Improvement of Safety Sensors in Autonomous Vehicles
| dc.contributor.advisor | Balajti, Istvan | |
| dc.contributor.author | Dina, Sadia Kabir | |
| dc.contributor.department | DE--Műszaki Kar | |
| dc.date.accessioned | 2023-12-20T13:29:37Z | |
| dc.date.available | 2023-12-20T13:29:37Z | |
| dc.date.created | 2023-12-01 | |
| dc.description.abstract | The focus of this research is to focus on the framework for sensor fusion, which allows for selective fusion of sensor inputs in a context aware way. In the case of object detection performed with a challenging factual data base, this will be verified by theoretically, qualitatively and quantitatively analysis. Here we will discuss ways of improving the current fusion architecture. This study would thoroughly examine the purpose, characteristics, benefits and disadvantages of sensors deployed in autonomous vehicles. To assess their effectiveness, benefits and disadvantages, an examination will be carried out on specific types of sensors. In addition, in view of the current software and hardware situation, it would be important to concentrate on clarifying the volatility nature of risk factors which could jeopardize the reliability and efficiency of these sensors. In examining risk assessment methodologies, the importance of a comprehensive and dynamic risk assessment would be emphasized. The intrinsic limits of present sensor technology will be thoroughly discussed by generic concerns, such as restrictions in sensor resolution and range. The need for an active strategy of adapting sensor systems to the continuously evolving environment is highlighted by dynamic risks. This research will also explore some of these risks, e.g. rapid changes in transport infrastructure, new cybersecurity attacks and changing legal requirements. The goal of this study is achieving a seamless integration of autonomous car sensors into our transportation scene requires finding a balance between the cognitive abilities of human drivers and their technical capabilities. A major feature of this study will be to evaluate the way auto sensors are able to make decisions such as human drivers. Human drivers are intuitive decision makers, flexible in unexpected situations and emotionally intelligent compared to autonomous car sensors that focus on accuracy, coherence or real time processing of large amounts of data. In principle, the study would offer a comprehensive investigation of autonomous vehicle sensors and acknowledge complex relationships between risks variables, software, hardware or decision-making processes. In this study, a brief discussion will be made of the future scope for implementing the findings of the research. | |
| dc.description.course | Mechatronical Engineering | en |
| dc.description.degree | BSc/BA | |
| dc.format.extent | 78 | |
| dc.identifier.uri | https://hdl.handle.net/2437/364144 | |
| dc.language.iso | en | |
| dc.rights.access | Hozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében. | |
| dc.subject | Sensor fusion | |
| dc.subject | Autonomous vehicle | |
| dc.subject | Efficient decision making | |
| dc.subject.dspace | DEENK Témalista::Engineering Sciences | |
| dc.title | Improvement of Safety Sensors in Autonomous Vehicles |