## 4 Conclusions and Outlook
Eyeonic vision systems are available for a variety of distance requirements: short-range (SR) Eyeonic vision systems, optimized for ranges up to 50 m with millimeter-to-sub-millimeter depth accuracy, ideal for high-precision AI machine vision tasks such as pallet and truck loading or product inspection; medium-range (MR) Eyeonic vision systems, optimized for ranges up to 150 m, offering 3D ranging, dual polarization, and instantaneous velocity with industry-leading accuracy for home security and factory automation applications; long-range (LR) Eyeonic vision systems, effective at ranges up to 300 m, providing instantaneous velocity detection and excel in dynamic object classification and prediction for applications such as ADAS, self-driving cars, outdoor industrial automation, smart infrastructure, and mapping; and ultra-long-range (ULR) Eyeonic vision systems, providing visual inspection from 500 m to over 1 km with leading angular resolution, range, and velocity accuracy, designed for applications such as drone tracking, perimeter security, and aircraft ground control [73]. In the future, with advancements in materials science and microelectronics technology, it is anticipated that more-compact, higher-performance, and lower-cost FMCW LiDAR systems will be developed. These advancements will further expand their range of applications. Continued research and development of silicon-based optoelectronic integration technology is crucial for achieving higher performance in FMCW LiDAR systems. This is not only an inevitable trend in technological innovation but also a key step in driving modern technology towards broader applications. Therefore, in-depth research and technological development in this field will open up more possibilities for implementing efficient, economical, and intelligent sensing technologies. This study comprehensively discusses the core parameters of the FMCW LiDAR system, covering key parameters, including detection distance, accuracy, ranging precision, modulation linearity, and narrow linewidth, and emphasizes their significance to system performance. The performance indicators of FMCW LiDAR are primarily limited by the performance parameters of the light source, thus generating a superior light modulation signal is also a focal point for FMCW LiDAR researchers. This discussion includes the roles of these parameters in the integrated system and provides a reference analysis for multi-parameter joint optimization, aimed at supporting future decisions in the integration process of FMCW LiDAR systems. This will focus attention on key parameters and their interactions during the design and optimization process, offering reliable references for making more informed choices. **Author Contributions:** Conceptualization, Z.W. and Y.S. ; methodology, J.L. and H.S. ; validation, M.S. and H.Z. ; formal analysis Y.C., C.Q. and Y.L. ; investigation, Y.S., L.L. and P.J. ; resources, L.W., Y.N., L.Q. and J.Z. ; writing--original draft, Z.W. ; writing--review and editing, Y.C. and Y.S. ; supervision, C.Q., Y.C. and L.W. ; project administration, Y.W. and J.L. ; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This work received support from the Science and Technology Development Project of Jilin Province [Grant No. 20220201072 GX], the Outstanding Scientific and Technological Talents Project of Jilin Province [Grant No. 20230508097 RC], the Science and Technology Development Project of Jilin Province [20220508036 RC], the Strategic Research and Consulting Project of the Chinese Academy of Engineering [Grant No. JL2023-16], the National Key Research and Development Program of China [Grant No. 2022 YFB2804504], the Science and Technology Development Project of Jilin Province [Grant No.20230201033 GX]; the Changchun Distinguished Young Scholars Program [Grant No.23 YQ18]; the National Natural Science Foundation of China [Grants Nos. 62090050, 62121005, 61934003, 62227819, and 62090051], and the Dawn Talent Training Program of CIOMP.