Integrated LiDAR system for autonomous vehicles

激光雷达是一种通过用脉冲激光照射目标并用传感器探测反射脉冲来测量目标距离的测量方法。如果能够降低成本，改善功能，到2030年，汽车和无人驾驶车辆的激光雷达市场有望超过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、与先进的人工智能(AI)系统集成的“快速、敏捷”的LiDAR平台。这类系统可以支持“关注”视野中的重要对象的模块，从而能够高分辨率地确定关键项目。激光雷达将不是一个简单的被动信息收集元素，而是一个集成的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“快速、敏捷”的LiDAR，该原型具有在人工智能基础设施中启用“注意力模块”的必要功能。其次，本项目提出开发一个基于PIC的激光雷达复杂仿真平台。该研究将为LiDAR PIC的完整系统级设计奠定基础。第一个目标是通过采用耗尽型移相器阵列和亚波长光栅耦合器来改善LiDAR的功能，以提高波控范围、分辨率和扫描速度。通过提供分析和优化能力，第二个目标将是对第一个目标的补充。这项工作将解决激光雷达设计和优化中存在的多物理和多尺度问题，通过结合电气、光学、半导体和热能设备和效应来处理系统。还将讨论物理层对系统设计和约束的影响。仿真工作将建立在OptiSPICE和ATAR现有的仿真框架上。预计将使用原型装置对建模进行实验验证。预计在CRD计划结束时，将有两代原型。

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.