Recently, the safety of ADAS (Autonomous Driving Assistance System) has become a critical issue in industrial areas. Modern intelligent automobile control systems adopt various sensors for recognizing environmental obstacles, and LiDAR (Light Detection and Ranging) sensor has been selected due to its consistently accurate sensing abilities regardless of external conditions. Also, many neural network models have been proposed to process LiDAR data. Short inference latency and high accuracy matter for ADAS. From this perspective, the encoder of Pointpillars shortens inference latency by converting the 3D LiDAR point cloud into a 2D pseudo-image for object detection.In this paper, we study the architecture of PointPillars and analyze its performance by VTune  on CPU and NVIDIA profiler on GPU . It has been observed that RPN (Region Proposal Network) shows the most dominant execution time to the overall model due to its internal convolution and transposed convolution operation. Therefore, the adoption of PointPillars to real ADAS requires RPN network optimization.