiency for Pre-determined Routes: Proactive Vehicle Traffic Control

RETRACTED CHAPTER: Optimization of Driving Efficiency for Pre-determined Routes: Proactive Vehicle Traffic Control

Feedback-Based Ramp Metering and Lane-Changing Control With Connected and Automated Vehicles

Dynamic motion planner with trajectory optimisation for automated highway lane‐changing driving

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