This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
GPU Implementation for Automatic Lane Tracking in Self-Driving Cars
Technical Paper
2019-01-0680
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
The development of efficient algorithms has been the focus of automobile engineers since self-driving cars become popular. This is due to the potential benefits we can get from self-driving cars and how they can improve safety on our roads. Despite the good promises that come with self-driving cars development, it is way behind being a perfect system because of the complexity of our environment. A self-driving car must understand its environment before it makes decisions on how to navigate, and this might be difficult because the changes in our environment is non-deterministic. With the development of computer vision, some key problems in intelligent driving have been active research areas. The advances made in the field of artificial intelligence made it possible for researchers to try solving these problems with artificial intelligence. Lane detection and tracking is one of the critical problems that need to be effectively implemented. The ability of a self-driving car to successfully drive from point A to point B without going off track is dependent on lane tracking. Lane tracking in self-driving cars is a computationally intensive task and a fast implementation is needed to help a self-driving car track lanes in real-time to make the right decision at the right time. Lane tracking in self-driving cars is also dependent on the visibility of lane markings on the road. It will be difficult for a self-driving car to track lanes if the lane marking has faded, blocked by an object, or there were no lane markings on the road. Most available lane tracking implementations in the literature do not give account to these two problems. Our implementation is to solve these two problems by using artificial intelligence techniques to track lanes in all conditions and using GPU computing on NVIDIA Jetson TX2 to speed-up the process.
Recommended Content
Topic
Citation
Yusuf, A. and Alawneh, S., "GPU Implementation for Automatic Lane Tracking in Self-Driving Cars," SAE Technical Paper 2019-01-0680, 2019, https://doi.org/10.4271/2019-01-0680.Also In
References
- New York Times 2017 https://www.nytimes.com/2017/02/15/business/highway-traffic-safety.html
- US Department of Transportation 2008 https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811059
- SBF Lawyers 2017 https://www.sbflawyers.com/5-most-common-causes-of-car-accidents/
- Guan , H. , Xingang , W. , and Wenqi , W. Real-Time Lane-Vehicle Detection and Tracking System 2016 Chinese Control and Decision Conference (CCDC) Yinchuan 2016
- Davies , A. 2015 https://www.wired.com/2015/05/oh-look-evidence-humans-shouldnt-driving/
- Vanderbilt , T. Why Do We Drive the Way We Do (and What It Says about Us) New York Alfred A. Knopf 2008
- World Health Organization Global Status Report on Road Safety Switzerland WHO Press 2015
- Lex , F. , Brown , D.E. , Glazer , M. , Angell , W. et al. 2017 https://arxiv.org/pdf/1711.06976.pdf
- Buehler , M. , Lagnemma , K. , and Singh , S. The DARPA Urban Challenge Cambridge Springer 2009
- DOrrier , J. 2014 https://singularityhub.com/2014/01/16/is-the-ihs-forecast-of-54-million-self-driving-cars-by-2035-too-conservative/
- Reportlinker 2015 https://www.prnewswire.com/news-releases/autonomous-vehicles-advanced-driver-assistance-systems-and-the-evolution-of-self-driving-functionality-global-market-analysis-and-forecasts-300139489.html
- D. C, R. SH, H. B and J. X Identifying the Factors Contributing to the Severity of Truck-Involved Crashes International Journal of Injury Control and Safety Promotion 22 2 116 126 2015
- Zhao , K. , Meuter , M. , Nunn , C. , Muller , D. et al. A Novel Multi-Lane Detection and Tracking System 2012 IEEE Intelligent Vehicles Symposium Alcada de Henares 2012
- Houston Chronicle 2012 https://www.chron.com/opinion/editorials/article/Get-ready-for-automated-cars-3857472.php
- Urmson , C. and Whittaker , W. Self-Driving Cars and the Urban Challenge IEEE Intelligent Systems 23 2 66 68 2008
- Watermark https://www.watermarkcommunities.com/driverless-cars-exciting-opportunity-or-futuristic-dream/ 2018
- Gao , Q. , Feng , Y. , and Wang , L. A Real-Time Lane Detection and Tracking Algorithm 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Chengdu 2018
- Lee D.-K. , Shin J.-S. , Jung J.-H. , Park S.-J. et al. Real-Time Lane Detection and Tracking System Using Simple Filter and Kalman Filter 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN) Milan 2017
- Lee , C. and Moon , J.-H. Robust Lane Detection and Tracking for Real-Time Applications IEEE Transactions on Intelligent Transportation Systems 99 1 6 2018
- Abdulhakam , A.M. , Assidiq , O.O. , Khalifa , M.R. , Islam et al. Real Time Lane Detection for Autonomous Vehicles International Conference on Computer and Communication Engineering Kuala Lumpur 2008
- Bounini , F. , Gingras , D. , Lapointe , V. , and Pollart , H. Autonomous Vehicle and Real Time Road Lanes Detection and Tracking IEEE Vehicle Power and Propulsion Conference (VPPC) Montreal 2015
- DeviceHive 2017 https://towardsdatascience.com/how-to-traine-tensorflow-models-79426dabd304
- 2018 https://elinux.org/Jetson_TX2
- Wikipedia 2018 https://en.wikipedia.org/wiki/Gaussian_blur
- Wikipedia 2018 https://en.wikipedia.org/wiki/Canny_edge_detector
- Wikipedia 2018 https://en.wikipedia.org/wiki/Hough_transform
- Wikipedia 2018 https://en.wikipedia.org/wiki/Deep_learning
- Kim , Z.W. Robust Lane Detection and Tracking in Challenging Scenarios IEEE Transactions on Intelligent Transportation Systems 9 1