RainSD: Rain Style Diversification Module for Image Synthesis Enhancement using Feature-Level Style Distribution

• URL: http://arxiv.org/abs/2401.00460v1
• Date: Sun, 31 Dec 2023 11:30:42 GMT
• Title: RainSD: Rain Style Diversification Module for Image Synthesis Enhancement using Feature-Level Style Distribution
• Authors: Hyeonjae Jeon, Junghyun Seo, Taesoo Kim, Sungho Son, Jungki Lee, Gyeungho Choi, Yongseob Lim
• Abstract summary: This paper presents a synthetic road dataset with sensor blockage generated from real road dataset BDD100K. Using this dataset, the degradation of diverse multi-task networks for autonomous driving has been thoroughly evaluated and analyzed. The tendency of the performance degradation of deep neural network-based perception systems for autonomous vehicle has been analyzed in depth.
• Score: 5.500457283114346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous driving technology nowadays targets to level 4 or beyond, but the researchers are faced with some limitations for developing reliable driving algorithms in diverse challenges. To promote the autonomous vehicles to spread widely, it is important to address safety issues on this technology. Among various safety concerns, the sensor blockage problem by severe weather conditions can be one of the most frequent threats for multi-task learning based perception algorithms during autonomous driving. To handle this problem, the importance of the generation of proper datasets is becoming more significant. In this paper, a synthetic road dataset with sensor blockage generated from real road dataset BDD100K is suggested in the format of BDD100K annotation. Rain streaks for each frame were made by an experimentally established equation and translated utilizing the image-to-image translation network based on style transfer. Using this dataset, the degradation of the diverse multi-task networks for autonomous driving, such as lane detection, driving area segmentation, and traffic object detection, has been thoroughly evaluated and analyzed. The tendency of the performance degradation of deep neural network-based perception systems for autonomous vehicle has been analyzed in depth. Finally, we discuss the limitation and the future directions of the deep neural network-based perception algorithms and autonomous driving dataset generation based on image-to-image translation.

Related papers

• End-to-end Autonomous Driving: Challenges and Frontiers [45.391430626264764]
  We provide a comprehensive analysis of more than 270 papers, covering the motivation, roadmap, methodology, challenges, and future trends in end-to-end autonomous driving. We delve into several critical challenges, including multi-modality, interpretability, causal confusion, robustness, and world models, amongst others. We discuss current advancements in foundation models and visual pre-training, as well as how to incorporate these techniques within the end-to-end driving framework.
  arXiv  Detail & Related papers  (2023-06-29T14:17:24Z)
• Penalty-Based Imitation Learning With Cross Semantics Generation Sensor Fusion for Autonomous Driving [1.2749527861829049]
  In this paper, we provide a penalty-based imitation learning approach to integrate multiple modalities of information. We observe a remarkable increase in the driving score by more than 12% when compared to the state-of-the-art (SOTA) model, InterFuser. Our model achieves this performance enhancement while achieving a 7-fold increase in inference speed and reducing the model size by approximately 30%.
  arXiv  Detail & Related papers  (2023-03-21T14:29:52Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• Exploring Contextual Representation and Multi-Modality for End-to-End Autonomous Driving [58.879758550901364]
  Recent perception systems enhance spatial understanding with sensor fusion but often lack full environmental context. We introduce a framework that integrates three cameras to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation. Our method achieves displacement error by 0.67m in open-loop settings, surpassing current methods by 6.9% on the nuScenes dataset.
  arXiv  Detail & Related papers  (2022-10-13T05:56:20Z)
• SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation [152.60469768559878]
  SHIFT is the largest multi-task synthetic dataset for autonomous driving. It presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density. Our dataset and benchmark toolkit are publicly available at www.vis.xyz/shift.
  arXiv  Detail & Related papers  (2022-06-16T17:59:52Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• One Million Scenes for Autonomous Driving: ONCE Dataset [91.94189514073354]
  We introduce the ONCE dataset for 3D object detection in the autonomous driving scenario. The data is selected from 144 driving hours, which is 20x longer than the largest 3D autonomous driving dataset available. We reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
  arXiv  Detail & Related papers  (2021-06-21T12:28:08Z)
• Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [59.60483620730437]
  We propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention. Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.
  arXiv  Detail & Related papers  (2021-04-19T11:48:13Z)
• Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification [8.007800530105191]
  We present a deep-neural-network architecture, we term D-CRNN, for building high-fidelity representations for driving style. Using CNN, we capture semantic patterns of driver behavior from trajectories. We then find temporal dependencies between these semantic patterns using RNN to encode driving style.
  arXiv  Detail & Related papers  (2021-02-11T04:33:43Z)
• The NEOLIX Open Dataset for Autonomous Driving [1.4091801425319965]
  We present the NEOLIX dataset and its applica-tions in the autonomous driving area. Our dataset includes about 30,000 frames with point cloud la-bels, and more than 600k 3D bounding boxes withannotations.
  arXiv  Detail & Related papers  (2020-11-27T02:27:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.