Multi-task Learning for Real-time Autonomous Driving Leveraging Task-adaptive Attention Generator

arXiv:2403.03468v1 Announce Type: new
Abstract: Real-time processing is crucial in autonomous driving systems due to the imperative of instantaneous decision-making and rapid response. In real-world scenarios, autonomous vehicles are continuously tasked with interpreting their surroundings, analyzing intricate sensor data, and making decisions within split seconds to ensure safety through numerous computer vision tasks. In this paper, we present a new real-time multi-task network adept at three vital autonomous driving tasks: monocular 3D object detection, semantic segmentation, and dense depth estimation. …

abstract arxiv attention autonomous autonomous driving autonomous driving systems autonomous vehicles cs.cv data decision decisions driving generator making multi-task learning processing real-time real-time processing safety sensor systems through type vehicles world