• Integrated artificial intelligence (AI)-driven architectures unify perception, planning, and control for autonomous driving, enabling high-speed desert navigation and urban sensing through networked sensors, probabilistic reasoning, and real-time decision-making [1], [14], [19], [11], [13], [6].

• Autonomy levels and evaluation metrics formalize how to define, compare, and design unmanned systems, guiding tradeoffs and human–automation interaction across platforms [8], [16], [9].

• Engineering processes and hardware–software co-design enable scalable autonomy, from very-large-scale integration (VLSI) qualitative reasoning chips to structured driving software architectures [2], [12], [13], [14].

• Sensing, estimation, and robust perception are central to navigation in cluttered/unstructured environments, leveraging vision, sonar, and sensor fusion on ground and air platforms [14], [1], [17], [11], [13].

• Agent-based and multisensory distributed intelligence extend autonomy to surveillance and urban contexts, integrating distributed sensors and autonomous decision-making [10], [11].