Current Issue : January-March Volume : 2025 Issue Number : 1 Articles : 5 Articles
Nowadays, it is essential to find increasingly rapid and efficient design strategies. This approach becomes crucial in the railway industry, where components must be verified according to multiple reference standards, both structurally and dynamically. In this context, the present research activity aims to develop a fast and effective desin procedure based on European reference standards. The goal was to develop the geometry of a motor bogie frame for a tram vehicle, integrating three fundamental tools for development: finite element simulations and topological structural optimization, aWrite Computer Aided Design (CAD) environment, and a multibody environment. Their integration could enhance design accuracy, streamline the traditional design workflow, and support innovation. The optimization process involved the introduction of complex technological constraints, directing the geometry toward production by casting. A tool was developed to automate running dynamics simulations and the output of results for immediate verification of the entire vehicle performance. Finally, the new geometry was tested both structurally and dynamically. The mass was reduced by approximately 7% while ensuring satisfactory mechanical performance. The maximum value of stress was reduced by about 16%. The dynamic performance showed negligible variation, confirming the encouraging outcomes to make this procedure increasingly effective and reliable....
The emergence of automotive-grade LiDARs has given rise to new potential methods to develop novel advanced driver assistance systems (ADAS). However, accurate and reliable parking slot detection (PSD) remains a challenge, especially in the low-light conditions typical of indoor car parks. Existing camera-based approaches struggle with these conditions and require sensor fusion to determine parking slot occupancy. This paper proposes a parking slot detection (PSD) algorithm which utilizes the intensity of a LiDAR point cloud to detect the markings of perpendicular parking slots. LiDAR-based approaches offer robustness in low-light environments and can directly determine occupancy status using 3D information. The proposed PSD algorithm first segments the ground plane from the LiDAR point cloud and detects the main axis along the driving direction using a random sample consensus algorithm (RANSAC). The remaining ground point cloud is filtered by a dynamic Otsu’s threshold, and the markings of parking slots are detected in multiple windows along the driving direction separately. Hypotheses of parking slots are generated between the markings, which are cross-checked with a non-ground point cloud to determine the occupancy status. Test results showed that the proposed algorithm is robust in detecting perpendicular parking slots in well-marked car parks with high precision, low width error, and low variance. The proposed algorithm is designed in such a way that future adoption for parallel parking slots and combination with free-space-based detection approaches is possible. This solution addresses the limitations of camera-based systems and enhances PSD accuracy and reliability in challenging lighting conditions....
Modern automotive systems must satisfy strict reliability requirements. Most real vehicle systems and safety-critical networks have complex interconnections. The sensitivities and probabilistic uncertainties of the reliability of systems with complex interconnections (SwCIs) can be investigated by Monte-Carlo Simulation (MCS). This paper focuses on the sensitivities and parametrical uncertainties of SwCIs’ reliability. The proposed method can be implemented in the investigation of the uncertainties of SwCI reliability, i.e., in the determination of critical system elements and the estimation of the required number of spare parts (RNSP) of the system, which depends on the probability of allowable spare equipment shortage....
This study examines the management of noise, vibration, and harshness (NVH) in electric vehicle (EV) powertrains, considering the challenges of the automotive industry’s transition to electric drivetrains. The growing popularity of electric vehicles brings new NVH challenges as the lack of internal combustion engine noise makes drivetrain noise more prominent. The key to managing NVH in electric vehicle powertrains is understanding the noise from electric motors, inverters, and gear systems. Noise from electric motors, mainly resulting from electromagnetic forces and highfrequency noise generated by inverters, significantly impacts overall NVH performance. This article details sources of mechanical noise and vibration, including gear defects in gear systems and shaft imbalances. The methods presented in the publication include simulation and modeling techniques that help identify and solve NVH difficulties. Tools like multi-body dynamics, the finite element method, and multi-domain simulation are crucial for understanding the dynamic behavior of complex systems. With the support of simulations, engineers can predict noise and vibration challenges and develop effective solutions during the design phase. This study emphasizes the importance of a system-level approach in NVH management, where the entire drivetrain is modeled and analyzed together, not just individual components....
Pose estimation is crucial for ensuring passenger safety and better user experiences in semi- and fully autonomous vehicles. Traditional methods relying on pose estimation from regular color images face significant challenges due to a lack of three-dimensional (3D) information and the sensitivity to occlusion and lighting conditions. Depth images, which are invariant to lighting issues and provide 3D information about the scene, offer a promising alternative. However, there is a lack of strong work in 3D pose estimation from such images due to the time-consuming process of annotating depth images with 3D postures. In this paper, we present a novel approach to 3D human posture estimation using depth and infrared (IR) images. Our method leverages a three-stage fine-tuning process involving simulation data, approximated data, and a limited set of manually annotated samples. This approach allows us to effectively train a model capable of accurate 3D pose estimation with a median error of under 10 cm across all joints, using fewer than 100 manually annotated samples. To the best of our knowledge, this is the first work focusing on vehicle occupant posture detection utilizing only depth and IR data. Our results demonstrate the feasibility and efficacy of this approach, paving the way for enhanced passenger safety in autonomous vehicle systems....
Loading....