Tesla robotaxi seen with no one driving https://t.co/pwLPIdmw5C ...

作者 | LiangWang 编辑 | 自动驾驶之心 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 >>点击进入→ 自动驾驶之心 『4D标注』技术交流群 本文只做学术分享，如有侵权，联系删文 近几年随着深度学习技术的发展，基于数据驱动的算法方案在自动驾驶/机器人领域逐渐成为主流，因此算法对数据的要求也越来越大。区别于传统单帧标注，基 于高精点云地图的4D标注方案能够有效减少标注成本并提高数据真值质量。 4D标注中的4D是指三维空间+时间维度，4D数据能够映射到任意时刻得到单帧真值用于模型训练，区别于大范围高精地图生产，4D标注只关注一小片区域的静态 和动态元素。然而如何生成标注所需底图是其中的一个关键环节，针对不同的标注需求，通常需要实现"单趟建图"，"多躺建图"和"重定位"等关键技术，在场景上 还需要支持有GNSS的行车场景和无GNSS的泊车场景。 LSD (LiDAR SLAM & Detection) 是一个开源的面向自动驾驶/机器人的环境感知算法框架，能够完成数据采集回放、多传感器标定、SLAM建图定位和障碍物检测 等多种感知任务。 本文将详细介 ...

Nvidia (NVDA 0.33%) generated more than $44 billion in revenue during its fiscal 2026 first quarter (which ended April 27), with $39.1 billion of that total coming from its data center business alone. The company sells the most powerful graphics processing units (GPUs) in the world for data centers, and they are the chips every artificial intelligence (AI) developer wants to use.Nvidia CEO Jensen Huang thinks AI data center spending will top $1 trillion per year by 2028, but he's eyeing another trillion-dol ...

Robotaxi is ready to kick ass and take the wheel https://t.co/m0Wdwbg24g ...

RT Dirty Tesla (@DirtyTesLa)Robotaxi pulls over for an ambulance 🤩🤩 https://t.co/QFdLggMiUy ...

RT Tesla Owners Silicon Valley (@teslaownersSV)My first ever Tesla Robotaxi drive@tesla @Tesla_AI @Tesla_Optimus@HansCNelson riding with me.The future is autonomous. https://t.co/kPLUYajsZG ...

RT Tesla Owners Silicon Valley (@teslaownersSV)My first ever Tesla Robotaxi drive@tesla @Tesla_AI @Tesla_Optimus@HansCNelson riding with me.The future is autonomous. https://t.co/kPLUYajsZG ...

My first ever Tesla Robotaxi drive@tesla @Tesla_AI @Tesla_Optimus@HansCNelson riding with me.The future is autonomous. https://t.co/kPLUYajsZG ...

Tesla robotaxi drive https://t.co/mQavfZp4SE ...

Core Viewpoint - The article emphasizes the growing importance of autonomous driving technology and highlights the availability of over 100 high-quality datasets for developers and researchers in the field. It introduces five key datasets that cover various tasks from perception to visual odometry, providing valuable resources for both beginners and experienced engineers [2]. Dataset Summaries 1. KITTI Dataset - The KITTI dataset is one of the most classic and widely used benchmark datasets in the autonomous driving field. It was collected in Karlsruhe, Germany, using high-precision sensors such as stereo color/gray cameras, Velodyne 3D LiDAR, and GPS/IMU. The dataset includes annotations for various perception tasks, including stereo vision, optical flow, visual odometry, and 3D object detection and tracking, making it a standard for evaluating vehicle vision algorithms [3]. 2. nuScenes Dataset - nuScenes is a large-scale multi-sensor dataset released by Motional, covering 1,000 continuous driving scenes in Boston and Singapore, totaling approximately 15 hours of data. It includes a full suite of sensors: six cameras, five millimeter-wave radars, one top-mounted LiDAR, and IMU/GPS. The dataset provides around 1.4 million high-resolution camera images and 390,000 LiDAR scans, annotated with 3D bounding boxes for 23 object categories, making it suitable for research on complex urban road scenarios [5][7]. 3. Waymo Open Dataset - The Waymo Open Dataset, released by Google Waymo, is one of the largest open data resources for autonomous driving. It consists of two main parts: a perception dataset with 2,030 scenes of high-resolution camera and LiDAR data, and a motion dataset with 103,354 vehicle trajectories and corresponding 3D map information. This extensive multi-sensor dataset covers various times, weather conditions, and urban environments, serving as a benchmark for target detection, tracking, and trajectory prediction research [10][12]. 4. PathTrack Dataset - PathTrack is a dataset focused on person tracking, containing over 15,000 trajectories across 720 sequences. It utilizes a re-trained existing person matching network, significantly reducing the classification error rate. The dataset is suitable for 2D/3D object detection, tracking, and trajectory prediction tasks [13][14][15]. 5. ApolloScape Dataset - ApolloScape, released by Baidu Apollo, is a massive autonomous driving dataset characterized by its large volume and high annotation accuracy. It reportedly exceeds similar datasets in size by over ten times, containing hundreds of thousands of high-resolution images with pixel-level semantic segmentation annotations. ApolloScape defines 26 different semantic categories and includes complex road scenarios, making it applicable for perception, map construction, and simulation training [17][19].