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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
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Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling.

Abdeljalil Abbas-Turki1, Yazan Mualla1, Nicolas Gaud1

  • 1CIAD UMR 7533, Univ. Bourgogne Franche-Comté, UTBM, F-90010 Belfort, France.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

Autonomous intersection management (AIM) optimizes traffic flow by coordinating connected and autonomous vehicles. This approach enhances urban traffic efficiency beyond traditional traffic light systems.

Keywords:
autonomous vehiclecooperative intelligent transport systems (C-ITS)cooperative intersection managementschedulingvirtual platooning

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Area of Science:

  • Transportation Engineering
  • Intelligent Transportation Systems
  • Traffic Management

Background:

  • Urban intersection congestion is a persistent problem, with traditional traffic light systems showing limitations.
  • Advances in traffic signal control have improved safety and efficiency but struggle with growing demand.
  • The advent of connected and autonomous vehicles (CAVs) offers a paradigm shift for intersection management.

Purpose of the Study:

  • To provide a comprehensive overview of advances in autonomous intersection management (AIM).
  • To explore how AIM enhances traffic efficiency by optimizing vehicle motion and access scheduling.
  • To present a novel approach using distributed particle swarm optimization for AIM.

Main Methods:

  • Reviewing existing literature on autonomous intersection management (AIM).
  • Proposing a method to tailor vehicle speed and schedules based on traffic demand.
  • Utilizing distributed particle swarm optimization for intersection control.
  • Employing flow-speed diagrams from traffic engineering to evaluate performance.

Main Results:

  • AIM enables unprecedented traffic performance by optimizing vehicle interactions at intersections.
  • Tailoring vehicle speed and schedules dynamically improves traffic flow.
  • Distributed particle swarm optimization effectively manages traffic demand.
  • Flow-speed diagrams quantify the significant impact of AIM optimizations.

Conclusions:

  • Autonomous intersection management represents a significant advancement over traditional traffic control.
  • AIM offers a promising solution for enhancing urban traffic efficiency and safety.
  • Further research is needed to address current challenges in AIM implementation.