Robust object detection in autonomous driving is challenged by inherent limitations of conventional frame-based cameras, such as motion blur and limited dynamic range. In contrast, event-based cameras, which operate asynchronously and capture rapid changes with high temporal resolution and expansive dynamic range, offer a promising augmentation. While the previous research on event-based object detection has predominantly focused on algorithmic enhancements via advanced preprocessing and network optimizations to improve detection accuracy, the practical engineering and integration challenges of deploying these sensors in real-world systems remain underexplored. To address this gap, our study investigates the integration of event-based cameras as a complementary sensor modality in autonomous driving. We adapted a conventional frame-based detection model (YOLOv8) for event-based inputs by training it on the GEN1 dataset, achieving a mean average precision (mAP) of 70.1%, a significant improvement over previous benchmarks. Additionally, we developed a real-time object detection pipeline optimized for event-based data, integrating it into the CARLA simulation environment and ROS for system prototyping. The model was further refined using transfer learning to better adapt to simulation conditions, and the complete pipeline was validated across diverse simulated scenarios to address practical challenges. These results underscore the feasibility of incorporating event cameras into existing perception systems, paving the way for their broader deployment in autonomous vehicle applications.

In this work, the effect of bagasse ash from waste sugarcane industries on American society for testing and materials in carbon steel A36 (ASTM A36) was investigated. The ash caused the formation on the surface of the samples’ carbon sheet, thereby improving the hardness of the materials’ property. The samples were polished and cleaned in preparation for hardness tests (Rockwell) and microstructural investigation. Furthermore, the bagasse was sintered in a furnace at 400 °C for 3 h to bagasse ash. Additionally, the ash was sifted to a sample size of 100 μm and mixed with 100:0, 90:10, and 80:20 ratios of barium carbonate (BaCO₃). The samples were calcined in a pack carburizing box between bagasse ash and BaCO₃ at 950 °C for 8 h. The carburized samples were used to classify by Thermogravimetric Analysis (TGA), Energy Dispersive X-ray Spectroscopy (EDS), Optical Emission Spectroscopy (OMS), and Optical Microscopy (OM), respectively. The results revealed that 88% of carbon by weight of bagasse ash contributes to significant improvement in the mechanical properties of the ASTM A36 steel.