Autonomous vehicle technology has the potential to improve the safety, efficiency, and cost of our current transportation system by removing human error. With sensors available today, it is possible for the development of these vehicles, however, there are still issues with autonomous vehicle operations in adverse weather conditions (e.g. snow-covered roads, heavy rain, fog, etc.) due to the degradation of sensor data quality and insufficiently robust software algorithms. Since autonomous vehicles rely entirely on sensor data to perceive their surrounding environment, this becomes a significant issue in the performance of the autonomous system. The purpose of this study is to collect sensor data under various weather conditions to understand the effects of weather on sensor data. The sensors used in this study were one camera and one LiDAR. These sensors were connected to an NVIDIA Drive Px2 which operated in a 2019 Kia Niro. Two custom scenarios (static and dynamic objects) were chosen to collect sensor data operating in four real-world weather conditions: fair, cloudy, rainy, and light snow. An algorithm developed herein was used to provide a method of quantifying the data for comparison against the other weather conditions. The results from these performance algorithms show that sensor data quality degrades by an average of 13.88% for static objects and 16.16% for dynamic objects while operating in these conditions, with operations in rain proving to have the most significant effect on sensor data degradation. From this study, it is hypothesized that advancements in data processing algorithms can improve the usability of this degraded data. In future work, we seek to explore fault-tolerant sensor fusion algorithms that can overcome the effects of adverse weather.

LiDAR point cloud clustering is an essential part of a wide range of applications such as object detection, object recognition, and localization. In this paper, we focus on Density Based Spatial Clustering of Applications with Noise (DBSCAN) as a very promising and efficient algorithm to cluster LiDAR point cloud. However, it requires two tuning parameters that are manually specified based on prior knowledge of the domain. Furthermore, the selected tuning parameters cannot be modified once the clustering process begins. Environments in domains like robotics and autonomous driving are very dynamic, and hence, automated and adaptive selection of these parameters is preferred. In this work, we propose to estimate DBSCAN tuning parameters automatically based on a field of view division scheme, and points’ distances and densities within the division scheme. Furthermore, we adaptively estimate the tuning parameters for every point cloud frame separately. We evaluated the proposed method using the KITTI dataset, and the preliminary results show that the proposed method can achieve comparable results to the original DBSCAN without the need for manually selecting the parameters.

Electric vehicles (EVs) are becoming increasingly popular as an efficient transportation solution but they also present unique challenges for energy management. Bi-directional charging (BDC) is a solution that allows EVs to not only consume energy from the grid but also supply energy back to the grid. This facilitates vehicle-to-load (V2L) integration, where EVs can act as mobile power sources for homes, buildings, and the grid. V2L enables better energy management by utilizing EVs as a flexible resource to balance grid demand and supply in the proposed system. This is achieved through intelligent coordination between the EVs, charging stations, and the grid, using smart meters and communication networks. Integration of BDC and V2L also enables EVs to provide backup power during grid outages, reduce the need for costly grid infrastructure, and support renewable energy integration. BDC with V2L integration is a promising approach for optimal energy management in EVs and can play a significant role in the future of sustainable transportation and energy systems. The proposed model reached 95.13% charging efficiency, 95.03% energy management, 95.69% power rating, 96.28% voltage support and 87.99% temperature management.

Pixel-level depth information is crucial to many applications, such as autonomous driving, robotics navigation, 3D scene reconstruction, and augmented reality. However, depth information, which is usually acquired by sensors such as LiDAR, is sparse. Depth completion is a process that predicts missing pixels’ depth information from a set of sparse depth measurements. Most of the ongoing research applies deep neural networks on the entire sparse depth map and camera scene without utilizing any information about the available objects, which results in more complex and resource-demanding networks. In this work, we propose to use image instance segmentation to detect objects of interest with pixel-level locations, along with sparse depth data, to support depth completion. The framework utilizes a two-branch encoder–decoder deep neural network. It fuses information about scene available objects, such as objects’ type and pixel-level location, LiDAR, and RGB camera, to predict dense accurate depth maps. Experimental results on the KITTI dataset showed faster training and improved prediction accuracy. The proposed method reaches a convergence state faster and surpasses the baseline model in all evaluation metrics.

This paper proposes a Balanced Power-Aware Clustering and Routing protocol (BPA-CRP). The clustering algorithm allows any cluster to operate multiple rounds (a batch) without any need for set-up overhead. BPA-CRP assigns four different broadcast ranges for each sensor. Not only to this extent, but rather, BPA-CRP introduces a routing algorithm in which a new node role called "forwarder" is capable of relaying the collected data from the layer, it resides in, and far away forwarders toward the base station. Simulation results showed an exceptional performance of BPA-CRP over different relevant works in terms of network lifetime.