Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
The Optical Mouse: Early Biomimetic Embedded Vision Read chapter Read first chapter

2014 | OriginalPaper | Chapter

10. Vision-Based Lane Analysis: Exploration of Issues and Approaches for Embedded Realization

Authors : Ravi Kumar Satzoda, Mohan M. Trivedi

Published in: Advances in Embedded Computer Vision

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Vision-based lane analysis has been investigated to different degrees of completeness. While most studies propose novel lane detection and tracking methods, there is some research on estimating lane-based contextual information using properties and positions of lanes. According to a recent survey of lane estimation in [7], there are still open challenges in terms of reliably detecting lanes in varying road conditions. Lane feature extraction is one of the key computational steps in lane analysis systems. In this paper, we propose a lane feature extraction method, which enables different configurations of embedded solutions that address both accuracy and embedded systems’ constraints. The proposed lane feature extraction process is evaluated in detail using real-world lane data to explore its effectiveness for embedded realization and adaptability to varying contextual information such as lane types and environmental conditions. Accuracy of more than 90 % is obtained during the evaluation of the proposed method using real-world driving data.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Vehicle Detection Onboard Small Unmanned Aircraft
next chapter Distributed Smart Cameras in the Age of Cloud Computing and the Internet-of-Things
Literature
1.
Borkar A, Hayes M, Smith MT (2012) A novel lane detection system with efficient ground truth generation. IEEE Trans Intell Transp Syst 13(1):365–374CrossRef
2.
Cheng H-Y, Jeng B-S, Tseng P-T, Fan K-C (2006) Lane detection with moving vehicles in the traffic scenes. IEEE Trans Intell Transp Syst 7(4):571–582CrossRef
3.
Cheng SY, Trivedi MM (2007) Lane tracking with omnidirectional cameras: algorithms and evaluation. EURASIP J Embed Syst 2007:1–8CrossRef
4.
Doshi A, Morris BT, Trivedi MM (2011) On-road prediction of driver’s intent with multimodal sensory cues. IEEE Pervasive Comput 10(3):22–34CrossRef
5.
Gopalan R, Hong T, Shneier M, Chellappa R (2012) A learning approach towards detection and tracking of lane markings. IEEE Trans Intell Transp Syst 13(3):1088–1098CrossRef
6.
Hautière N, Tarel JP, Aubert D (2007) Towards fog-free in-vehicle vision systems through contrast restoration. In: 2007 IEEE computer society conference on computer vision and pattern recognition (CVPR), pp 1–8
7.
Hillel AB, Lerner R, Levi D, Raz G (2012) Recent progress in road and lane detection: a survey. Mach Vis Appl 23:1159–1175CrossRef
8.
Kim ZW (2008) Robust lane detection and tracking in challenging scenarios. IEEE Trans Intell Transp Syst 9(1):16–26CrossRef
9.
Marzotto R, Zoratti P, Bagni D, Colombari A, Murino V (2010) A real-time versatile roadway path extraction and tracking on an FPGA platform. Comput Vis Image Underst 114(11):1164–1179CrossRef
10.
McCall JC, Trivedi MM (2006) Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Trans Intell Transp Syst 7(1):20–37CrossRef
11.
McCall JC, Trivedi MM, Wipf D (2005) Lane change intent analysis using robust operators and sparse bayesian learning. In: 2005 IEEE conference on computer vision and pattern recognition (CVPR’05)—workshops, vol 3, pp 59–59
12.
Nedevschi S, Popescu V, Danescu R, Marita T, Oniga F (2013) Accurate ego-vehicle global localization at intersections through alignment of visual data with digital map. In: IEEE transactions on intelligent transportation systems, pp 1–15
13.
Sathyanarayana SS, Satzoda RK, Srikanthan T (2009) Exploiting inherent parallelisms for accelerating linear hough transform. IEEE Trans Image Process 18(10):2255–2264CrossRefMathSciNet
14.
Satzoda RK, Sathyanarayana S, Srikanthan T (2010) Hierarchical additive hough transform for lane detection. IEEE Embed Syst Lett 2(2):23–26CrossRef
15.
Shang E, Li J, An X, He H (2011) Lane detection using steerable filters and FPGA-based implementation. In: 2011 sixth international conference on image and graphics, August 2011, pp 908–913
16.
Sivaraman S, Trivedi MM (2013) Integrated lane and vehicle detection, localization, and tracking: a synergistic approach. IEEE Trans Intell Trans Syst 11(4):1–12
17.
Sivaraman S, Trivedi MM (2010) Improved vision-based lane tracker performance using vehicle. In: 2010 IEEE intelligent vehicles symposium, pp 676–681
18.
Stein F (2012) The challenge of putting vision algorithms into a car. In: 2012 IEEE conference on computer vision and pattern recognition workshop on embedded vision, June 2012, pp 89–94
19.
Satzoda RK, Martin S, Ly MV, Gunaratne P, Trivedi MM (2013) Towards automated drive analysis: a multiomdal synergistic approach. In: 2013 IEEE Intelligent Transportation Systems Conference, pp 1912–1916
20.
Satzoda RK, Trivedi MM (2013) Selective salient feature based Lane Analysis. In: 2013 IEEE Intelligent Transportation Systems Conference, pp 1906–1911
21.
Satzoda RK, Trivedi MM (2014) Drive analysis using vehicle dynamics and vision-based Lane Semantics. IEEE Trans Intell Transp Syst (99), In press
22.
Satzoda RK, Trivedi MM (2014) Efficient lane and vehicle detection with integrated synergies (ELVIS). In: 2014 IEEE Computer Vision and Pattern Recognition Workshops on Embedded Vision, pp 708–713
23.
Trivedi MM, Gandhi T, McCall J (2007) Looking-in and looking-out of a vehicle: computer-vision-based enhanced vehicle safety. IEEE Trans Intell Transp Syst 8(1):108–120CrossRef
Metadata
Title
Vision-Based Lane Analysis: Exploration of Issues and Approaches for Embedded Realization
Authors
Ravi Kumar Satzoda
Mohan M. Trivedi
Copyright Year
2014
Book
Advances in Embedded Computer Vision

Print ISBN: 978-3-319-09386-4

Electronic ISBN: 978-3-319-09387-1

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-09387-1_10

Premium Partner

    Image Credits