Recently, autonomous mobile robots (AMRs) have begun to be used in the delivery of goods, but one of the biggest challenges faced in this field is the navigation system that guides a robot to its destination. The navigation system must be able to identify objects in the robot's path and take evasive actions to avoid them. Developing an object detection system for an AMR requires a deep learning model that is able to achieve a high level of accuracy, with fast inference times, and a model with a compact size that can be run on embedded control systems. Consequently, object recognition requires a convolutional neural network (CNN)-based model that can yield high object classification accuracy and process data quickly. This paper introduces a new CNN-based object detection system for an AMR that employs real-world vehicle datasets. First, we create original real-world datasets of images from Banda Aceh city. We then develop a new CNN-based object identification system that is capable of identifying cars, motorcycles, people, and rickshaws under morning, afternoon, and evening lighting conditions. An SSD Mobilenetv2 FPN Lite 320 × 320 architecture is employed for retraining using these real-world datasets. Quantitative and qualitative performance indicators are then applied to evaluate the CNN model. Training the pre-trained SSD Mobilenetv2 FPN Lite 320 × 320 model improves its classification and detection accuracy, as indicated by its performance results. We conclude that the proposed CNN-based object detection system has the potential for use in an AMR.

Device-to-device (D2D) communication is one of the key enabling technologies for 5G to support many connections efficiently. In a cooperative out-band D2D communication network, relay nodes have an essential role in receiving and forwarding information signals from source (S) to destination (D), making the system more reliable and with higher energy efficiency. Therefore, the relay selection method is very important in determining the best relay among existing relays to achieve maximum performance. This paper proposes a new Adaptive Relay Selection (ARS) scheme for cooperative out-band D2D networks based on channel gain value and transmission link distance. Firstly, the best relay is adaptively selected among N available relays (R) based on the maximum channel gain values between S to R if the channel gain-based signal-to-noise ratio (SNR) is greater than the distance-based SNR. Otherwise, the best relay is selected based on the minimum distance between S to R. We also analyzed an exact closed-form throughput and the total energy consumption required for a cooperative out-band D2D communication system using the Quantization-and-Forward (QF) protocol. The numerical results show that the proposed ARS scheme has a higher throughput than the two previous schemes: maximum channel gain and minimum distance. Furthermore, the proposed ARS can reduce the total energy consumption, which indirectly impacts the resulting energy efficiency level. The proposed ARS scheme achieves higher energy efficiency than the previous schemes, either by link distance or power allocation. So, the proposed ARS scheme for an out-band D2D communication network is an appropriate solution for the next generation of cellular communications.

The perception system is vital for the autonomous navigation of self-driving cars in complex traffic environments. A critical part of this system is object detection, which enables the vehicle to perceive and interpret its surroundings. Object detection in mixed traffic environments becomes particularly problematic due to the increased complexity from diverse interacting objects and other factors such as cluttered, blurred scenes, etc. To address this problem, we present the MXT-Dataset, a novel dataset that captures the complexities of real-world mixed traffic scenarios. We also propose MXT-YOLOv7t, a real-time object detection model designed to efficiently and effectively handle the various challenges in mixed traffic scenarios. This model enhances YOLOv7-tiny P5 by improving the detection rate and reducing inference time. The enhancements include refining the feature extraction network by integrating a lightweight attention mechanism into the ELAN blocks and replacing the activation function in each convolution layer with a sigmoid-weighted linear unit. Then, we evaluate the performance by training and testing the proposed model alongside several other previous YOLO variant models using the same MXT-Dataset. The experimental results show that MXT-YOLOv7t model surpasses YOLOv7-tiny model by up to 6.1%, as measured by mAP@0.5. Furthermore, MXT-YOLOv7t achieves a recall of up to 0.934 and a precision of 0.937, significantly enhancing detection accuracy while maintaining low computational complexity. Therefore, the proposed model effectively performs real-time detection in complex traffic scenarios, offering a lightweight design with high detection accuracy, fast inference times, and minimal computational cost.