AurigaNet: A Real-Time Multi-Task Network for Enhanced Urban Driving Perception

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 driving conditions. Key innovations of AurigaNet include its end-to-end instance segmentation capability, which significantly enhances both accuracy and efficiency in path estimation for autonomous vehicles. Experimental results demonstrate that AurigaNet achieves an 85.2% IoU in drivable area segmentation, outperforming its closest competitor by 0.7%. In lane detection, AurigaNet achieves a remarkable 60.8% IoU, surpassing other models by more than 30%. Furthermore, the network achieves an mAP@0.5:0.95 of 47.6% in traffic object detection, exceeding the next leading model by 2.9%. Additionally, we validate the practical feasibility of AurigaNet by deploying it on embedded devices such as the Jetson Orin NX, where it demonstrates competitive real-time performance. These results underscore AurigaNet's potential as a robust and efficient solution for autonomous driving perception systems. The code can be found here https://github.com/KiaRational/AurigaNet.