LiDAR sensors have the advantage of being able to generate high-resolution imaging quickly during both day and night; however, their performance is severely limited in adverse weather conditions such as snow, rain, and dense fog. Consequently, many researchers are actively working to overcome these limitations by applying sensor fusion with radar and optical cameras to LiDAR. While studies on the denoising of point clouds acquired by LiDAR in adverse weather have been conducted recently, the results are still insufficient for application to autonomous vehicles because of speed and accuracy performance limitations. Therefore, we propose a new intensity-based filter that differs from the existing distance-based filter, which limits the speed. The proposed method showed overwhelming performance advantages in terms of both speed and accuracy by removing only snow particles while leaving important environmental features. The intensity criteria for snow removal were derived based on an analysis of the properties of laser light and snow particles.

This brief presents a new method of analyzing stability and design of a controller for an electric power steering (EPS) system. The most important task when designing a steering system is ensuring that the driver is pleased with how the steering feels. The way that the steering feels is dependent upon the assist torque map. The assist torque map is a one-to-one map between sensed driver torque and assist motor torque that varies with the cars speed. However, an assist torque map cannot be applied alone as an EPS controller because of high level of steering assist gain and nonlinearity of the torque map. Both elements of torque map can result in instability, which leads to vibration or divergence of the steering system. Therefore, an EPS system always needs to be designed with a stabilizing compensator and an assist torque map. The objective of designing a compensator is to stabilize the system with robustness and attenuate any unpleasant vibration. This brief presents a mechanical model of the EPS system and demonstrates a method to identify the model parameter. Based on the EPS model, stability of the system with an approximate linear torque map and nonlinear torque map is analyzed. Furthermore, criteria for designing the stabilizing compensator are suggested. Lead-lag compensators with different parameters are applied with the torque map in simulations, and vehicle experiments are performed to verify the theoretical analysis.

This study proposes an advanced current and position sensor fault diagnosis mechanism for permanent magnet synchronous motor (PMSM) applications. Machine nonlinear dynamics as well as parameter and load uncertainties are addressed. The features of the results of this study are summarized as follows: 1) a proportional-type full-state observer is combined with a disturbance observer to deal with the model uncertainties, 2) a partial optimal process is introduced for determining the observer gain, 3) the concept of normalized residuals is introduced for fault diagnosis criteria, and 4) a closed-loop analysis of the convergence, performance recovery, and offset-free guarantee is provided. The performance recovery property makes the proposed algorithm robust to parameter uncertainties and load variations, and sensitive only to sensor faults. In addition, the location and type of sensor faults can be identified by the fault diagnosis criteria using the normalized residuals. The performance of the sensor fault diagnosis algorithm was experimentally verified under several credible scenarios using a 7.5-kW PMSM.

This study proposes a robust SOC estimation scheme for lithium-ion batteries in commercialized electric vehicles: pure electric vehicles and hybrid electric vehicles. Development of a nonlinear-model-based robust SOC estimation method and comprehensive performance comparison with the existing SOC estimation methods under the two kinds of operating scenarios are considered. The features of the results of this study are summarized as follows: a) a robust SOC estimator is designed to deal with the model uncertainty and parameter variation, b) an analysis of asymptotic stability of the proposed robust SOC estimator is provided and c) a comprehensive analysis was performed not only on the different aging levels of battery cells but also on the different operational conditions of the electrified vehicles. The effectiveness of the proposed SOC estimation method was experimentally verified using two 18 650 cylindrical battery cells with different degradation levels. The proposed SOC estimation scheme ensures considerable accuracy under both pure electric vehicle operating mode and hybrid vehicle operating mode even if the battery cell is aged.

In this paper, a novel algorithm for estimating clothing insulation is proposed to assess thermal comfort, based on the non-contact and real-time measurements of the face and clothing temperatures by an infrared camera. The proposed method can accurately measure the clothing insulation of various garments under different clothing fit and sitting postures. The proposed estimation method is investigated to be effective to measure its clothing insulation significantly in different seasonal clothing conditions using a paired t-test in 99% confidence interval. Temperatures simulated with the proposed estimated insulation value show closer to the values of actual temperature than those with individual clothing insulation values. Upper clothing’s temperature is more accurate within 3% error and lower clothing’s temperature is more accurate by 3.7%~6.2% error in indoor working scenarios. The proposed algorithm can reflect the effect of air layer which makes insulation different in the calculation to estimate clothing insulation using the temperature of the face and clothing. In future, the proposed method is expected to be applied to evaluate the customized passenger comfort effectively.

Recently, several 4D radar datasets have been published due to its high resolution and robustness in extreme weather conditions. However, most of the recent 4D radar dataset publications have insufficient odometry sensors, which is disadvantageous for 4D radar odometry research. To tackle this problem, this letter presents a 4D radar dataset with various odometry sensors based on a robot operating system (ROS) framework called MSC-RAD4R , which stands for Motivated for SLAM in City, ROS-based Automotive Dataset with 4D Radar . Our dataset at a glance includes 98,786 pairs of stereo images, 60,562 frames of LiDAR data, 90,864 frames of 4D radar data, 60,570 frames of RTK-GPS, 60,559 frames of GPS, 1,211,486 frames of IMU data and 6,057,276 wheel data covering approximately 51.6 km and 100 minutes of automotive data in various environments including day, night, snow and smoke. In particular, our setup includes a high-resolution 79 GHz Adaptive PDM FMCW Oculii 4D radar, which provides approximately 5,000–20,000 points per frame and operates at a long range of 400 meters with a frequency of 15 Hz. It is expected that the proposed dataset will be useful for researchers working on 4D radar SLAM.