Improving Ability and Lane Detection of Self-Directed-Car

Authors

  • Rameez Ahmad
  • Imran Khan et. al.,

DOI:

https://doi.org/10.21015/vtse.v8i2.383

Abstract

Driverless vehicles are on the move to announcement by Google, which drove more than 500,000 miles on its original model vehicles and further key automakers specify the prospective enlargement in this region with the capability to convert the transportation infrastructure, enlarge access and convey settlement to variety of user. A few users address the anticipated unfinished convenience of self directed cars by 2020 with accessibility to the community by 2040.Certain trust that self-directed car make necessary to renovate the modern transportation that fundamentally removing coincidences andcleaning uptheroadenvironment.Thisstudyunderstandstheeffectsthat self- driving car orroboticvehicletravel demandsandride schemeislikelytohave,without thetypicalobstaclesthatallowsdetectionof vision basedhardwareandsoftware constructionof SDC (self-directed car) technologyandGold(GenericObstacleLaneDetection) toa knowledge-basedsystemtoexpectthepotentialandconsidertheshape,color, balancein organizedenvironmentwithcoloredlane patternswhichisimplemented by a particlefilter. Thealgorithm is implemented andtestingwereapprovedonroadsandthe consequences show the strength ofthe algorithm to the problemnatural in road location.Driverless vehicles are on the move to announcement by Google, which drove more than 500,000 miles on its original model vehicles and further key automakers specify the prospective enlargement in this region with the capability to convert the transportation infrastructure, enlarge access and convey settlement to variety of user. A few users address the anticipated unfinished convenience of self directed cars by 2020 with accessibility to the community by 2040.Certain trust that self-directed car make necessary to renovate the modern transportation that fundamentally removing coincidences andcleaning uptheroadenvironment.Thisstudyunderstandstheeffectsthat self- driving car orroboticvehicletravel demandsandride schemeislikelytohave,without thetypicalobstaclesthatallowsdetectionof vision basedhardwareandsoftware constructionof SDC (self-directed car) technologyandGold(GenericObstacleLaneDetection) toa knowledge-basedsystemtoexpectthepotentialandconsidertheshape,color, balancein organizedenvironmentwithcoloredlane patternswhichisimplemented by a particlefilter. Thealgorithm is implemented andtestingwereapprovedonroadsandthe consequences show the strength ofthe algorithm to the problemnatural in road location.

Downloads

Published

2015-12-04

How to Cite

Ahmad, R., & Khan et. al., I. (2015). Improving Ability and Lane Detection of Self-Directed-Car. VFAST Transactions on Software Engineering, 3(1), 71–78. https://doi.org/10.21015/vtse.v8i2.383

Issue

Vol. 3 No. 1 (2015): January-December

Section

Articles

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

                                      

This work is licensed under a Creative Commons Attribution  License CC BY