Authors :
Sivaprakesh.J; Madhumita. T; Jaswanth kumar.V; K. Gowri
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/zhskcf82
Scribd :
https://tinyurl.com/4d775cus
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR872
Abstract :
This study explores the advancements and
challenges of AI-powered self-driving cars, specifically in
the context of urban planning, traffic management, and
transportation systems. It investigates the technological
components of autonomous vehicles, including computer
vision, machine learning algorithms, sensor fusion, and
real-time decision-making systems. The research further
delves into the training and learning procedures,
focusing on the use of large datasets, deep neural
networks, and reinforcement learning to continuously
enhance driving capabilities through interaction with the
environment. The goal is to assess the potential of AI to
improve road safety, transit efficiency, and individual
mobility, while acknowledging the obstacles that need to
be overcome for widespread adoption and societal trust.
Keywords :
Artificial Intelligence, Deep Learning, Deep Neural Networks, Transit Efficiency, Automation Challenges.
This study explores the advancements and
challenges of AI-powered self-driving cars, specifically in
the context of urban planning, traffic management, and
transportation systems. It investigates the technological
components of autonomous vehicles, including computer
vision, machine learning algorithms, sensor fusion, and
real-time decision-making systems. The research further
delves into the training and learning procedures,
focusing on the use of large datasets, deep neural
networks, and reinforcement learning to continuously
enhance driving capabilities through interaction with the
environment. The goal is to assess the potential of AI to
improve road safety, transit efficiency, and individual
mobility, while acknowledging the obstacles that need to
be overcome for widespread adoption and societal trust.
Keywords :
Artificial Intelligence, Deep Learning, Deep Neural Networks, Transit Efficiency, Automation Challenges.
