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本文介绍了华为自动驾驶ADS4.0，回顾了ADS发展历程。ADS4.0采用WEWA架构，端到端时延降低50%，通行效率提升20%。感知硬件上，传感器配置丰富，安全系统升级为CAS4.0。功能上有四个配置版本，旗舰版支持高速L3等。问界M9将首批升级。
在之前的博客《自动驾驶---行泊一体（车位到车位功能）量产》中笔者简单介绍了ADS1.0，ADS2.0，ADS3.0的相关信息。
华为 ADS3.0 是于2024年4月24日正式发布。时隔一年，华为的ADS进化了一代，下面帮助读者朋友们回顾下华为ADS发展历程（没有发布1.0）：
2020年：4月13日，德国莱茵TÜV集团向HUAWEIADS颁发了《ISO26262：2018功能安全管理体系认证证书》，这标志着华为ADS研发流程体系通过汽车功能安全最高等级ASILD认证。
2023年：4月16日发布ADS2.0。其算法架构基于BEV+GOD2.0+RCR2.0架构，减少了对高精地图的依赖，硬件上有所调整，降本的同时提升了整体功能的性能和体验。同年，华为智选车业务首款纯电轿跑车型Luceed亮相；问界M5智驾
自动驾驶L4级技术落地：特斯拉、Waymo与华为的路线之争
自动驾驶L4级技术落地：特斯拉、Waymo与华为的路线之争
大模型重塑无人驾驶：技术突破、场景落地与未来挑战
在特斯拉“鬼探头”测试中，该系统对比传统CNN模型，行人轨迹预测误差减少42%，尤其在恶劣天气下（如团雾），多模态大模型通过跨传感器特征互补，将漏检率控制在0.1%以下。更关键的是，大模型能够捕捉传统算法忽略的隐性特征（如路面材质变化对制动距离的影响），为模型训练注入更高维度的信息。针对车端算力限制，黑芝麻智能提出“动态神经架构搜索”方案：云端训练万亿参数基础模型，车端根据硬件配置自动裁剪出百亿参数子模型，在保持90%性能的同时，功耗降低至30W以下。
自动驾驶大模型---Momenta之飞轮大模型
Momenta作为中国领先的自动驾驶企业，在城市NOA市场占据超60%份额。随着与智己汽车等合作落地，Momenta的技术路线在实测中表现优异，市场份额预计2025年将提升至70%。其融合感知与规划的一体化模型，为解决复杂场景下的自动驾驶问题提供了新思路。
微信公众号数据采集实战：历史文章、互动数据与评论详情全解析
本文提供了一套完整的微信公众号数据采集实战方案，详细解析了如何通过接口调用获取公众号的历史文章列表、单篇文章的互动数据以及详细的用户评论。文章从环境准备、核心参数解析到分页逻辑、批量处理和异常处理，提供了可运行的Python代码示例，旨在帮助开发者高效、稳定地构建自动化采集工具，用于竞品分析、内容研究和舆情监控。
VSCode配置C/C++开发环境：从零安装MinGW-w64并运行第一个C程序
在Windows系统上搭建C语言开发环境时，很多初学者混淆编辑器、编译器与调试器的分工，导致配置过程屡屡受挫。理解VSCode作为轻量级编辑器的定位，以及MinGW-w64提供的gcc编译器在源码编译中的核心作用，是顺利完成环境配置的关键。环境变量的正确设置直接影响gcc命令能否全局调用，而C/C++插件则赋予编辑器语法高亮与智能提示能力。通过安装VSCode、配置MinGW-w64、安装插件、汉化界面并运行一个最简单的hello.c程序，就能系统掌握这套开源工具链的完整工作流。无论是用于课程学习、算法练习
中国30米逐年植被净初级生产力（NPP）数据集（1982年至今CASA模型）
1.3 气温、降水、地表太阳辐射数据 气温和降水数据月度空间插值数据是基于全国2400多个站点的日观测数据，在计算各气象要素月值的基础上，基于Anusplin软件插值生成的1982年以来各月度30米分辨率气温和降水栅格数据，插值过程中引入高程作为协变量。气温单位为℃，降水单位为mm。1.2 土地覆盖数据 土地覆盖数据来源于1985 年-2020年全球30米精细地表覆盖动态监测产品，该数据集包含29个地表覆盖类型，进行拼接、裁剪、重分类和重采样处理，获得8期250米分辨率的植被类型数据。
AI上推荐 之 协同过滤
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团体程序设计天梯赛竞赛题--登顶题【L3-039 攀岩】
登顶级 3 道题，每道题 30 分，满分为 90 分。
【异常】CNB提示The bill authorization scope cannot access the current request.
摘要：CNB API返回403错误，表明当前token的授权范围无法访问目标仓库。主要原因是：1) REPO_SLUG填写错误，导致资源地址不匹配；2) CNB_TOKEN未授权目标仓库；3) 账号账单状态异常。解决步骤：先检查REPO_SLUG是否与CNB平台仓库地址完全一致；再确认CNB_TOKEN是否绑定目标仓库且有读取权限；最后检查账号账单是否正常且覆盖目标资源。完成排查后更新配置即可解决此授权问题。
道通无人机信号分析
本文分析了道通无人机及其遥控器的EIRP参数（表1-2），发现不同地区的功率标准存在差异。针对5230MHz图传信号，观测到四帧连续信号结构类似LORA分包传输。脉冲分析（表3）显示固定3.9ms脉宽和1.1ms间隔。频谱异常表现为非矩形窗特征，经分析系多径效应引起的频率选择性衰落所致——不同路径传播导致特定频率出现相长/相消干涉，形成波峰波谷的起伏频谱特征。这种现象与无线信道的多径时延、衰减差异直接相关。
uniapp - 最新实现hbuilder项目运行到ios基座或苹果ios真机设备详细教程，如何在ios苹果手机上真机运行测试，IOS自定义基座在线打包调试（uniapp项目如何在苹果手机上运行调试）
uni-app，苹果，app，ios，设备，自定义基座，运行app到苹果手机，mac电脑，Xcode，hbuilderX，运行ios基座调试Uniapp，无需打包，苹果证书，ipa，运行失败，报错，无法启动，连接失败，出错，详细教程，如何解决这个问题，怎么办，申请iOS证书，运行项目到iOS基座，打包ios自定义基座，在iOS真机上运行，新手小白，超级详细的教程，dcloud如何苹果ios系统真机测试，调试基座失败，真机调试ios app，App基座安装后无法打开问题，真机运行时检测不到手机的问题或者基座安
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
假人有限元模型
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android 蓝牙通讯demo
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