用于自动驾驶汽车和机器人的先进成像感测器

Advanced Imaging Sensors for Autonomous Vehicles and Robotics Sectors

出版日期: 2025年10月28日 | 出版商:

Frost & Sullivan | 英文 47 Pages | 商品交期: 最快1-2个工作天内

价格

简介目录

下一代成像：从相机到高光谱遥测和事件驱动感测器

先进成像感测器市场正快速成长，这主要得益于技术的快速发展和消费者需求的不断增长。随着各行业越来越重视舒适性、便利性、安全性和效率，全球自动驾驶汽车和机器人感测器市场也蓬勃发展。

全球各国政府和监管机构正在实施自主移动和机器人平台感知系统的最低性能标准。

本报告分析了支援自主驾驶的关键成像感测器技术，重点关注可见光相机、光达、雷达、热成像（短波红外线和长波红外线）、事件驱动视觉和高光谱遥测感测器，以及量子成像和神经形态成像等新兴技术。该报告还探讨了感测器融合、数据处理演算法、特斯拉和Waymo等行业领先企业的实际部署案例以及先进的机器人平台。

本研究探讨的关键问题包括：哪些先进的影像感测器技术正在推动自动驾驶汽车和机器人平台的发展？近期哪些创新正在提升各种成像感测器的效能？人工智慧、边缘处理和神经形态架构如何影响下一代感测器的设计？感测器融合将在提升实际环境中的感知精度、延迟和可靠性方面发挥怎样的作用？汽车和机器人应用场景对成像感测器的要求有何不同？

智慧视觉：整合光达、雷达和热感测器
影像感测器基础知识
先进成像感测器类型
雷达感测器研发的未来展望
多感测器整合可提高自主系统的可靠性
多感测器资料整合：滤波和深度学习
感测器融合与感知系统案例研究
自动驾驶汽车应用：主动式车距维持定速系统
自动驾驶车辆应用：车道侦测与偏离预警系统
自动驾驶汽车应用：停车辅助和自动停车系统
自动驾驶车辆应用：障碍物侦测与碰撞避免
机器人应用：自主移动机器人（AMR）导航
机器人技术应用：作物监测
机器人应用：仓储自动化与物流
利用智慧机器人进行料箱拣选与分类
新兴科技与未来趋势
下一代影像感测器的发展蓝图

成长机会领域

增长机会1：用于ADAS和自动驾驶汽车的先进摄影机系统
成长机会2：神经形态或事件驱动型视觉感测器
成长机会3：热成像或红外线成像感测器

附录

技术成熟度等级（TRL）解释

下一步

成长机会带来的益处和影响
下一步
免责声明

简介目录

Product Code: DB26

Next-Generation Imaging: From Cameras to Hyperspectral and Event-Based Sensors

The advanced imaging sensors market is growing rapidly, driven by fast-paced innovations and increasing consumer demand. As industries place greater emphasis on comfort, convenience, safety, and efficiency, the global sensor market for autonomous vehicles and robotics is experiencing exponential growth.

Governments and regulatory bodies across regions are implementing minimum performance standards for perception systems in autonomous mobility and robotic platforms.

This report provides an analysis of the critical imaging sensor technologies that power autonomy. It focuses on visible cameras, LiDAR, RADAR, thermal imaging (SWIR & LWIR), event-based vision, hyperspectral sensors, and emerging technologies like quantum and neuromorphic imaging. The report also explores sensor fusion, data processing algorithms, and real-world deployments by industry leaders such as Tesla, Waymo, and advanced robotics platforms.

Key Questions the Study Addressed: What are the leading advanced imaging sensor technologies enabling autonomous vehicles and robotic platforms? What recent innovations have improved sensor capabilities across different imaging sensors? How are AI, edge processing, and neuromorphic architectures influencing next-generation sensor design? What role does sensor fusion play in improving perception accuracy, latency, and reliability in real-world environments? How do imaging sensor requirements differ across automotive and robotics use cases?

Strategic Imperatives

Why Is It Increasingly Difficult to Grow? The Strategic Imperative 8: Factors Creating Pressure on Growth
The Strategic Imperative 8
The Impact of the Top 3 Strategic Imperatives on Automotive & Robotics Industry
Growth Opportunities Fuel the Growth Pipeline Engine
Research Methodology

Advanced Imaging Sensors for Autonomous Vehicles and Robotics Sectors-Growth Opportunity Analysis

Scope of Analysis
Segmentation

Growth Generator

Growth Drivers
Growth Restraints

Sensor Technologies

Smart Vision: Integrating LiDAR, RADAR, and Thermal Sensor
Fundamentals of Imaging Sensors
Types of Advanced Imaging Sensors
Future Directions of Radar Sensor R&D
Multi-Sensor Integration for Autonomous System Reliability
Integrating Multi-Sensor Data: Filtering and Deep Learning
Sensor Fusion and Perception Systems Case Studies
Applications in Autonomous Vehicles: Adaptive Cruise Control
Applications in Autonomous Vehicles: Lane Detection and Departure Warning Systems
Applications in Autonomous Vehicles: Parking Assistance and Automated Parking Systems
Applications in Autonomous Vehicles: Obstacle Detection and Collision Avoidance
Applications in Robotics: Autonomous Mobile Robot AMR Navigation
Applications in Robotics: Agricultural Crop Monitoring
Applications in Robotics: Warehouse Automation and Logistics
Smart Robotics for Bin Picking and Sorting
Emerging Technologies and Future Trends
Roadmap for Next-Generation Imaging Sensors