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Published Article
AN EVALUATION OF THE NEED FOR A ROBOT TO HANDLE REAL-WORLD DRIVING: STUDYING THE STRATEGIES AND WHAT IT TAKES
ABSTRACT
This
study focused on the need for robots in handling real-world driving by
discussing the strategies, benefits, challenges, and technological requirements
involved in autonomous driving systems. The study explained that robotics and
artificial intelligence have become important innovations in modern
transportation because of the growing demand for safer, faster, and more
efficient mobility systems. Real-world driving involves operating vehicles
under normal traffic, weather, and road conditions, which often expose human
drivers to errors caused by fatigue, distraction, and poor judgment. The study
revealed that robotic driving systems equipped with artificial intelligence,
machine learning, sensors, LiDAR, radar, and computer vision technologies can
improve road safety, reduce traffic congestion, and enhance transportation
efficiency through intelligent decision-making and continuous environmental
monitoring. The study further identified important strategies for adopting
robots in real-world driving, including sensor fusion, smart transportation
infrastructure, testing and simulation, legal regulations, and public awareness
programs. However, challenges such as cybersecurity threats, perception
degradation, edge-case scenarios, and the black-box nature of deep learning
systems were also identified as major limitations affecting the widespread
adoption of autonomous vehicles. The study concluded that despite these
challenges, continuous technological advancement, improved safety systems, and
effective regulatory frameworks can support the successful integration of
robotic systems into real-world driving environments for safer and more
sustainable transportation systems. One of the recommendations made was that
Governments and transportation authorities should invest more in smart road
infrastructure, intelligent traffic systems, and vehicle-to-everything (V2X)
communication technologies to support the effective operation of autonomous
robotic vehicles in real-world driving environments.
KEYWORDS:
Robot, Real-World Driving, Strategies.
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