Integrated LiDAR system for autonomous vehicles

适用于自动驾驶汽车的集成激光雷达系统

• 批准号: 534012-2018
• 负责人: Ye, Winnie
• 金额: $ 2.91万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=698426
• 关键词: Integrated; LiDAR; system; autonomous; vehicles

LiDAR (Light Detection and Ranging), is a surveying method that measures distance to a target by illuminating the target with pulsed laser light and detects the reflected pulses with a sensor. If costs can be lowered and functionality improved, the market for LiDAR in automobiles and unmanned vehicle is expected to surpass USD$2.5 Billion by 2030. An approach to this challenge is to create "fast agile" LiDAR platforms based on photonic integrated circuits, integrated with advanced Artificial Intelligence (AI) systems. Such systems could support modules that would "attend to" objects of importance in the field of view allowing for high-resolution identification of critical items. LiDAR will not be simply a passive information gathering element but an actively controlled part of an integrated sensing aware AI platform. This grant has two overarching goals. Firstly, it proposes to generate a viable PIC prototype for the implementation of a "fast agile" LiDAR with the necessary functionality to enable an "attention module" within an AI infrastructure. Secondly, this project proposes to develop a sophisticated simulation platform for PIC based LiDAR. The proposed research will form a foundation for the complete system level design of the LiDAR PIC. The first goal will improve the functionality of the LiDAR by employing depletion-based phase shifter arrays and subwavelength grating couplers to improve beam steering range, resolution and scanning speed. The second goal will be complimentary to the first by providing analysis and optimization capabilities. This work will address the multi-physics and multi-scale issues present in LIDAR design and optimization, dealing with systems by incorporating electrical, optical, semi-conductor and thermal devices and effects. The impact of the physical layer on system design and constraints will also be addressed. The simulation work will build on the existing simulation frameworks of OptiSPICE and Atar. Experimental verification of the modeling using prototype devices are anticipated. Two prototype generations are expected by the end of the CRD program.

LiDAR（光探测和测距）是一种测量方法，通过照明测量到目标的距离 用脉冲激光照射目标，并用传感器检测反射的脉冲。如果能降低成本， 随着功能的改善，汽车和无人驾驶汽车的LiDAR市场预计将超过 到2030年达到25亿美元。应对这一挑战的一种方法是创建基于以下内容的“快速敏捷”LiDAR平台： 光子集成电路，与先进的人工智能（AI）系统集成。这样的系统可以 支持模块将“关注”视野中的重要物体， 确定关键项目。LiDAR将不仅仅是一个被动的信息收集元素， 集成感知AI平台的主动控制部分。 这项赠款有两个首要目标。首先，它提出了一个可行的PIC原型， 实施具有必要功能的“快速敏捷”激光雷达，以启用内部的“注意力模块” AI基础设施。其次，本项目提出了开发一个先进的PIC仿真平台 基于LiDAR本文的研究成果将为整个系统级的设计奠定基础。 激光雷达图片。 第一个目标将通过采用基于耗尽的移相器阵列来改善LiDAR的功能 和亚波长光栅耦合器以提高光束控制范围、分辨率和扫描速度。的 第二个目标将通过提供分析和优化能力来补充第一个目标。这项工作 将解决激光雷达设计和优化中存在的多物理场和多尺度问题， 通过整合电、光、半导体和热器件和效应，的影响 物理层对系统设计的影响和限制也将得到解决。模拟工作将建立在 现有的OptiSPICE和Atar仿真框架。模型的实验验证， 原型装置是预期的。两代原型预计在CRD计划结束时完成。