Photonic Application in the Automotive Industry

Light detection and ranging (LiDAR) is widely used in the automotive industry as it can provide point cloud information containing precise distances. Three-dimensional (3D) object detection based on LiDAR point clouds is significant for environment perception tasks. However, feature learning for point clouds remains challenging. This paper proposes a two-stage voxel-based LiDAR 3D object detector, referred to as density-aware and neighborhood attention (DenNet), that focuses on the neighborhood information of objects. DenNet mainly integrates two modules: voxel density-aware (VDA) and neighborhood attention (NA). VDA introduces density information of the point cloud. Here, point cloud density information was added as voxel features in the voxel-based framework to alleviate the information loss during voxelization. Additionally, to extract neighbor information, the characteristics of 3D objects were analyzed for traffic scenes. The NA mechanism was adopted, which localizes the receptive field for each query to its nearest neighboring points. DenNet yielded competitive results, as compared with state-of-the-art methods, for the KITTI and One Million Scenes (ONCE) datasets; notably, it afforded an improvement of 3.96% relative to the baseline mean average precision on the more challenging ONCE dataset. Full article

In photoacoustic spectroscopy, the signal is inversely proportional to the resonant cell volume. Photoacoustic spectroscopy (PAS) is an absorption spectroscopy technique that is suitable for detecting gases at low concentrations. This desirable feature has created a growing interest in miniaturizing PA cells in recent years. In this paper, a simulation of a miniaturized H-type photoacoustic cell consisting of two buffer holes and a resonator was performed in order to detect CO, NH₃, NO, and CH₄ pollutants. These gases are the main components of the air pollutants that are produced by the automotive industry. The linear forms of the continuity, Navier–Stokes equations, and the energy equation were solved using the finite element method in a gaseous medium. The generated pressure could be measured by a MEMS sensor. Photoacoustic spectroscopy has proven to be a sensitive method for detecting pollutant gases. The objectives of the measurements were: determining the proper position of the pressure gauge sensor; measuring the frequency response; measuring the frequency response changes at different temperatures; studying the local velocity at the resonant frequency; and calculating the quality factor. The acoustic quality coefficient, acoustic response (pressure), local velocity, frequency response, and the effect of different temperatures on the frequency response were investigated. A frequency response measurement represents the fact that different gases have different resonance frequencies, for which CO and NO gases had values of 23.131 kHz and 23.329 kHz, respectively. The difference between these gases was 200 Hz. NH₃ and CH₄ gases with values of 21.206 kHz and 21.106 kHz were separable with a difference of 100 Hz. In addition, CO and NO gases had a difference of 2000 Hz compared to NH₃ and CH₄, which indicates the characteristic fingerprint of the designed cell in the detection of different gases. Better access to high-frequency acoustic signals was the goal of the presented model in this paper. Full article