Advancements, Challenges and Safety Implications of AI in Autonomous Vehicles: A Comparative Analysis of Urban vs. Highway Environments

N. S. Abu, W. M. Bukhari, M. H. Adli, Hari Maghfiroh, Alfian Ma’arif

Abstract


This research reviews AI integration in AVs, evaluating its effectiveness in urban and highway settings. Analyzing over 161 studies, it explores advancements like machine learning perception, sensor technology, V2X communication, and adaptive cruise control. It also examines challenges like traffic congestion, pedestrian and cyclist safety, regulations, and technology limitations. Safety considerations include human-AI interaction, cybersecurity, and liability/ethics. The study contributes valuable insights into the latest developments and challenges of AI in AVs, specifically in urban and highway contexts, which will guide future transportation research and decision-making. In urban settings, AI-powered sensor fusion technology helps AVs navigate dynamic traffic safely. On highways, adaptive cruise control systems maintain safe distances, reducing accidents. These findings suggest AI facilitates safer navigation in urban areas and enhances safety and efficiency on highways. While AI integration in AVs holds immense potential, innovative solutions like advanced perception systems and optimized long-range communication are needed to create safer and more sustainable transportation systems.


Keywords


Autonomous Vehicles (AVs); AI-driven Navigation; AI-enable Sensor Fusion; Machine Learning-Based Perception Systems in AVs; Pedestrian and Cyclist Safety; Urban and Highway Transportations.

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References


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DOI: https://doi.org/10.18196/jrc.v5i3.21114

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