Velocity Estimation from LiDAR Sensors Motion Distortion Effect.

Many modern automated vehicle sensor systems use light detection and ranging (LiDAR) sensors. The prevailing technology is scanning LiDAR, where a collimated laser beam illuminates objects sequentially point-by-point to capture 3D range data. In current systems, the point clouds from the LiDAR sensors are mainly used for object detection. To estimate the velocity of an object of interest (OoI) in the point cloud, the tracking of the object or sensor data fusion is needed. Scanning LiDAR sensors show the motion distortion effect, which occurs when objects have a relative velocity to the sensor. Often, this effect is filtered, by using sensor data fusion, to use an undistorted point cloud for object detection. In this study, we developed a method using an artificial neural network to estimate an object's velocity and direction of motion in the sensor's field of view (FoV) based on the motion distortion effect without any sensor data fusion. This network was trained and evaluated with a synthetic dataset featuring the motion distortion effect. With the method presented in this paper, one can estimate the velocity and direction of an OoI that moves independently from the sensor from a single point cloud using only one single sensor. The method achieves a root mean squared error (RMSE) of 0.1187 m s-1 and a two-sigma confidence interval of [-0.0008 m s-1, 0.0017 m s-1] for the axis-wise estimation of an object's relative velocity, and an RMSE of 0.0815 m s-1 and a two-sigma confidence interval of [0.0138 m s-1, 0.0170 m s-1] for the estimation of the resultant velocity. The extracted velocity information (4D-LiDAR) is available for motion prediction and object tracking and can lead to more reliable velocity data due to more redundancy for sensor data fusion.

Methyl-CpG-Binding Protein 2 Emerges as a Central Player in Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorders.

MECP2 and its product methyl-CpG binding protein 2 (MeCP2) are associated with multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD), which are inflammatory, autoimmune, and demyelinating disorders of the central nervous system (CNS). However, the mechanisms and pathways regulated by MeCP2 in immune activation in favor of MS and NMOSD are not fully understood. We summarize findings that use the binding properties of MeCP2 to identify its targets, particularly the genes recognized by MeCP2 and associated with several neurological disorders. MeCP2 regulates gene expression in neurons, immune cells and during development by modulating various mechanisms and pathways. Dysregulation of the MeCP2 signaling pathway has been associated with several disorders, including neurological and autoimmune diseases. A thorough understanding of the molecular mechanisms underlying MeCP2 function can provide new therapeutic strategies for these conditions. The nervous system is the primary system affected in MeCP2-associated disorders, and other systems may also contribute to MeCP2 action through its target genes. MeCP2 signaling pathways provide promise as potential therapeutic targets in progressive MS and NMOSD. MeCP2 not only increases susceptibility and induces anti-inflammatory responses in immune sites but also leads to a chronic increase in pro-inflammatory cytokines gene expression (IFN-γ, TNF-α, and IL-1ß) and downregulates the genes involved in immune regulation (IL-10, FoxP3, and CX3CR1). MeCP2 may modulate similar mechanisms in different pathologies and suggest that treatments for MS and NMOSD disorders may be effective in treating related disorders. MeCP2 regulates gene expression in MS and NMOSD. However, dysregulation of the MeCP2 signaling pathway is implicated in these disorders. MeCP2 plays a role as a therapeutic target for MS and NMOSD and provides pathways and mechanisms that are modulated by MeCP2 in the regulation of gene expression.

A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors.

In this work, we introduce a novel approach to model the rain and fog effect on the light detection and ranging (LiDAR) sensor performance for the simulation-based testing of LiDAR systems. The proposed methodology allows for the simulation of the rain and fog effect using the rigorous applications of the Mie scattering theory on the time domain for transient and point cloud levels for spatial analyses. The time domain analysis permits us to benchmark the virtual LiDAR signal attenuation and signal-to-noise ratio (SNR) caused by rain and fog droplets. In addition, the detection rate (DR), false detection rate (FDR), and distance error derror of the virtual LiDAR sensor due to rain and fog droplets are evaluated on the point cloud level. The mean absolute percentage error (MAPE) is used to quantify the simulation and real measurement results on the time domain and point cloud levels for the rain and fog droplets. The results of the simulation and real measurements match well on the time domain and point cloud levels if the simulated and real rain distributions are the same. The real and virtual LiDAR sensor performance degrades more under the influence of fog droplets than in rain.

Performance Evaluation of MEMS-Based Automotive LiDAR Sensor and Its Simulation Model as per ASTM E3125-17 Standard.

Measurement performance evaluation of real and virtual automotive light detection and ranging (LiDAR) sensors is an active area of research. However, no commonly accepted automotive standards, metrics, or criteria exist to evaluate their measurement performance. ASTM International released the ASTM E3125-17 standard for the operational performance evaluation of 3D imaging systems commonly referred to as terrestrial laser scanners (TLS). This standard defines the specifications and static test procedures to evaluate the 3D imaging and point-to-point distance measurement performance of TLS. In this work, we have assessed the 3D imaging and point-to-point distance estimation performance of a commercial micro-electro-mechanical system (MEMS)-based automotive LiDAR sensor and its simulation model according to the test procedures defined in this standard. The static tests were performed in a laboratory environment. In addition, a subset of static tests was also performed at the proving ground in natural environmental conditions to determine the 3D imaging and point-to-point distance measurement performance of the real LiDAR sensor. In addition, real scenarios and environmental conditions were replicated in the virtual environment of a commercial software to verify the LiDAR model's working performance. The evaluation results show that the LiDAR sensor and its simulation model under analysis pass all the tests specified in the ASTM E3125-17 standard. This standard helps to understand whether sensor measurement errors are due to internal or external influences. We have also shown that the 3D imaging and point-to-point distance estimation performance of LiDAR sensors significantly impacts the working performance of the object recognition algorithm. That is why this standard can be beneficial in validating automotive real and virtual LiDAR sensors, at least in the early stage of development. Furthermore, the simulation and real measurements show good agreement on the point cloud and object recognition levels.

Development of High-Fidelity Automotive LiDAR Sensor Model with Standardized Interfaces.

This work introduces a process to develop a tool-independent, high-fidelity, ray tracing-based light detection and ranging (LiDAR) model. This virtual LiDAR sensor includes accurate modeling of the scan pattern and a complete signal processing toolchain of a LiDAR sensor. It is developed as a functional mock-up unit (FMU) by using the standardized open simulation interface (OSI) 3.0.2, and functional mock-up interface (FMI) 2.0. Subsequently, it was integrated into two commercial software virtual environment frameworks to demonstrate its exchangeability. Furthermore, the accuracy of the LiDAR sensor model is validated by comparing the simulation and real measurement data on the time domain and on the point cloud level. The validation results show that the mean absolute percentage error (MAPE) of simulated and measured time domain signal amplitude is 1.7%. In addition, the MAPE of the number of points Npoints and mean intensity Imean values received from the virtual and real targets are 8.5% and 9.3%, respectively. To the author's knowledge, these are the smallest errors reported for the number of received points Npoints and mean intensity Imean values up until now. Moreover, the distance error derror is below the range accuracy of the actual LiDAR sensor, which is 2 cm for this use case. In addition, the proving ground measurement results are compared with the state-of-the-art LiDAR model provided by commercial software and the proposed LiDAR model to measure the presented model fidelity. The results show that the complete signal processing steps and imperfections of real LiDAR sensors need to be considered in the virtual LiDAR to obtain simulation results close to the actual sensor. Such considerable imperfections are optical losses, inherent detector effects, effects generated by the electrical amplification, and noise produced by the sunlight.