Multi-Objective Evolutionary Optimization for Autonomous Intersection Management

  • Kazi Shah Nawaz RiponEmail author
  • Jostein Solaas
  • Håkon Dissen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10593)


This paper investigates the real-time application of multi-objective evolutionary algorithm (MOEA) for managing traffic at an intersection with its focus on autonomous vehicles. Most of the existing works on intersection management emphasize using MOEAs to optimize parameters for traffic-light based intersections, or they target human drivers. However, the advent of autonomous vehicles has changed the field of intersection management. To maximize the use of autonomous vehicles, the intersections should be autonomous also. This paper proposes an autonomous intersection management (AIM) system that controls the speed for each vehicle approaching at an intersection by using MOEA. The proposed system first looks at splitting the continuous problem of intersection management into smaller independent scenarios. Then it utilizes the MOEA to find solutions for each scenario by optimizing multiple objectives with different goals in terms of overall performance. In order to give the MOEA low level control of traffic at intersections, the autonomous vehicles are modelled as travelling along a predefined path, with a speed determined by the MOEA.


Multi-objective evolutionary optimization Autonomous intersection management Discrete time steps Autonomous vehicle 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kazi Shah Nawaz Ripon
    • 1
    Email author
  • Jostein Solaas
    • 1
  • Håkon Dissen
    • 1
  1. 1.Department of Computer ScienceNorwegian University of Science and TechnologyTrondheimNorway

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