AurigaNet: A Real-Time Multi-Task Network for Enhanced Urban Driving Perception
Kiarash Ghasemzadeh, Sedigheh Dehghani

TL;DR
AurigaNet is a multi-task neural network designed for real-time urban driving perception, integrating object detection, lane detection, and drivable area segmentation, achieving high accuracy and efficiency on embedded devices.
Contribution
The paper introduces AurigaNet, a novel end-to-end multi-task network that advances autonomous driving perception with superior accuracy and real-time performance on embedded hardware.
Findings
Achieves 85.2% IoU in drivable area segmentation.
Surpasses state-of-the-art in lane detection with 60.8% IoU.
Demonstrates real-time deployment on Jetson Orin NX.
Abstract
Self-driving cars hold significant potential to reduce traffic accidents, alleviate congestion, and enhance urban mobility. However, developing reliable AI systems for autonomous vehicles remains a substantial challenge. Over the past decade, multi-task learning has emerged as a powerful approach to address complex problems in driving perception. Multi-task networks offer several advantages, including increased computational efficiency, real-time processing capabilities, optimized resource utilization, and improved generalization. In this study, we present AurigaNet, an advanced multi-task network architecture designed to push the boundaries of autonomous driving perception. AurigaNet integrates three critical tasks: object detection, lane detection, and drivable area instance segmentation. The system is trained and evaluated using the BDD100K dataset, renowned for its diversity in…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Advanced Neural Network Applications · Adversarial Robustness in Machine Learning
