Enhancing Autonomous Vehicle Navigation by Detecting Lane and Objects based on LaneNet and CustomYOLOv5 - Jayamani SIDDAIYAN; Kumar PONNUSAMY (Promet (Zagreb))
Lane and object detection are the major concerns of an autonomous vehicle’s ability to move continuously without creating any traffic congestion or collisions. Highly populated rural and urban roads are still facing many challenges to enabling the intelligent transport system with an end-to-end customer connectivity. The proposed work is to identify the drivable space by combining lane line detection by using the LaneNet with sliding window and front road object detection and using the customised YOLOv5. The appropriate pre-processing methods are carried out to reduce the computational complexity and speed up the process. Followed by pre-processing, the reference line is assumed at the far-end distance from the host vehicle to identify the driving space. The lane line borders and objects bounding box coordinate intersecting points on the reference line are picked up to calculate the drivable space. Finally, the proposed system is validated on various public and own datasets. Lane line detection and object detection accuracy of 97% and 98%, respectively are achieved by the LaneNet with sliding windows and custom YOLOv5.