The advanced driver-assistance system (ADAS) is designed to help drivers while they are driving. To help the drivers, ADAS first comprehends the situation by analyzing the data obtained from the road surroundings. In this process, the road boundary is one of the most important targets to detect for safe driving, but is frequently misdetected on crowded roads. Therefore, a new method for robustly detecting road boundaries on crowded roads is presented in this paper. First, road-boundary detection using a standard Hough transform is described, and its limitations are shown. Second, the cause of the limitations is explained by the measurement model of a laser scanner. Then, the standard Hough transform is modified to reflect the measurement model of the laser scanner; this change reduces the effect of closed obstacles. Finally, the proposed method is tested in the real-world environment, and it shows better performance than previous works in crowded environments.
|Number of pages||8|
|Journal||International Journal of Fuzzy Logic and Intelligent Systems|
|Publication status||Published - 2017|
Bibliographical noteFunding Information:
This work was supported by the Hyundai Motor Company.
© The Korean Institute of Intelligent Systems.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Science Applications
- Computational Theory and Mathematics
- Artificial Intelligence