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- Title
SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System.
- Authors
Abdou, Mohammed; Kamal, Hanan Ahmed
- Abstract
Currently, deep learning and IoT collaboration is heavily invading automotive applications especially in autonomous driving throughout successful assistance functionalities. Crash avoidance, path planning, and automatic emergency braking are essential functionalities for autonomous driving. Trigger-action-based IoT platforms are widely used due to its simplicity and ability of doing receptive tasks accurately. In this work, we propose SDC-Net system: an end-to-end deep learning IoT hybrid system in which a multitask neural network is trained based on different input representations from a camera-cocoon setup installed in CARLA simulator. We build our benchmark dataset covering different scenarios and corner cases that the vehicle may expose in order to navigate safely and robustly while testing. The proposed system aims to output relevant control actions for crash avoidance, path planning and automatic emergency braking. Multitask learning with a bird's eye view input representation outperforms the nearest representation in precision, recall, f1-score, accuracy, and average MSE by more than 11.62%, 9.43%, 10.53%, 6%, and 25.84%, respectively.
- Subjects
DRIVERLESS cars; HYBRID systems; DEEP learning; AUTONOMOUS vehicles; COCOONS; BLENDED learning
- Publication
Sensors (14248220), 2022, Vol 22, Issue 23, p9108
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s22239108