Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
Computer vision is one of the key technologies of advanced driver assistance systems (ADAS), but the incorporation of a vision-based driver assistance system (still) poses a great challenge due to the special characteristics of the algorithms, the neural network architecture, the constraints, and the strict hardware/software requirements that need to be met. The aim of this study is to show the influence of image resolution in traffic lane detection using a virtual dataset from virtual simulation environment (CARLA) combined with a real dataset (TuSimple), considering four performance parameters: Mean Intersection over Union (mIoU), F1 precision score, Inference time, and processed frames per second (FPS). By using a convolutional neural network (U-Net) specifically designed for image segmentation tasks, the impact of different input image resolutions (512 × 256, 640 × 320, and 1024 × 512) on the efficiency of traffic line detection and on computational efficiency was analyzed and presented. Results indicate that a resolution of 512 × 256 yields the best trade-off, offering high mIoU and F1 scores while maintaining real-time processing speeds on a standard CPU. A key contribution of this work is the demonstration that combining synthetic and real datasets enhances model performance, especially when real data is limited. The novelty of this study lies in its dual analysis of simulationbased data and image resolution as key factors in training effective lane detection systems. These findings support the use of synthetic environments in training neural networks for autonomous driving applications....
The rapid devolopment of Internet of Vehicles (IoV) and Autonomous Connected Vehicles (ACVs) has increased the complexity of in-vehicle networks, exposing security vulnerabilities in traditional Controller Area Network (CAN) systems. CAN security faces dual challenges: stringent computational constraints imposed by automotive functional safety requirements and the impracticality of protocol modifications in multi-device networks. To address this, we propose a lightweight intrusion detection algorithm leveraging information entropy to analyze side-channel CAN message ID distributions. Evaluated in terms of detection accuracy, false positive rate, and sensitivity to bus load variations, the algorithm was implemented on an NXP MPC-5748G embedded platform through the AutoSar Framework. Experimental results demonstrate robust performance under low computational resources, achieving high detection accuracy with high recall (>80%) even at 10% bus load fluctuation thresholds. This work provides a resource-efficient security framework compatible with existing CAN infrastructures, effectively balancing attack detection efficacy with the operational constraints of automotive embedded systems....
This work systematically explores the impact of spark plug electrode number on engine performance and environmental effects, including noise, vibration, fuel consumption, and exhaust emissions. Indicators of combustion efficiency and mechanical health are engine vibration and noise; emissions directly affect ecological sustainability. Four-electrode spark plugs reduce vibration by 10%, noise by 5%, and fuel economy by 15%, according to experimental results showing they outperform single-electrode designs. Especially four-electrode designs also lower harmful hydrocarbon (HC) and carbon monoxide (CO) emissions by up to 20%, indicating more complete combustion and providing significant environmental benefits through lower air pollution and greenhouse gas emissions. Reduced exhaust temperatures of surface discharge plugs indicate better combustion efficiency and perhaps help with decarbonization. With poorer emission profiles, twoand three-electrode configurations raise fuel consumption, noise, and vibration. Reduced quenching effects, improved spark distribution, and accelerated flame propagation all help to explain enhanced combustion efficiency in multi-electrode designs and so affect the fundamental combustion chemistry. These results highlight the possibilities of four-electrode spark plugs to improve engine performance and reduce environmental impact, providing information for automotive engineers and legislators aiming at strict emissions standards (e.g., Euro 7) and sustainability targets. With an eye toward the chemical processes involved, additional study is required to investigate electrode geometry, material innovations, and lifetime environmental impacts....
How to use efficient and accurate methods to estimate the capacity of lithium batteries has always been an important research topic. Traditional capacity estimation methods are time-consuming and require strict experimental conditions, making them unsuitable for real-time applications. This article introduces the concept of the inflection point of the charge/discharge curve in the SOC-V curve and proposes a fast estimation method for battery capacity by combining the advantages of the IC curve peak and SOC inflection point methods. By analyzing the charge and discharge data of grouped batteries, it was found that there is a certain correspondence between the inflection point of the SOC-V curve and the peak point of the IC curve. This relationship remains stable during battery aging and can provide a reliable basis for battery SOH evaluation, further improving the estimation accuracy of SOH. This method significantly reduces experimental time, is more suitable for practical applications, and provided an efficient and practical technical means for battery performance evaluation....
Path tracking control is a key technology in the research of intelligent vehicles. In the path tracking process of intelligent vehicles, there are multiple constraints and time-varying nonlinear system states. To address the problems of low tracking accuracy and poor robustness, a method based on Radau pseudospectral method(RPM) is designed. Firstly, a 4-DOF vehicle model was established. Secondly, the multiple phase Radau pseudospectral method(MPRPM) was used to discretize the control and state variables. Then, the path tracking problem was transformed into a nonlinear programming problem. Finally, the method was compared with other control methods such as Gaussian pseudospectral method(GPM) and linear quadratic regulator (LQR). The simulation results show that the tracking error of the proposed method is 0.075 m while those of the GPM and LQR are 0.029 m and 0.05 m, respectively. The simulation and virtual as well as the real vehicle test results indicate that the method can control the vehicle track the given path while meeting various constraint requirements achieving ideal results and good tracking accuracy....
Loading....