Opportunities, Challenges and Future Prospects of Artificial Intelligence in Autonomous Driving

Effects of implementing artificial intelligence in transport

Modern technologies are rapidly advancing, and artificial intelligence is becoming a key driver of transformation in the transportation sector. The implementation of AI leads to increased efficiency, safety, and environmental sustainability of transportation systems, but is also associated with a number of challenges and risks. In recent decades, the world has faced challenges associated with accelerating technological progress and development. Artificial intelligence (AI) has become a key factor in the transformation of various economic sectors, including transportation and logistics. These changes open up new opportunities for improving the efficiency, safety, and sustainability of transportation systems, creating a customer-centric industry, and ensuring individual mobility for all segments of the population, which is particularly important given the growing needs of society and businesses. This article examines the key implications of the implementation of artificial intelligence (AI) technologies in the transportation industry. It analyzes the positive and negative effects, as well as prospects for further development in the context of digitalization and automation of transportation systems.

Future of Transportation Safety: Emerging Technologies, Challenges, And Opportunities

The rapid advancements in intelligent mobility, linked cars, and autonomous transportation systems have elevated transportation safety to the forefront of research priorities. When it comes to monitoring, decision-making, and road safety, traditional transport is being utterly transformed by technologies like AI, ML, the Internet of Things (IoT), V2X communications, edge computing, and autonomous cars. The present review provides an extensive overview of the key concepts of transportation safety, such as VRUs, camera-based perception, AI detection and recognition, tracking and trajectory prediction techniques. In addition, the state-of-the-art in the areas of IoT enabled transportation systems, intelligent vehicles and V2X communication frameworks is discussed to explore how these innovations enable cooperative and connected mobility. Besides that, the main challenges faced by the autonomous vehicles, especially safety and reliability issues, legal and moral concerns, infrastructure issues and human-machine interaction are considered. Moreover, the recent literature in the area of emerging transportation technologies is reviewed in order to examine the most recent trends, advancements and open problems. This analysis demonstrates how critical it is to build a trustworthy transportation ecosystem by integrating intelligent communication networks, artificial intelligence (AI), and edge computing.

Recent Advances in Artificial Intelligence for Smart Vehicles and Intelligent Transportation Systems

: The growing need for intelligent, information-based, and automated transportation systems has been brought about by the rapid progress of intelligent vehicles and Intelligent Transportation Systems (ITS). Artificial Intelligence (AI) has emerged as an indispensable asset for the challenges and opportunities of today’s transportation, improving decision-making, flexibility, and system efficiency. This survey examines breakthroughs in AI techniques applied to smart vehicles and ITS between 2019 and 2026, focusing on Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Computer Vision. The survey also includes the integration of AI with enabling technologies, such as the Internet of Things (IoT), edge computing, and cloud computing, in this instance, to create real-time distributed intelligence in transportation networks. Also, the application fields of autonomous driving, intelligent traffic control, Advanced Driver Assistance Systems (ADAS), intelligent parking, and Vehicle-to-Everything (V2X) communication are covered. The survey showed that other key challenges stemming from data heterogeneity, scale, latency, security, privacy, and model interpretability would need to be resolved for stable deployment. Lastly, AI-fueled Smart Cities, 5G/6G-powered transportation, Digital Twins, and Explainable Artificial Intelligence (XAI) are briefly mentioned as future research directions and novelties. This survey offers a comprehensive overview of AI-based transportation systems and will be of interest to researchers in the field.

Driving into the Future: A Comprehensive Survey of Autonomous Vehicle Technologies

This work maps out the market landscape and identifies hybrid architectures as the current Pareto-optimal solution, balancing operational capability, safety assurance, and immediate regulatory feasibility for L3/L4 automation.

Prospects of intelligent autonomous control technology in aerospace

Artificial Intelligence-Based Mobile Robots: Development Trends and Challenges

The integration of artificial intelligence (AI) with mobile robotics is transforming automation technologies, enabling advancements across industries such as manufacturing, healthcare, and hazardous material handling. As AI-driven mobile robots become increasingly prevalent, their ability to enhance operational efficiency and safety underscores the need for a comprehensive understanding of their underlying methodologies. This study critically examines the integration of nature-inspired optimization algorithms and deep learning techniques in autonomous mobile robotics, addressing key research gaps related to adaptability, real-time decision-making, and energy-efficient operations. A significant contribution of this work is the exploration of the synergistic application of bio-inspired heuristics and AI-driven perception systems, which substantially improve environmental awareness and autonomous navigation. Furthermore, this review highlights existing limitations in computational complexity and ethical considerations within current frameworks, proposing a structured approach to mitigate these challenges. By systematically analyzing recent advancements and outlining future research directions, this study provides valuable insights that contribute to the development of more robust, intelligent, and ethically responsible autonomous robotic systems.