arXiv subjects

Zhanhong Huang

Publications and source records attributed to Zhanhong Huang.

4 recordsLinked to original sources

arXiv2024

Accelerating Point Cloud Ground Segmentation: From Mechanical to Solid-State Lidars

In this study, we propose a novel parallel processing method for point cloud ground segmentation, aimed at the technology evolution from mechanical to solid-state Lidar (SSL). We first benchmark point-based, grid-based, and range image-based ground segmentation algorithms using the SemanticKITTI dataset. Our results indicate that the range image-based method offers superior performance and robustness, particularly in resilience to frame slicing. Implementing the proposed algorithm on an FPGA demonstrates significant improvements in processing speed and scalability of resource usage. Additionally, we develop a custom dataset using camera-SSL equipment on our test vehicle to validate the effectiveness of the parallel processing approach for SSL frames in real world, achieving processing rates up to 30.9 times faster than CPU implementations. These findings underscore the potential of parallel processing strategies to enhance Lidar technologies for advanced perception tasks in autonomous vehicles and robotics. The data and code will be available post-publication on our GitHub repository: \url{https://github.com/WPI-APA-Lab/GroundSeg-Solid-State-Lidar-Parallel-Processing}

cs.CV↗

arXiv2024

Stream-Based Ground Segmentation for Real-Time LiDAR Point Cloud Processing on FPGA

This paper presents a novel and fast approach for ground plane segmentation in a LiDAR point cloud, specifically optimized for processing speed and hardware efficiency on FPGA hardware platforms. Our approach leverages a channel-based segmentation method with an advanced angular data repair technique and a cross-eight-way flood-fill algorithm. This innovative approach significantly reduces the number of iterations while ensuring the high accuracy of the segmented ground plane, which makes the stream-based hardware implementation possible. To validate the proposed approach, we conducted extensive experiments on the SemanticKITTI dataset. We introduced a bird's-eye view (BEV) evaluation metric tailored for the area representation of LiDAR segmentation tasks. Our method demonstrated superior performance in terms of BEV areas when compared to the existing approaches. Moreover, we presented an optimized hardware architecture targeted on a Zynq-7000 FPGA, compatible with LiDARs of various channel densities, i.e., 32, 64, and 128 channels. Our FPGA implementation operating at 160 MHz significantly outperforms the traditional computing platforms, which is 12 to 25 times faster than the CPU-based solutions and up to 6 times faster than the GPU-based solution, in addition to the benefit of low power consumption.

eess.SP↗

arXiv2023

Real-Time Joint Simulation of LiDAR Perception and Motion Planning for Automated Driving

Real-time perception and motion planning are two crucial tasks for autonomous driving. While there are many research works focused on improving the performance of perception and motion planning individually, it is still not clear how a perception error may adversely impact the motion planning results. In this work, we propose a joint simulation framework with LiDAR-based perception and motion planning for real-time automated driving. Taking the sensor input from the CARLA simulator with additive noise, a LiDAR perception system is designed to detect and track all surrounding vehicles and to provide precise orientation and velocity information. Next, we introduce a new collision bound representation that relaxes the communication cost between the perception module and the motion planner. A novel collision checking algorithm is implemented using line intersection checking that is more efficient for long distance range in comparing to the traditional method of occupancy grid. We evaluate the joint simulation framework in CARLA for urban driving scenarios. Experiments show that our proposed automated driving system can execute at 25 Hz, which meets the real-time requirement. The LiDAR perception system has high accuracy within 20 meters when evaluated with the ground truth. The motion planning results in consistent safe distance keeping when tested in CARLA urban driving scenarios.

cs.RO↗

arXiv2023

From Emulation to Mathematical: A More General Traffic Obfuscation Approach To Encounter Feature based Mobile App traffic Classification

The usage of the mobile app is unassailable in this digital era. While tons of data are generated daily, user privacy security concerns become an important issue. Nowadays, tons of techniques, such as machine learning and deep learning traffic classifiers, have been applied to analyze users app traffic. These techniques allow the monitor to get the fingerprints of using apps while the user traffic is still encrypted, which raises a severe privacy issue. In order to fight against this type of data analysis, people have been researching obfuscation algorithms to confuse feature-based machine learning classifiers with data camouflage by modification on packet length distribution. The existing works achieve this goal by remapping traffic packet length distribution from the source app to the fake camouflage app. However, this solution suffers from its lack of scalability and flexibility in practical application since the method needs to pre-sample the target fake apps traffic before the use of traffic camouflage. In this paper, we proposed a practical solution by using a mathematical model to calculate the target distribution while maintaining at least 50 percent accuracy drops on the performance of the AppScanner mobile traffic classifier and roughly 20 percent overhead created during packet modification.

cs.CR↗