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- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC) - Maui, HI, USA (2018.11.4-2018.11.7)] 2018 21st International Conference on Intelligent Transportation Systems (ITSC) - Pedestrian-Detection Method based on 1D-CNN during LiDAR Rotation
摘要： Pedestrian detection in autonomous driving systems is important for preventing accidents involving pedestrians and vehicles. Conventional pedestrian detection methods involve Light Detection and Ranging (LiDAR), which requires clustering points into a cloud before determining whether each point is a pedestrian. Therefore, there may not be sufficient time for an autonomous driving system to ensure safety if a pedestrian and vehicle are too close to each other. We propose a pedestrian detection method that is based on a one-dimensional convolution neural network (1D-CNN) that processes LiDAR waveform data without delay. The proposed method sequentially inputs LiDAR waveform data to the 1D-CNN and determines whether each point belongs to a pedestrian. Therefore, it is possible to reduce the difference between the detected and actual positions of pedestrians since our method can be used during LiDAR sensor rotation.
关键词： autonomous driving,LiDAR,Pedestrian detection,1D-CNN
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC) - Maui, HI, USA (2018.11.4-2018.11.7)] 2018 21st International Conference on Intelligent Transportation Systems (ITSC) - The TUBS Road User Dataset: A New LiDAR Dataset and its Application to CNN-based Road User Classification for Automated Vehicles
摘要： We present a novel approach for classifying pre-segmented laser scans of road users with consideration of real-time capability for applications in automated vehicles. Our classification approach uses 2.5D Convolutional Neural Networks (CNNs) to process range data as well as intensity information retrieved from reflected beams. We do not solely rely on publicly available laser scan datasets, which lack several features, but we provide an additional dataset from real-world sensor recordings, annotated by a tracking-based automatic labeling process. We evaluate the classification performance of our CNN regarding different feature configurations. For training, we use automatically and manually labeled data as well as mixtures with other public datasets. The results show promising classification capabilities. Training with automated labels shows similar results, providing a possibility to avoid the need for manual editing expense.
关键词： CNN,dataset,road user classification,automated vehicles,LiDAR
更新于2025-09-04 15:30:14
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[IEEE 2018 37th Chinese Control Conference (CCC) - Wuhan (2018.7.25-2018.7.27)] 2018 37th Chinese Control Conference (CCC) - A Lidar-Based Tree Canopy Detection System Development
摘要： In this paper, a LiDAR-based interactive target detection system was developed to characterized tree canopy structures under various laser sensor travel speed and detection distances. The system composed of a sliding motion control system and a Lidar-based target detection unit. The target detection unit used a 2700 range laser scanning sensor to measure target object surface distances based on the time-of-flight principle. The laser sensor travel speed and travel distance was controlled via the control system by specifying a position and speed as a set point. A real-time data acquisition and data post-processing programs were developed based on C++ and MATLAB programming languages respectively. The entire system was tested in the laboratory for a wide range of parameters and operating conditions. The test result showed that the system could detect and characterize tree canopy structure at very low travel speed (0.3 m/s) and high travel speed (5.0 m/s), respectively with acceptable accuracy.
关键词： Tree canopy,three-dimensional image reconstruction,Precision agriculture,LiDAR,Servo system
更新于2025-09-04 15:30:14
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Lidar arc scan uncertainty reduction through scanning geometry optimization
摘要： Doppler lidars are frequently operated in a mode referred to as arc scans, wherein the lidar beam scans across a sector with a ?xed elevation angle and the resulting measurements are used to derive an estimate of the n minute horizontal mean wind velocity (speed and direction). Previous studies have shown that the uncertainty in the measured wind speed originates from turbulent wind ?uctuations and depends on the scan geometry (the arc span and the arc orientation). This paper is designed to provide guidance on optimal scan geometries for two key applications in the wind energy industry: wind turbine power performance analysis and annual energy production prediction. We present a quantitative analysis of the retrieved wind speed uncertainty derived using a theoretical model with the assumption of isotropic and frozen turbulence, and observations from three sites that are onshore with ?at terrain, onshore with complex terrain and offshore, respectively. The results from both the theoretical model and observations show that the uncertainty is scaled with the turbulence intensity such that the relative standard error on the 10 min mean wind speed is about 30 % of the turbulence intensity. The uncertainty in both retrieved wind speeds and derived wind energy production estimates can be reduced by aligning lidar beams with the dominant wind direction, increasing the arc span and lowering the number of beams per arc scan. Large arc spans should be used at sites with high turbulence intensity and/or large wind direction variation.
关键词： arc scans,wind energy,Doppler lidar,scan geometry optimization,turbulence intensity
更新于2025-09-04 15:30:14
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Performance comparison of Fabry-Perot and Mach-Zehnder interferometers for Doppler lidar based on double-edge technique
摘要： Compared with dual-channel Fabry-Perot interferometer (DFPI), dual-channel Mach-Zehnder interferometer (DMZI) is a new frequency discriminator for Doppler wind lidar based on double-edge detection. The double-edge frequency discrimination systems based on DFPI and DMZI in the wind lidar system are designed, respectively, and the detection principle is analyzed theoretically. Under certain simulation conditions, performances such as line-of-sight (LOS) wind velocity measurement sensitivities, signal-to-noise ratios (SNR) and LOS wind velocity measurement errors of both DFPI and DMZI systems are simulated and compared based on the U.S. standard atmospheric model. The simulation results show that for 1064nm aerosol system, the LOS wind velocity measurement sensitivity of the DMZI system is lower than that of DFPI system and the error is higher than that of DFPI system with the same dynamic wind velocity measurement range. Nevertheless, for 355nm atmospheric molecular system, DMZI system provides a factor of 1.33 times lower error in the LOS wind velocity than that of DFPI with an altitude of 30 km and the LOS wind velocity of 200m/s. Furthermore, in the LOS wind velocity range of -200m/s~+200m/s, the measurement error of DMZI system hardly increases with the increasing of wind speed. Therefore, the results of this study not only provide guidance for the selection of discriminators, but also provide theoretical support for the application of DMZI in the double-edge detection of atmospheric molecular lidar.
关键词： dual-channel Fabry-Perot interferometer,Doppler lidar,wind velocity measurement,dual-channel Mach-Zehnder interferometer,double-edge detection
更新于2025-09-04 15:30:14
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Estimation of Voxel-Based Above-Ground Biomass Using Airborne LiDAR Data in an Intact Tropical Rain Forest, Brunei
摘要： The advancement of LiDAR technology has enabled more detailed evaluations of forest structures. The so-called “Volumetric pixel (voxel)” has emerged as a new comprehensive approach. The purpose of this study was to estimate plot-level above-ground biomass (AGB) in different plot sizes of 20 m × 20 m and 30 m × 30 m, and to develop a regression model for AGB prediction. Both point cloud-based (PCB) and voxel-based (VB) metrics were used to maximize the efficiency of low-density LiDAR data within a dense forest. Multiple regression model AGB prediction performance was found to be greatest in the 30 m × 30 m plots, with R2, adjusted R2, and standard deviation values of 0.92, 0.87, and 35.13 Mg·ha?1, respectively. Five out of the eight selected independent variables were derived from VB metrics and the other three were derived from PCB metrics. Validation of accuracy yielded RMSE and NRMSE values of 27.8 Mg·ha?1 and 9.2%, respectively, which is a reasonable estimate for this structurally complex intact forest that has shown high NRMSE values in previous studies. This voxel-based approach enables a greater understanding of complex forest structure and is expected to contribute to the advancement of forest carbon quantification techniques.
关键词： LiDAR,voxel,REDD+,volumetric pixel,forest biomass,forest carbon stock,climate change
更新于2025-09-04 15:30:14