Chapter 31
Lane Detection Using Image Processing
for Driving Assistance
Atzroulnizam Abu, Mohd Rohaimi Mohd Dahalan,
Ahmad Zawawi Jamaluddin, and Dzul Fadhli Hisyam Mat Daut
Abstract The number of traffic accidents and injuries continues to increase year
by year which causes loss of life and assets. The key facts from the World Health
Organization (WHO), approximately 1.35 million people die each year as a result of
road traffic crashes. Under this condition, to reduce the number of deadly accidents,
the lane detection system is applied in an autonomous vehicle to increase the vehicle
safety and passenger safety. The lane detection is one of the computer vision-based
systems for driver assistance, as it uses an existing lane marking where the image is
captured by the onboard camera mounted in the rear-view mirror facing the front of
the vehicle. The proposed algorithm that has been created can detect a lane marking
under variable condition of the road such as in the case of urban roads or highways
with different printed marks as solid or dashed lines. Furthermore, the algorithm is
designed for real-time applications.
Keywords Image processing · Lane detection · Edge detection · Hough
transform · Lane detection
31.1 Introduction
Lane detection is an essential part of the vision-based driver assistance systems of
intelligent vehicles. This driving assistance reduces road accidents, enhances the
A. Abu (B) · M. R. M. Dahalan · A. Z. Jamaluddin · D. F. H. M. Daut
Marine and Electrical Engineering Technology Section, Universiti Kuala Lumpur Malaysian
Institute of Marine Engineering Technology, Lumut, Perak, Malaysia
e-mail: atzroulnizam@unikl.edu.my
M. R. M. Dahalan
e-mail: mrohaimi@unikl.edu.my
A. Z. Jamaluddin
e-mail: azawawi@unikl.edu.my
D. F. H. M. Daut
e-mail: dzulfadhlihisyam@gmail.com
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
A. Ismail et al. (eds.), Advanced Engineering for Processes and Technologies II,
Advanced Structured Materials 147,
https://doi.org/10.1007/978-3-030-67307-9_31
323
Lane Detection Using Image Processing
for Driving Assistance
Atzroulnizam Abu, Mohd Rohaimi Mohd Dahalan,
Ahmad Zawawi Jamaluddin, and Dzul Fadhli Hisyam Mat Daut
Abstract The number of traffic accidents and injuries continues to increase year
by year which causes loss of life and assets. The key facts from the World Health
Organization (WHO), approximately 1.35 million people die each year as a result of
road traffic crashes. Under this condition, to reduce the number of deadly accidents,
the lane detection system is applied in an autonomous vehicle to increase the vehicle
safety and passenger safety. The lane detection is one of the computer vision-based
systems for driver assistance, as it uses an existing lane marking where the image is
captured by the onboard camera mounted in the rear-view mirror facing the front of
the vehicle. The proposed algorithm that has been created can detect a lane marking
under variable condition of the road such as in the case of urban roads or highways
with different printed marks as solid or dashed lines. Furthermore, the algorithm is
designed for real-time applications.
Keywords Image processing · Lane detection · Edge detection · Hough
transform · Lane detection
31.1 Introduction
Lane detection is an essential part of the vision-based driver assistance systems of
intelligent vehicles. This driving assistance reduces road accidents, enhances the
A. Abu (B) · M. R. M. Dahalan · A. Z. Jamaluddin · D. F. H. M. Daut
Marine and Electrical Engineering Technology Section, Universiti Kuala Lumpur Malaysian
Institute of Marine Engineering Technology, Lumut, Perak, Malaysia
e-mail: atzroulnizam@unikl.edu.my
M. R. M. Dahalan
e-mail: mrohaimi@unikl.edu.my
A. Z. Jamaluddin
e-mail: azawawi@unikl.edu.my
D. F. H. M. Daut
e-mail: dzulfadhlihisyam@gmail.com
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
A. Ismail et al. (eds.), Advanced Engineering for Processes and Technologies II,
Advanced Structured Materials 147,
https://doi.org/10.1007/978-3-030-67307-9_31
323