Accurate speed estimation of surrounding vehicles is of paramount importance for autonomous driving to prevent potential hazards. This paper emphasizes the critical role of precise speed estimation and presents a novel real-time framework based on deep learning to achieve this from images captured by an onboard camera. The system detects and tracks vehicles using convolutional neural networks and analyzes their trajectories with a tracking algorithm. Vehicle speeds are then accurately estimated using a regression model based on random sample consensus. A synthetic dataset using the CARLA simulator has been generated to validate the presented methodology. The system can simultaneously estimate the speed of multiple vehicles and can be easily integrated into onboard computer systems, providing a cost-effective solution for real-time speed estimation. This technology holds significant potential for enhancing vehicle safety systems, driver assistance, and autonomous driving.
The global transition to sustainable energy systems has placed the use of electric vehicles (EVs) among the areas that might contribute to reducing carbon emissions and optimizing energy usage. This paper presents a bibliometric analysis of the interconnected domains of EVs, arti…
Multi-Access Edge Computing (MEC) is a fundamental component for 5G networks to overcome the latency limitations of traditional cloud computing. However, bringing resources closer to users exposes edge nodes to significant security threats, particularly volumetric Denial of Servi…
In recent years, with the rapid development of science and technology and the substantial improvement of computing power, various deep learning research topics have been promoted. However, existing autonomous driving technologies still face significant challenges in achieving rob…
Collision avoidance and path planning are critical topics in autonomous vehicle development. This paper presents the progressive development of an optimization-based controller for autonomous vehicles using the Control Lyapunov Function–Control Barrier Function–Quadratic Programm…
This study aimed to develop and evaluate a deep learning diagnostic model integrating a convolutional autoencoder (CAE) and ResNet18 for the early and accurate diagnosis of developmental dysplasia of the hip (DDH) in children, addressing the subjectivity of traditional methods. T…
This study investigates the optimization of traffic scheduling in autonomous vehicle networks using time-sensitive networking (TSN), a type of deterministic Ethernet. Ethernet has high bandwidth and compatibility to support various protocols, and its application range is expandin…