High Definition Map for Autonomous Driving of NVIDIA

NVIDIA提供了一套端到端的自动驾驶汽车映射系统,利用NVIDIA DRIVE™ PX2 AI超级计算机与数据中心内的NVIDIA Tesla® GPU,加速创建并更新高度详细的地图。此系统将原本耗时数周的过程缩短为接近实时完成,且更加高效地将数据处理转移到车辆内,减少了与云端的通讯。

NVIDIA offers an end-to-end mapping system for self-driving cars, designed to help automakers, map companies and startups to rapidly create HD maps and keep them updated. This process that used to take weeks can now happen in near real-time. For automotive developers, the same architecture used to create maps and keep them up-to-date can also enable self-driving cars.


This state-of-the-art technology uses an NVIDIA DRIVE™ PX 2 AI supercomputer, coupled with NVIDIA Tesla® GPUs in the data center, to accelerate the creation and updating of highly detailed maps for autonomous vehicles. Traditional mapping techniques have required numerous expensive sensors in the car to collect massive volumes of data that were recorded and then processed offline. Conversely, this HD mapping system is highly-efficient, moving data processing data into the vehicle, and minimizing communication with the cloud. The process that used to take weeks can now happen in near real-time.

- See more at: http://www.nvidia.com/object/hd-mapping-system.html#sthash.QQhupowx.dpuf

This state-of-the-art technology uses an NVIDIA DRIVE™ PX 2 AI supercomputer, coupled with NVIDIA Tesla® GPUs in the data center, to accelerate the creation and updating of highly detailed maps for autonomous vehicles. Traditional mapping techniques have required numerous expensive sensors in the car to collect massive volumes of data that were recorded and then processed offline. Conversely, this HD mapping system is highly-efficient, moving data processing data into the vehicle, and minimizing communication with the cloud. The process that used to take weeks can now happen in near real-time.

- See more at: http://www.nvidia.com/object/hd-mapping-system.html#sthash.QQhupowx.dpuf

This state-of-the-art technology uses an NVIDIA DRIVE™ PX 2 AI supercomputer, coupled with NVIDIA Tesla® GPUs in the data center, to accelerate the creation and updating of highly detailed maps for autonomous vehicles. Traditional mapping techniques have required numerous expensive sensors in the car to collect massive volumes of data that were recorded and then processed offline. Conversely, this HD mapping system is highly-efficient, moving data processing data into the vehicle, and minimizing communication with the cloud. The process that used to take weeks can now happen in near real-time.

- See more at: http://www.nvidia.com/object/hd-mapping-system.html#sthash.QQhupowx.dpuf

This state-of-the-art technology uses an NVIDIA DRIVE™ PX 2 AI supercomputer, coupled with NVIDIA Tesla® GPUs in the data center, to accelerate the creation and updating of highly detailed maps for autonomous vehicles. Traditional mapping techniques have required numerous expensive sensors in the car to collect massive volumes of data that were recorded and then processed offline. Conversely, this HD mapping system is highly-efficient, moving data processing data into the vehicle, and minimizing communication with the cloud. The process that used to take weeks can now happen in near real-time.

- See more at: http://www.nvidia.com/object/hd-mapping-system.html#sthash.QQhupowx.dpuf
This state-of-the-art technology uses an NVIDIA DRIVE™ PX 2 AI supercomputer, coupled with NVIDIA Tesla® GPUs in the data center, to accelerate the creation and updating of highly detailed maps for autonomous vehicles. Traditional mapping techniques have required numerous expensive sensors in the car to collect massive volumes of data that were recorded and then processed offline. Conversely, this HD mapping system is highly-efficient, moving data processing data into the vehicle, and minimizing communication with the cloud. The process that used to take weeks can now happen in near real-time.

NVIDIA's open mapping platform is built on the NVIDIA DriveWorks software toolkit for autonomous driving, and designed for carmakers, map companies and startups to accelerate development. When just using cameras, the system incorporates deep learning algorithms to detect lanes, signs and other landmarks, and can be used to both create maps and determine when the environment has changed.

- See more at: http://www.nvidia.com/object/hd-mapping-system.html#sthash.QQhupowx.dpuf

NVIDIA's open mapping platform is built on the NVIDIA DriveWorks software toolkit for autonomous driving, and designed for carmakers, map companies and startups to accelerate development. When just using cameras, the system incorporates deep learning algorithms to detect lanes, signs and other landmarks, and can be used to both create maps and determine when the environment has changed.

- See more at: http://www.nvidia.com/object/hd-mapping-system.html#sthash.QQhupowx.dpuf

NVIDIA's open mapping platform is built on the NVIDIA DriveWorks software toolkit for autonomous driving, and designed for carmakers, map companies and startups to accelerate development. When just using cameras, the system incorporates deep learning algorithms to detect lanes, signs and other landmarks, and can be used to both create maps and determine when the environment has changed.

- See more at: http://www.nvidia.com/object/hd-mapping-system.html#sthash.QQhupowx.dpuf

NVIDIA's open mapping platform is built on the NVIDIA DriveWorks software toolkit for autonomous driving, and designed for carmakers, map companies and startups to accelerate development. When just using cameras, the system incorporates deep learning algorithms to detect lanes, signs and other landmarks, and can be used to both create maps and determine when the environment has changed.

- See more at: http://www.nvidia.com/object/hd-mapping-system.html#sthash.QQhupowx.dpuf

NVIDIA's open mapping platform is built on the NVIDIA DriveWorks software toolkit for autonomous driving, and designed for carmakers, map companies and startups to accelerate development. When just using cameras, the system incorporates deep learning algorithms to detect lanes, signs and other landmarks, and can be used to both create maps and determine when the environment has changed.

For localization, structure-from-motion algorithms enable data from multiple cameras to be converted into detailed 3D mapping information. Combining data from various inertial sensors in the car, along with GPS data and cameras, enables precise positioning of key landmarks.If desired, lidar information can be utilized to create even richer maps with greater detail. A combination of artificial intelligence and VSLAM (Visual Simultaneous Localization and Mapping) handle all stages of map creation.

The end-to-end system is highly customizable, incorporating both an in-vehicle AI supercomputer, and a cloud component based on Tesla GPUs. For automotive developers, this same architecture used to create maps and keep them up-to-date can also enable self-driving cars.

基于遗传算法的新的异构分布式系统任务调度算法研究(Matlab代码实现)内容概要:本文档围绕基于遗传算法的异构分布式系统任务调度算法展开研究,重点介绍了一种结合遗传算法的新颖优化方法,并通过Matlab代码实现验证其在复杂调度问题中的有效性。文中还涵盖了多种智能优化算法在生产调度、经济调度、车间调度、无人机路径规划、微电网优化等领域的应用案例,展示了从理论建模到仿真实现的完整流程。此外,文档系统梳理了智能优化、机器学习、路径规划、电力系统管理等多个科研方向的技术体系与实际应用场景,强调“借力”工具与创新思维在科研中的重要性。; 适合人群:具备一定Matlab编程基础,从事智能优化、自动化、电力系统、控制工程等相关领域研究的研究生及科研人员,尤其适合正在开展调度优化、路径规划或算法改进类课题的研究者; 使用场景及目标:①学习遗传算法及其他智能优化算法(如粒子群、蜣螂优化、NSGA等)在任务调度中的设计与实现;②掌握Matlab/Simulink在科研仿真中的综合应用;③获取多领域(如微电网、无人机、车间调度)的算法复现与创新思路; 阅读建议:建议按目录顺序系统浏览,重点关注算法原理与代码实现的对应关系,结合提供的网盘资源下载完整代码进行调试与复现,同时注重从已有案例中提炼可迁移的科研方法与创新路径。
### 端到端方法在自动驾驶技术中的应用 #### 定义与概述 端到端学习是一种机器学习范式,在这种范式下,整个系统直接从原始输入数据映射到最终输出决策。对于自动驾驶车辆而言,这意味着可以从传感器获取的数据(如摄像头图像、激光雷达信号等)直接预测驾驶命令(转向角、加速/减速)。这种方法简化了传统上需要分别处理环境感知、行为决策和车辆控制三个子任务的过程[^1]。 #### 技术实现方式 目前,大多数成功的端到端模型基于深度神经网络构建,特别是卷积神经网络(CNN),因为CNN擅长于处理高维空间结构化数据,比如来自车载摄像机的视频流。通过大量标注过的训练样本集,这些模型能够自动提取特征并建立从输入到输出之间的复杂关系。然而值得注意的是,尽管深层架构具有强大的表达能力,但在实际部署之前仍需克服许多挑战,包括但不限于安全性验证不足等问题[^2]。 #### 应用现状与发展前景 虽然端到端的方法展示了其潜力,尤其是在模拟环境中取得了令人印象深刻的结果;但是当涉及到真实世界的不确定性以及极端情况下的鲁棒性时,则显得力不从心。因此现阶段主流观点认为完全依靠单一模式可能并不是最优解法——至少短期内如此。未来的研究方向可能会集中在如何更好地融合不同类型的先验知识或者多模态信息来增强系统的可靠性和泛化性能[^4]。 ```python import tensorflow as tf from tensorflow.keras import layers, models def create_end_to_end_model(input_shape=(None, None, 3)): model = models.Sequential([ layers.Conv2D(24, kernel_size=5, strides=(2, 2), activation='relu', input_shape=input_shape), layers.MaxPooling2D(pool_size=(2, 2)), layers.Flatten(), layers.Dense(100, activation='relu'), layers.Dropout(rate=0.5), layers.Dense(1) # Output layer for steering angle prediction ]) return model ```
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