ABSTRACT
This study is the first to develop technology to evaluate the object recognition performance of camera sensors, which are increasingly important in autonomous vehicles owing to their relatively low price, and to verify the efficiency of camera recognition algorithms in obstruction situations. To this end, the concentration and color of the blockage and the type and color of the object were set as major factors, with their effects on camera recognition performance analyzed using a camera simulator based on a virtual test drive toolkit. The results show that the blockage concentration has the largest impact on object recognition, followed in order by the object type, blockage color, and object color. As for the blockage color, black exhibited better recognition performance than gray and yellow. In addition, changes in the blockage color affected the recognition of object types, resulting in different responses to each object. Through this study, we propose a blockage-based camera recognition performance evaluation method using simulation, and we establish an algorithm evaluation environment for various manufacturers through an interface with an actual camera. By suggesting the necessity and timing of future camera lens cleaning, we provide manufacturers with technical measures to improve the cleaning timing and camera safety.
ABSTRACT
In an effort to better represent aerosol transport in mesoscale and global-scale models, large eddy simulations (LES) from the National Center for Atmospheric Research (NCAR) Turbulence with Particles (NTLP) code are used to develop a Markov chain random walk model that predicts aerosol particle profiles in a cloud-free marine atmospheric boundary layer (MABL). The evolution of vertical concentration profiles are simulated for a range of aerosol particle sizes and in a neutral and an unstable boundary layer. For the neutral boundary layer we find, based on the LES statistics and a specific model time step, that there exist significant correlation for particle positions, meaning that particles near the bottom of the boundary are more likely to remain near the bottom of the boundary layer than being abruptly transported to the top, and vice versa. For the unstable boundary layer, a similar time interval exhibits a weaker tendency for an aerosol particle to remain close to its current location compared to the neutral case due to the strong nonlocal convective motions. In the limit of a large time interval, particles have been mixed throughout the MABL and virtually no temporal correlation exists. We leverage this information to parameterize a Markov chain random walk model that accurately predicts the evolution of vertical concentration profiles. The new methodology has significant potential to be applied at the subgrid level for coarser-scale weather and climate models, the utility of which is shown by comparison to airborne field data and global aerosol models.
