找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Deep Neural Networks and Data for Automated Driving; Robustness, Uncertai Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben Book‘‘‘‘‘‘‘‘ 20

[復(fù)制鏈接]
41#
發(fā)表于 2025-3-28 16:27:12 | 只看該作者
Safety Assurance of?Machine Learning for?Perception Functions to be defined and argued. At the same time, the use of machine learning (ML) functions is increasingly seen as a prerequisite to achieving the necessary levels of perception performance in the complex operating environments of these functions. This inevitably leads to the question of which supporti
42#
發(fā)表于 2025-3-28 21:29:02 | 只看該作者
A Variational Deep Synthesis Approach for?Perception Validationctionality of these systems, specifically in the context of automated driving. The main contributions are the introduction of a generative, parametric description of three-dimensional scenarios in a validation parameter space, and layered scene generation process to reduce the computational effort.
43#
發(fā)表于 2025-3-29 00:10:24 | 只看該作者
The Good and?the?Bad: Using Neuron Coverage as?a?DNN Validation Techniquecode coverage in software testing, has been proposed as one such V&V method. We provide a summary of different neuron coverage variants and their inspiration from traditional software engineering V&V methods. Our first experiment shows that novelty and granularity are important considerations when a
44#
發(fā)表于 2025-3-29 06:11:45 | 只看該作者
45#
發(fā)表于 2025-3-29 08:59:04 | 只看該作者
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetyencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent
46#
發(fā)表于 2025-3-29 13:28:27 | 只看該作者
Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?f training data, architecture, and training are kept separate or independence is trained using special loss functions. Using data from different sensors (realized by up to five 2D projections of the 3D MNIST dataset) in our experiments is more efficiently reducing correlations, however not to an ext
47#
發(fā)表于 2025-3-29 16:16:32 | 只看該作者
48#
發(fā)表于 2025-3-29 21:32:32 | 只看該作者
49#
發(fā)表于 2025-3-30 00:43:46 | 只看該作者
50#
發(fā)表于 2025-3-30 06:03:50 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-12 18:33
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
灌云县| 江源县| 朔州市| 永寿县| 名山县| 镇康县| 马龙县| 新昌县| 柘城县| 乐至县| 潼南县| 黄平县| 五指山市| 获嘉县| 安吉县| 江陵县| 滨海县| 潞城市| 舟曲县| 江津市| 阿瓦提县| 高邑县| 东平县| SHOW| 临高县| 阳原县| 栾川县| 安宁市| 固安县| 县级市| 赣榆县| 琼中| 九江市| 青海省| 环江| 高台县| 崇信县| 德昌县| 淳化县| 仁寿县| 德保县|