| Title |
Investigating Drivable Space Instance Segmentation for Connected and Autonomous Vehicles |
| ID_Doc |
41293 |
| Authors |
Mistry, V; Rinchen, S; Vaidya, B; Mouftah, HT |
| Title |
Investigating Drivable Space Instance Segmentation for Connected and Autonomous Vehicles |
| Year |
2022 |
| Published |
|
| DOI |
10.1109/IWCMC55113.2022.9824992 |
| Abstract |
Connected and Autonomous Vehicles (CAVs) are vehicles that provide connectivity between other vehicles (V2V), infrastructure (V2I) and any things (V2X) using various communication technologies. Deploying CAVs can make transportation safer, improve mobility and provide benefits to the Smart city environment. For autonomous driving, lane detection/segmentation is one of important tasks, and changing lanes is one of the crucial driving decisions. This paper exclusively investigates drivable space segmentation and state-of-the-art deep learning model for instance segmentation. The results show that the selected Mask R-CNN model accurately detects and segments direct lane and alternative lanes with high confidence score. |
| Author Keywords |
Connected and Autonomous Vehicles; Drivable space; Instance segmentation; Mask R-CNN; Smart city environment |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:001058917600076 |
| WoS Category |
Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Computer Science; Engineering; Telecommunications |
| PDF |
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