Conference proceeding
Camera-Based Lane Marking Detection for ADAS and Autonomous Driving
IMAGE ANALYSIS AND RECOGNITION (ICIAR 2015), v 9164, pp 514-519
01 Jan 2015
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Advanced driver assistance systems (ADAS) and autonomous driving (AD) have increasingly gainedmore attention in automotive industries and road safety research. Several sensors such asRadar, LiDAR, GPS, ultrasonic sensors and cameras are often embedded in modern vehicles to facilitate ADAS and AD applications. The data obtained from these sensors can often be used in combination with machine learning models to create an empirical approach for ADAS vision tasks such as lane detection (LD). In this paper we survey recent techniques and approaches in vision-based lane marking detection for ADAS systems. We introduce a benchmark dataset and initial lane marking detection results using probabilistic Hough transform.
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Details
- Title
- Camera-Based Lane Marking Detection for ADAS and Autonomous Driving
- Creators
- Yasamin Alkhorshid - Chemnitz University of TechnologyKamelia Aryafar - Drexel UniversityGerd Wanielik - Chemnitz University of TechnologyAli Shokoufandeh - Drexel University
- Contributors
- M Kamel (Editor)A Campilho (Editor)
- Publication Details
- IMAGE ANALYSIS AND RECOGNITION (ICIAR 2015), v 9164, pp 514-519
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000364183400057
- Scopus ID
- 2-s2.0-84984629814
- Other Identifier
- 991019168070804721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods
- Mathematical & Computational Biology
- Robotics