International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | March 2024
p-ISSN: 2395-0072
www.irjet.net
SteerBrains : Moving towards Vehicular Autonomy Omkar Singh1, Priyanshu Prajapati2 1Head of the Department, Department of Data Science, Thakur College of Science and Commerce, Mumbai,
Maharashtra, India 2Student, Department of Data Science, Thakur College of Science and Commerce, Mumbai, Maharashtra, India
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Abstract - The concept of autonomous driving represents
associated with autonomous driving, from perception and decision-making to control and execution. By leveraging state-of-the-art methodologies and cutting-edge technologies, our system aims to push the boundaries of what is possible in autonomous transportation.
a significant advancement in transportation, where artificial intelligence (AI) assumes the role of the driver, navigating vehicles on the road. This paper, titled "SteerBrains: Moving towards Vehicular Autonomy," serves as a platform to discuss the design and development of autonomous systems, drawing insights from personal experiences and partial results obtained. While not presenting comprehensive results for the entire autonomous driving scenario, the paper provides a structured framework for designing such systems, which may serve as a foundation for further research endeavors. Beginning with an overview of the evolution of autonomous driving, the paper examines fundamental principles underlying the concept and explores challenges and considerations in system design. It delves into the potential implications of AI-driven autonomy for road safety, efficiency, and accessibility. Through this exploration, the paper aims to offer valuable insights into the transformative impact of autonomous driving on the future of transportation. Despite presenting partial results, the paper contributes to the broader understanding of autonomous systems' design and lays the groundwork for future advancements in the field.
1.1 Background Our system is built upon a foundation of cutting-edge methodologies, including computer vision for visual perception, Kalman filters for state estimation, and finite state machines for behavior planning. Through the integration of these techniques, we aim to address the multifaceted challenges inherent in autonomous driving, from perception and decision-making to control and execution. 1.2 Tools and Technologies Central to our research is the utilization of the Carla Simulator, a state-of-the-art simulation platform that enables us to replicate real-world driving scenarios in a virtual environment. By leveraging the capabilities of the Carla Simulator, we can evaluate the performance of our autonomous driving algorithms under various conditions, from inclement weather to heavy traffic.
Key Words: Autonomous Vehicles, Autonomous Systems, Driving Automation, Vehicle Control, Self driving cars, Supervised learning, SteerBrains, Localization, Simulator, Carla.
Additionally, our system is implemented and programmed entirely in Python, harnessing the flexibility and versatility of this programming language for the design and development of complex autonomous systems. Operating on a Windows system, our development environment provides the necessary resources and support for software development, testing, and validation.
1. Introduction In recent years, the automotive industry has witnessed a paradigm shift with the emergence of autonomous driving technology. This groundbreaking advancement holds the promise of transforming the way we perceive and interact with transportation, offering unprecedented levels of safety, efficiency, and convenience on our roads. As such, our research endeavors to contribute to this transformative field by developing an innovative autonomous driving system that embodies the latest advancements in technology and methodology.
In the following sections, we delve deeper into the intricacies of our autonomous driving system, detailing the methodologies, algorithms, and experiments conducted. Through empirical evaluation and analysis, we seek to demonstrate the effectiveness and reliability of our system in navigating urban environments autonomously.
Our research represents a comprehensive exploration of autonomous driving, capable of navigating complex environments autonomously. Through meticulous design and implementation, we aim to address the challenges
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