Black carbon (BC), normally existing as aggregates, significantly affects the Earth radiative forcing, energy balance, and climate by scattering and absorbing both solar radiation and terrestrial emission. The BC particles are usually treated as fractal aggregates with same-sized monomers. However, experimental studies show that monomer diameters of BC normally obey a lognormal distribution ranging from 10 nm to over 100 nm. This study investigates the effects of monomer size distribution on the radiative properties of BC particles. The fractal aggregates are generated by a cluster-cluster aggregation (CCA) algorithm, and the Multiple Sphere T-Matrix (MSTM) method is used to simulate the radiative properties of randomly oriented aggregates. The integral radiative properties of aggregates with different-sized monomers have normal distributions with large standard deviations, and it requires to average radiative properties of over 60 aggregate realizations to represent their ensemble-averaged properties. The aggregates with different-sized monomers exhibit much stronger scattering and absorption than the aggregates with same-sized monomers and the geometric mean diameter, whereas the absorption cross section becomes comparable to that given by aggregates with same-sized monomer and the equivalent volume diameter. Similar phase matrix elements are obtained for the aggregates with different-sized and same-sized monomers. Furthermore, the Rayleigh-Debye-Gans (RDG) approach is significantly challenged for approximating the absorption and scattering cross sections of the aggregates with different-sized monomers, whereas it performs quite accurately for the phase matrix elements.
Environment perception is critical for feasible path planning and safe driving for autonomous vehicles. Perception devices, such as camera, LiDAR (Light Detection and Ranging), IMU (Inertial Measurement Unit), etc., only provide raw sensing data with no identification of vital objects, which is insufficient for autonomous vehicles to perform safe and efficient self-driving operations. This study proposes an improved edge-oriented segmentation-based method to detect the objects from the sensed three-dimensional (3D) point cloud. The improved edge-oriented segmentation-based method consists of three main steps: First, the bounding areas of objects are identified by edge detection and stixel estimation in corresponding two-dimensional (2D) images taken by a stereo camera. Second, 3D sparse point clouds of objects are reconstructed in bounding areas. Finally, the dense point clouds of objects are segmented by matching the 3D sparse point clouds of objects with the whole scene point cloud. After comparison with the existing methods of segmentation, the experimental results demonstrate that the proposed edge-oriented segmentation method improves the precision of 3D point cloud segmentation, and that the objects can be segmented accurately. Meanwhile, the visualization of output data in advanced driving assistance systems (ADAS) can be greatly facilitated due to the decrease in computational time and the decrease in the number of points in the object’s point cloud.
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