Integrated LiDAR system for autonomous vehicles

激光雷达（光探测和测距）是一种测量方法，它通过脉冲激光照射目标，并用传感器检测反射脉冲来测量到目标的距离。如果能够降低成本并改善功能，到2030年，汽车和无人驾驶汽车的激光雷达市场预计将超过25亿美元。应对这一挑战的一种方法是创建基于光子集成电路的“快速敏捷”激光雷达平台，并与先进的人工智能（AI）系统集成。这样的系统可以支持“关注”视场中重要对象的模块，从而实现对关键项目的高分辨率识别。激光雷达将不仅仅是一个被动的信息收集元件，而是一个集成传感感知人工智能平台的主动控制部分。这项拨款有两个首要目标。首先，它建议生成一个可行的PIC原型，用于实现具有必要功能的“快速敏捷”激光雷达，以在人工智能基础设施中启用“注意力模块”。其次，本项目提出了一个复杂的基于pic的激光雷达仿真平台。本研究将为雷达PIC的完整系统级设计奠定基础。第一个目标是通过采用基于耗尽的移相器阵列和亚波长光栅耦合器来改善激光雷达的功能，以提高光束转向范围、分辨率和扫描速度。第二个目标将通过提供分析和优化功能来补充第一个目标。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，通过结合电气、光学、半导体和热器件和效应来处理系统。物理层对系统设计和约束的影响也将被解决。仿真工作将建立在OptiSPICE和Atar现有仿真框架的基础上。期望使用原型装置对模型进行实验验证。预计在CRD项目结束时将生产两代原型机。

LiDAR (Light Detection and Ranging), is a surveying method that measures distance to a target by illuminatingthe target with pulsed laser light and detects the reflected pulses with a sensor. If costs can be lowered andfunctionality improved, the market for LiDAR in automobiles and unmanned vehicle is expected to surpassUSD$2.5 Billion by 2030. An approach to this challenge is to create "fast agile" LiDAR platforms based onphotonic integrated circuits, integrated with advanced Artificial Intelligence (AI) systems. Such systems couldsupport modules that would "attend to" objects of importance in the field of view allowing for high-resolutionidentification of critical items. LiDAR will not be simply a passive information gathering element but anactively 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 theimplementation of a "fast agile" LiDAR with the necessary functionality to enable an "attention module" withinan AI infrastructure. Secondly, this project proposes to develop a sophisticated simulation platform for PICbased LiDAR. The proposed research will form a foundation for the complete system level design of theLiDAR PIC.The first goal will improve the functionality of the LiDAR by employing depletion-based phase shifter arraysand subwavelength grating couplers to improve beam steering range, resolution and scanning speed. Thesecond goal will be complimentary to the first by providing analysis and optimization capabilities. This workwill address the multi-physics and multi-scale issues present in LIDAR design and optimization, dealing withsystems by incorporating electrical, optical, semi-conductor and thermal devices and effects. The impact of thephysical layer on system design and constraints will also be addressed. The simulation work will build on theexisting simulation frameworks of OptiSPICE and Atar. Experimental verification of the modeling usingprototype devices are anticipated. Two prototype generations are expected by the end of the CRD program.