Modern autonomous systems — from self-driving cars and unmanned aerial vehicles (UAVs) to marine robots and industrial co-bots — depend fundamentally on software architectures capable of real-time sensing, decision-making, and control. While mechanical and electronic components define what a system can do, the software stack defines how it does it — how it perceives the world, interprets data, plans actions, and interacts safely with its environment ^[1,2]. Autonomy software differs from conventional embedded or enterprise software in several critical ways:

• It operates under strict real-time constraints.
• It must integrate data from heterogeneous sensors.
• It needs robust fault tolerance and safety compliance.
• It supports continuous learning and adaptation through AI.
• It often spans distributed systems, connecting vehicles, edge servers, and cloud services.

This combination of safety-critical engineering and AI-driven decision-making makes autonomy software one of the most challenging areas in modern computing.

Autonomy software must achieve four key functional objectives ^[3,4]:

• Perception – sensing and interpreting the environment (via LiDAR, radar, cameras, sonar, etc.).
• Localisation – estimating the system’s precise position and orientation in the world.
• Planning – generating paths or behaviours that fulfil mission goals while avoiding obstacles.
• Control – executing actions safely and stably, compensating for environmental changes.

Each of these objectives corresponds to distinct software layers and modules in the autonomy stack.