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|Title:||Development of a 3D vehicle driving simulator (cross platform simulation of CAV)||Authors:||Feng, Zhen||Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2021||Publisher:||Nanyang Technological University||Source:||Feng, Z. (2021). Development of a 3D vehicle driving simulator (cross platform simulation of CAV). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158145||Project:||A1127-211||Abstract:||An autonomous vehicle (AV), often known as a driverless car, is a vehicle that can perceive its surroundings and navigate safely without the need for human intervention. The future of this technology could have ripple effects in a variety of sectors and situations. Radar, lidar, sonar, GPS, odometry, and inertial measurement units are among the sensors used by AVs to sense their environment. Advanced control systems evaluate sensor data to establish appropriate navigation routes, obstacles, and necessary signage. Connected and autonomous vehicle (CAV) is a game-changing technology that has the potential to significantly revolutionize our daily lives. In recent years, CAV research has progressed tremendously. The technique has shown promise in terms of enhancing traffic flow stability, safety, and fuel efficiency. Even when only 5% of vehicles are autonomous, previous studies have shown a 40% improvement in traffic flow efficiency and up to a 28% reduction in fuel usage. The testing community is still grappling with how to effectively test and evaluate autonomous cars. To guarantee safe and dependable functioning, methodology adopted has concentrated on the physical elements of a system. However, the design of automated systems necessitates that the logical component, i.e. the system's core, be thoroughly verified as well. A significant amount of recent research has been on extending software testing approaches to automated systems with massive quantities of inputs, such as defect identification or model verification of the fundamental decision algorithm. Though this can give some runtime guarantees for the software's resilience, it provides little or no information on how the automated system will function throughout mission execution. Furthermore, current testing methods do not reveal the external aspects that influence the automated system's decision-making process. Autonomous vehicle systems are growing more intricate, and they must be extensively examined before being deployed. This is a difficult undertaking to guarantee the safety of autonomous vehicle systems in critical scenarios.||URI:||https://hdl.handle.net/10356/158145||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
Updated on Jun 27, 2022
Updated on Jun 27, 2022
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