Autonomous Systems in Transport and Industrial Vehicles: Current Status and Challenges

This narrative review analyses the current state of the art in autonomous systems for road transport and industrial environments (logistics, manufacturing and agriculture). The literature was identified mainly through searches of the Scopus and Web of Science databases (final search: July 2026), complemented by standards and regulatory documents; the search and selection procedure and its limitations are reported. The paper proposes a two-dimensional classification that links the domain of use (industrial and logistics applications, shared mobility on fixed routes and road transport of persons) with the degree of autonomy, using the SAE J3016 levels for on-road vehicles and operational-design-domain descriptions and domain-specific safety frameworks for AGVs, AMRs, drones and agricultural machinery; the technical differences between industrial and road operating environments are compared explicitly. The core technologies are then reviewed and compared: sensing and perception (passive and active sensors, multi-sensor fusion), trajectory planning and decision-making, machine learning and reinforcement learning (including human-in-the-loop approaches), testing, simulation and validation, V2V/V2I/V2X connectivity and ADAS. Comparative tables summarise the evaluation data, metrics, operating conditions and limitations of representative methods; their possible transfer to industrial environments is discussed as a hypothesis that requires industrial validation, not as a demonstrated result. Challenges related to decision-making under uncertainty, ethics, fragmented regulation and human factors, including driver state, emotions and trust, are discussed in relation to the evidence reviewed. Finally, each identified gap is translated into research questions, measurable requirements and validation approaches, and the limits of the review’s coverage are stated.