In recent years, artificial intelligence has led to rapid development and application across various industries, which has prompted this. One of the significant developments is the improvement of transportation methods. Accidents involving vehicles frequently result in a high number of fatalities as well as economic damage. Road detection is one of the applications that can be used by self-driving cars. Traffic accidents happen, but artificial intelligence is used in many nations to construct smart cities and apps for self-driving cars. Since public road sign datasets have been used in significant research for road sign identification and analysis, these datasets are particularly significant for training autonomous vehicles. This study records the roads of various cities in Taiwan through road driving. It manually collects traffic signs in Taiwan to create a data set of road signs in Taiwan in daytime environments as well as nighttime environments. Since there is currently no data set of road signs in Taiwan, this study is necessary to create a data set of road signs in Taiwan. The YOLO model is utilized in this work to design road signs in Taiwan for mark detection. The techniques of Contrast Stretching (CS), Histogram Equalization (HE), and Contrast Limited Adaptive Histogram Equalization (CLAHE) are evaluated in a nighttime setting and compared to the original image captured at night. The experimental results show that the best model during the day is YOLO V4 (no flip), the test set mAP is 86.77%, the Precision is 82%, the Recall is 87%, the F1-score is 84%, and the IoU is 63.92%. At night, the CLAHE image method works best in the YOLOv5x model, with a mAP of 86.40%. And YOLOv5 can be used in mobile devices or embedded devices, so this study recommends using CLAHE’s YOLOv5x as the best model at night and used to improve the effect of road sign detection at night.

The popularization of big data makes the enterprise need to store more and more data. The data in the enterprise’s database must be accessed as fast as possible, but the Relational Database (RDB) has the speed limitation due to the join operation. Many enterprises have changed to use a NoSQL database, which can meet the requirement of fast data access. However, there are more than hundreds of NoSQL databases. It is important to select a suitable NoSQL database for a certain enterprise because this decision will affect the performance of the enterprise operations. In this paper, fifteen categories of NoSQL databases will be introduced to find out the characteristics of every category. Some principles and examples are proposed to choose an appropriate NoSQL database for different industries.

Mode 4 of 3GPP Cellular Vehicle-to-Everything (C-V2X) uses a new Sensing-Based Semi-Persistent Scheduling (SB-SPS) algorithm to manage its radio resources. SB-SPS applies a probabilistic approach to provide the resource allocation in the system. The resource keep probability ($P_{rk}$) variable plays an essential role in the resource allocation mechanism. Most of the previous works used a fixed $P_{rk}$ value. However, the Packet Delivery Ratio (PDR) can be improved by adapting the optimal $P_{rk}$ value. Hence, we propose a Fuzzy Inference System (FIS) with two inputs, distance, and Channel State Information (CSI) to determine the suitable $P_{rk}$. The simulation results show that the proposed FIS method outperforms the other methods for sparse and congested road scenarios, with total numbers of vehicles at 200 and 400, respectively.

The modulation coding scheme (MCS) index is the essential configuration parameter in cellular vehicle-to-everything (C-V2X) communication. As referenced by the 3rd Generation Partnership Project (3GPP), the MCS index will dictate the transport block size (TBS) index, which will affect the size of transport blocks and the number of physical resource blocks. These numbers are crucial in the C-V2X resource management since it is also bound to the transmission power used in the system. To the authors’ knowledge, this particular area of research has not been previously investigated. Ultimately, this research establishes the fundamental principles for future studies seeking to use the MCS adaptability in many contexts. In this work, we proposed the application of the reinforcement learning (RL) algorithm, as we used the Q-learning approach to adaptively change the MCS index according to the current environmental states. The simulation results showed that our proposed RL approach outperformed the static MCS index and was able to attain stability in a short number of events.

The use of a static modulation coding scheme (MCS), such as 7, and resource keep probability ($P_{rk}$) value, such as 0.8, was proven to be insufficient to achieve the best packet reception ratio (PRR) performance. Various adaptation techniques have been used in the following years. This work introduces a novel optimization algorithm approach called the fuzzy inference reinforcement learning (FIRL) sequence for adaptive parameter configuration in cellular vehicle-to-everything (C-V2X) mode-4 communication networks. This innovative method combines a Sugeno-type fuzzy inference system (FIS) control system with a Q-learning reinforcement learning algorithm to optimize the PRR as the key metric for overall network performance. The FIRL sequence generates adaptive configuration parameters for $P_{rk}$ and MCS index values each time the Long-Term Evolution (LTE) packet is generated. Simulation results demonstrate the effectiveness of this optimization algorithm approach, achieving up to a 169.83% improvement in performance compared to static baseline parameters.