Large-scale wind power grid integration challenges and their solution: a detailed review.

Despite global warming, renewable energy has gained much interest worldwide due to its ability to generate large-scale energy without emitting greenhouse gases. The availability and low cost of wind energy and its high efficiency and technological advancements make it one of the most promising renewable energy sources. Hence, capturing large amounts of wind energy is essential today. The large-scale integration of wind power sources must be evaluated and mitigated to develop a sustainable future power system. Wind energy research and the government are working together to overcome the potential barriers associated with its penetration into the power grid. This paper reviews the social, environmental, and cost-economic impacts of installing large-scale wind energy plants. A valuable review of wind energy technology and its challenges is also presented in this paper, including the effects of wind farms on nearby communities, generation uncertainty, power quality issues, angular and voltage stability, reactive power support, and fault ride-through capability. Besides, socioeconomic, environmental, and electricity market challenges due to the grid integration of wind power are also investigated. Finally, potential technical challenges to integrating large-scale wind energy into the power grid are reviewed regarding current research and their available mitigation techniques.

Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality.

The ability of artificial intelligence to drive toward an intended destination is a key component of an autonomous vehicle. Different paradigms are now being employed to address artificial intelligence advancement. On the one hand, modular pipelines break down the driving model into submodels, such as perception, maneuver planning and control. On the other hand, we used the end-to-end driving method to assign raw sensor data directly to vehicle control signals. The latter is less well-studied but is becoming more popular since it is easier to use. This article focuses on end-to-end autonomous driving, using RGB pictures as the primary sensor input data. The autonomous vehicle is equipped with a camera and active sensors, such as LiDAR and Radar, for safe navigation. Active sensors (e.g., LiDAR) provide more accurate depth information than passive sensors. As a result, this paper examines whether combining the RGB from the camera and active depth information from LiDAR has better results in end-to-end artificial driving than using only a single modality. This paper focuses on the early fusion of multi-modality and demonstrates how it outperforms a single modality using the CARLA simulator.

Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane.

Nowadays, autonomous vehicle is an active research area, especially after the emergence of machine vision tasks with deep learning. In such a visual navigation system for autonomous vehicle, the controller captures images and predicts information so that the autonomous vehicle can safely navigate. In this paper, we first introduced small and medium-sized obstacles that were intentionally or unintentionally left on the road, which can pose hazards for both autonomous and human driving situations. Then, we discuss Markov random field (MRF) model by fusing three potentials (gradient potential, curvature prior potential, and depth variance potential) to segment the obstacles and non-obstacles into the hazardous environment. Since the segment of obstacles is done by MRF model, we can predict the information to safely navigate the autonomous vehicle form hazardous environment on the roadway by DNN model. We found that our proposed method can segment the obstacles accuracy from the blended background road and improve the navigation skills of the autonomous vehicle.