Abstract
Advanced driver assistance systems (ADAS) and autonomous driving (AD) have increasingly gained more attention in automotive industries and road safety research. Several sensors such as Radar, 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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The ROI is often in front of the vehicle which contain fallacious information and mostly occurs in the bottom half of image.
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Alkhorshid, Y., Aryafar, K., Wanielik, G., Shokoufandeh, A. (2015). Camera-Based Lane Marking Detection for ADAS and Autonomous Driving. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_57
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DOI: https://doi.org/10.1007/978-3-319-20801-5_57
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