Autonomous Vehicles and Machines最新文献_第2页

The design and validation of a rain model for a simulated automotive environment 模拟汽车环境下降雨模型的设计与验证

Autonomous Vehicles and Machines Pub Date : 2023-01-16 DOI: 10.2352/ei.2023.35.16.avm-116

T. Brophy, B. Deegan, J. Salado, A. Tena, Patrick Denny, M. Glavin, Enda Ward, J. Horgan, E. Jones

引用次数: 0

How much depth information can radar contribute to a depth estimation model? 雷达能为深度估计模型提供多少深度信息?

Autonomous Vehicles and Machines Pub Date : 2022-02-26 DOI: 10.2352/EI.2023.35.16.AVM-122

Chen-Chou Lo, P. Vandewalle

{"title":"How much depth information can radar contribute to a depth estimation model?","authors":"Chen-Chou Lo, P. Vandewalle","doi":"10.2352/EI.2023.35.16.AVM-122","DOIUrl":"https://doi.org/10.2352/EI.2023.35.16.AVM-122","url":null,"abstract":"Recently, several works have proposed fusing radar data as an additional perceptual signal into monocular depth estimation models because radar data is robust against varying light and weather conditions. Although improved performances were reported in prior works, it is still hard to tell how much depth information radar can contribute to a depth estimation model. In this paper, we propose radar inference and supervision experiments to investigate the intrinsic depth potential of radar data using state-of-the-art depth estimation models on the nuScenes dataset. In the inference experiment, the model predicts depth by taking only radar as input to demonstrate the inference capability using radar data. In the supervision experiment, a monocular depth estimation model is trained under radar supervision to show the intrinsic depth information that radar can contribute. Our experiments demonstrate that the model using only sparse radar as input can detect the shape of surroundings to a certain extent in the predicted depth. Furthermore, the monocular depth estimation model supervised by preprocessed radar achieves a good performance compared to the baseline model trained with sparse lidar supervision.","PeriodicalId":177462,"journal":{"name":"Autonomous Vehicles and Machines","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121519799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A review of IEEE P2020 noise metrics IEEE P2020噪声指标综述

Autonomous Vehicles and Machines Pub Date : 2022-01-16 DOI: 10.2352/ei.2022.34.16.avm-109

O. Skorka, Paul Romanczyk

引用次数: 2

Efficient in-cabin monitoring solution using TI TDA2PxSOCs 采用TI tda2pxsoc的高效舱内监控解决方案

Autonomous Vehicles and Machines Pub Date : 2022-01-16 DOI: 10.2352/ei.2022.34.16.avm-116

Mangla Mayank, Mihir Mody, Chitnis Kedar, Goswami Piyali, Pande Tarkesh, D. Shashank, Jagannathan Shyam, Haas Stefan, Hua Gang, G. Hrushikesh, Kumar Desappan, Shankar Prithvi, N. Niraj

引用次数: 1

Spatial precision and recall indices to assess the performance of instance segmentation algorithms 空间精度和召回率指标评价实例分割算法的性能

Autonomous Vehicles and Machines Pub Date : 2022-01-16 DOI: 10.2352/ei.2022.34.16.avm-101

Mattis Brummel, Patrick Müller, Alexander Braun

{"title":"Spatial precision and recall indices to assess the performance of instance segmentation algorithms","authors":"Mattis Brummel, Patrick Müller, Alexander Braun","doi":"10.2352/ei.2022.34.16.avm-101","DOIUrl":"https://doi.org/10.2352/ei.2022.34.16.avm-101","url":null,"abstract":"Since it is essential for Computer Vision systems to reliably perform in safety-critical applications such as autonomous vehicles, there is a need to evaluate their robustness to naturally occurring image perturbations. More specifically, the performance of Computer Vision systems needs to be linked to the image quality, which hasn’t received much research attention so far. In fact, aberrations of a camera system are always spatially variable over the Field of View, which may influence the performance of Computer Vision systems dependent on the degree of local aberrations. Therefore, the goal is to evaluate the performance of Computer Vision systems under effects of defocus by taking into account the spatial domain. Large-scale Autonomous Driving datasets are degraded by a parameterized optical model to simulate driving scenes under physically realistic effects of defocus. Using standard evaluation metrics, the Spatial Recall Index (SRI) and the new Spatial Precision Index (SPI), the performance of Computer Visions systems on these degraded datasets are compared with the optical performance of the applied optical model. A correlation could be observed between the spatially varying optical performance and the spatial performance of Instance Segmentation systems.","PeriodicalId":177462,"journal":{"name":"Autonomous Vehicles and Machines","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115265319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time LIDAR imaging by solid-state single chip beam scanner 固态单片波束扫描仪实时激光雷达成像

Autonomous Vehicles and Machines Pub Date : 2022-01-16 DOI: 10.2352/ei.2022.34.16.avm-172

Jisan Lee, Kyunghyun Son, Chang-Bum Lee, Inoh Hwang, Bongyong Jang, Eunkyung Lee, Dongshik Shim, H. Byun, C. Shin, Tatsuhiro Otsuka, Yongchul Cho, Kyoungho Ha, H. Choo

引用次数: 1

Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving 自动驾驶多任务视觉感知的对抗性攻击

Autonomous Vehicles and Machines Pub Date : 2021-07-15 DOI: 10.2352/j.imagingsci.technol.2021.65.6.060408

Ibrahim Sobh, Ahmed Hamed, V. Kumar, S. Yogamani

引用次数: 10

AVM Conference Overview and Papers Program AVM会议概述和论文计划

Autonomous Vehicles and Machines Pub Date : 2021-01-18 DOI: 10.2352/issn.2470-1173.2021.17.avm-a17

引用次数: 0

Single Chip Auto-Valet Parking System with TDA4VMID SoC 基于TDA4VMID SoC的单芯片自动代客泊车系统

Autonomous Vehicles and Machines Pub Date : 2021-01-18 DOI: 10.2352/issn.2470-1173.2021.17.avm-113

Mihir Mody, Kedar Chitnis, H. Hariyani, Shyam Jagannathan, Jason Jones, G. Shurtz, Abhishek Shankar, Ankur, Mayank Mangla, Sriramakrishnan Govindarajan, Aish Dubey, K. Chirca

{"title":"Single Chip Auto-Valet Parking System with TDA4VMID SoC","authors":"Mihir Mody, Kedar Chitnis, H. Hariyani, Shyam Jagannathan, Jason Jones, G. Shurtz, Abhishek Shankar, Ankur, Mayank Mangla, Sriramakrishnan Govindarajan, Aish Dubey, K. Chirca","doi":"10.2352/issn.2470-1173.2021.17.avm-113","DOIUrl":"https://doi.org/10.2352/issn.2470-1173.2021.17.avm-113","url":null,"abstract":"\u0000 Auto-Valet parking is a key emerging function for Advanced Driver Assistance Systems (ADAS) enhancing traditional surround view system providing more autonomy during parking scenario. Auto-Valet parking system is typically built using multiple HW components e.g. ISP, micro-controllers,\u0000 FPGAs, GPU, Ethernet/PCIe switch etc. Texas Instrument’s new Jacinto7 platform is one of industry’s highest integrated SoC replacing these components with a single TDA4VMID chip. The TDA4VMID SoC can concurrently do analytics (traditional computer vision as well as deep learning)\u0000 and sophisticated 3D surround view, making it a cost effective and power optimized solution. TDA4VMID is a truly heterogeneous architecture and it can be programmed using an efficient and easy to use OpenVX based middle-ware framework to realize distribution of software components across cores.\u0000 This paper explains typical functions for analytics and 3D surround view in auto-valet parking system with data-flow and its mapping to multiple cores of TDA4VMID SoC. Auto-valet parking system can be realized on TDA4VMID SOC with complete processing offloaded of host ARM to the rest of SoC\u0000 cores, providing ample headroom for customers for future proofing as well as ability to add customer specific differentiation.\u0000","PeriodicalId":177462,"journal":{"name":"Autonomous Vehicles and Machines","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114578796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FisheyeDistanceNet++: Self-Supervised Fisheye Distance Estimation with Self-Attention, Robust Loss Function and Camera View Generalization fishheyedistancenet++:自关注、鲁棒损失函数和相机视图泛化的自监督鱼眼距离估计

Autonomous Vehicles and Machines Pub Date : 2021-01-18 DOI: 10.2352/issn.2470-1173.2021.17.avm-181

V. Kumar, S. Yogamani, Stefan Milz, Patrick Mäder

{"title":"FisheyeDistanceNet++: Self-Supervised Fisheye Distance Estimation with Self-Attention, Robust Loss Function and Camera View Generalization","authors":"V. Kumar, S. Yogamani, Stefan Milz, Patrick Mäder","doi":"10.2352/issn.2470-1173.2021.17.avm-181","DOIUrl":"https://doi.org/10.2352/issn.2470-1173.2021.17.avm-181","url":null,"abstract":"\u0000 FisheyeDistanceNet [1] proposed a self-supervised monocular depth estimation method for fisheye cameras with a large field of view (> 180°). To achieve scale-invariant depth estimation, FisheyeDistanceNet supervises depth map predictions over multiple scales during training.\u0000 To overcome this bottleneck, we incorporate self-attention layers and robust loss function [2] to FisheyeDistanceNet. A general adaptive robust loss function helps obtain sharp depth maps without a need to train over multiple scales and allows us to learn hyperparameters in loss function to\u0000 aid in better optimization in terms of convergence speed and accuracy. We also ablate the importance of Instance Normalization over Batch Normalization in the network architecture. Finally, we generalize the network to be invariant to camera views by training multiple perspectives using front,\u0000 rear, and side cameras. Proposed algorithm improvements, FisheyeDistanceNet++, result in 30% relative improvement in RMSE while reducing the training time by 25% on the WoodScape dataset. We also obtain state-of-the-art results on the KITTI dataset, in comparison to other self-supervised monocular\u0000 methods.\u0000","PeriodicalId":177462,"journal":{"name":"Autonomous Vehicles and Machines","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130618135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10