This study introduces a novel pedestrian monitoring methodology that integrates LiDAR point cloud data with camera systems to enhance road safety in autonomous driving environments. By projecting LiDAR data onto camera images, the proposed system enables precise distance estimation for effective collision prevention. Advanced statistical techniques and the RANSAC algorithm are employed to extract ground information and generate accurate distance maps. Validation using the KITTI dataset demonstrates promising performance in short- to midrange distance estimation, achieving accuracy levels of 98.2% to 99.7% within 0-10 meters (5m threshold). Addressing the limitations of radar-based Advanced Driver Assistance Systems (ADAS), this research presents a comprehensive framework for pedestrian detection, tracking, and distance estimation. The findings establish a solid foundation for integrating LiDAR and camera technologies into autonomous vehicles, contributing to safer and more efficient driving systems.

Recently, various approaches have been developed for traffic analysis systems, ranging from Vehicle Detection Systems (VDS) to Mobile Detection Systems (MDS). However, VDS has limitations. For example, in actual driving situations such as intersections or exits, each lane may have different traffic conditions, revealing the limitations of traffic information provided by conventional VDS. In this paper, we propose a methodology to efficiently monitor the traffic condition and safety on the road by utilizing a LiDAR sensor installed on a vehicle to collect continuous traffic information about surrounding vehicles. To implement the Mobile Detection System (MDS), we collect point cloud data from LiDAR and detect the position and size of vehicles using a deep learning-based voxel-RCNN. We then convert the data into traffic information for analysis. Furthermore, we propose an efficient method for analyzing road hazards by introducing the MTTC method based on the TTC for hazard assessment. To evaluate the performance of the proposed method, we compare its reliability with that of conventional VDS and perform road hazards analysis using LiDAR-based probe vehicles with data collected directly from highways in Korea.

A new hierarchical layout vs. schematic (LVS) verification system has been developed for layout verification. It compares a hierarchical schematic netlist and a flattened layout netlist. The schematic hierarchy is restructured for consistent hierarchical matching and then the same hierarchy is built from the layout netlist. For efficiency, simple gates are found by using a fast rule-based pattern matching algorithm. Each subcircuit is found from the layout by using a modified SubGemini algorithm in bottom-up fashion. Experimental results show that our hierarchical netlist comparison technique is effective and efficient in CPU time and in memory usage.

A hierarchical placement algorithm which combines mincut partitioning and simulated annealing has been developed. The objective of mincut partitioning is to minimize the number of crossing nets while the objective of placement by simulated annealing is usually to minimize the total estimated wire-length. The combined placement algorithm can optimize both the routing density and the estimated wire-length. For efficiency, the placement is performed using multiple levels of hierarchy in the top-down direction, i.e., big groups of cells are placed at the beginning and leafcells are placed at the final level. Several standard-cell and sea-of-gates circuits are placed using the combined placement techniques and promising results are obtained when compared with those of several other placement methods.