Cascade drive: a unified deep learning framework for multi-featured detection and control in autonomous electric vehicles on unstructured roadways
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an autonomous electric vehicle (AEV) system for smart cities, utilizing deep learning to navigate challenging roads. The AEV system achieves high accuracy in lane and object detection, making autonomous driving viable for developing economies.
Area Of Science
- Engineering
- Computer Science
- Artificial Intelligence
Background
- Industry 5.0 emphasizes customer-centric development and sustainability.
- Smart cities are rapidly growing, creating opportunities for automated solutions.
- Automation is crucial for sustainable smart city development.
Purpose Of The Study
- To present a sustainable solution for autonomous electric vehicles (AEVs) in smart cities.
- To address the challenges of unstructured roadways in developing countries using AEVs.
- To develop a comprehensive system for driver-free vehicle operation on diverse road conditions.
Main Methods
- Integration of multiple deep learning models in a cascaded architecture.
- Development of a unified framework for simultaneous lane boundary and object detection.
- Implementation on resource-constrained hardware with prioritization of safety features.
Main Results
- Achieved 97.26% accuracy for lane detection (DeepLabv3+).
- Obtained 0.92 mAP for general object detection (YOLOv5).
- Reached 0.83 mAP for pothole detection (YOLOv7).
Conclusions
- The proposed AEV system demonstrates high performance on challenging road conditions.
- The system is capable of handling diverse and unstructured roadways effectively.
- Successful implementation on a custom electric vehicle platform shows commercial viability for developing economies.
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