@article{2129,
  author = {Sania Khadim, Shafaq Murtaza, Faisal Riaz},
  title = {An Efficient and Robust Particle Swarm Optimization based Collision Avoidance Scheme for Autonomous Vehicles},
  journal = {Journal of Information Security Research},
  year = {2016},
  volume = {7},
  number = {4},
  doi = {},
  url = {http://www.dline.info/jisr/fulltext/v7n4/jisrv7n4_2.pdf},
  abstract = {This research means to present a novel collision avoidance strategy for autonomous road vehicles utilizing a metaheuristic approach named ‘Particle Swarm Optimization’. In exceedingly dynamic street environment, changes happen most of the time and in order to adapt to these changes vulnerability an exceptionally vigorous and capable calculation is required. Thus, PSO based plan won for being computationally sparing. The proposed PSO based impact evasion plan has been executed utilizing a swarm of 30 particles. PSO gives back an improved choice which can get away from the mischance situation with the exactness of more prominent than 93%. The presented exactness is in correlation with a perfect arrangement which has been acquired from earlier learning of the predetermined area. The strength and productivity of exhibited plan are stamped after its correlation with another collision avoidance technique based on ‘Genetic Algorithm’ which is also a biologically inspired optimization algorithm. The two plans were looked at on the premise of the quality of solutions being produced and required computation time. Re-enactment results have demonstrated the value of PSO construct approach in the light of two presented measurements.},
}