In recent years, achieving full autonomy in driving has emerged as a paramount objective for both the industry and academia. Among various perception technologies, Lidar (Light detection and ranging) stands out for its high-precision and high-resolution capabilities based on the principle of light propagation and coupling ranging module and imaging module. Lidar is a sophisticated system that integrates multiple technologies such as optics, mechanics, circuits, and algorithms. Therefore, there are various feasible Lidar schemes to meet the needs of autonomous driving in different scenarios. The ranging performance of Lidar is a key factor that determines the overall performance of autonomous driving systems. As such, it is necessary to conduct a systematic analysis of the ranging performance of different Lidar schemes. In this paper, we present the ranging performance analysis methods corresponding to different optical designs, device selec-tions and measurement mechanisms. By using these methods, we compare the ranging perfor-mance of several typical commercial Lidars. Our findings provide a reference framework for de-signing Lidars with various trade-offs between cost and performance, and offer insights into the advancement towards improving Lidar schemes.

Conic optimization plays a crucial role in many machine learning (ML) problems. However, practical algorithms for conic constrained ML problems with large datasets are often limited to specific use cases, as stochastic algorithms for general conic optimization remain underdeveloped. To fill this gap, we introduce a stochastic interior-point method (SIPM) framework for general conic optimization, along with four novel SIPM variants leveraging distinct stochastic gradient estimators. Under mild assumptions, we establish the global convergence rates of our proposed SIPMs, which, up to a logarithmic factor, match the best-known rates in stochastic unconstrained optimization. Finally, our numerical experiments on robust linear regression, multi-task relationship learning, and clustering data streams demonstrate the effectiveness and efficiency of our approach.

We demonstrate the possibility of reconstructing images from fNIRS brain activity and start building a prototype to match the required specs. By training an image reconstruction model on downsampled fMRI data, we discovered that cm-scale spatial resolution is sufficient for image generation. We obtained 71% retrieval accuracy with 1-cm resolution, compared to 93% on the full-resolution fMRI, and 20% with 2-cm resolution. With simulations and high-density tomography, we found that time-domain fNIRS can achieve 1-cm resolution, compared to 2-cm resolution for continuous-wave fNIRS. Lastly, we share designs for a prototype time-domain fNIRS device, consisting of a laser driver, a single photon detector, and a time-to-digital converter system.