Autonomy Software Stack

A typical autonomy software stack is organised into hierarchical layers, each responsible for a specific subset of functions — from low-level sensor control to high-level decision-making and fleet coordination. Although implementations differ across domains (ground, aerial, marine), the core architectural logic remains similar:

Perception Layer – sensing and understanding the environment.
Localisation Layer – determining position and orientation.
Planning Layer – deciding what actions to take.
Control Layer – executing those actions through actuators.
System Layer – managing communication, hardware, and runtime.
Infrastructure Layer – providing simulation, cloud services, and DevOps.

This layered design aligns closely with both robotics frameworks (ROS 2) and automotive architectures (AUTOSAR Adaptive).

The SCOR Model Typical Autonomy Software Stack — Figure 1: Typical Autonomy Software Stack (Adapted from ^[1] ^[2]

In Figure 1, the main software layers and their functions are depicted.

Hardware Abstraction Layer (HAL) The HAL provides standardised access to hardware resources. It translates hardware-specific details (e.g., sensor communication protocols, voltage levels) into software-accessible APIs. This functionality typically includes:

Managing device drivers for LiDARs, radars, cameras, IMUs, and other devices needed for the autonomous system.
Monitoring power systems and diagnostics, which includes behaviour change triggers if needed.
Providing time-stamped sensor data streams. The real-time or time-stamped data is the essential part of any data-driven system to ensure proper work of time-series analysis algorithms including deep learning methods.
Handling real-time control of actuators (motors, servos, brakes).

HAL ensures portability — software modules remain agnostic to specific hardware vendors or configurations ^[3].

Operating System (OS) and Virtualisation Layer The OS layer manages hardware resources, process scheduling, and interprocess communication (IPC) as well as real-time operation, alert and trigger raising using watchdog processes. Here, data processing parallelisation is one of the keys to ensuring resources for time-critical applications. Autonomous systems often use:

Linux (Ubuntu or Yocto-based) for flexibility.
Real-Time Operating Systems (RTOS) like QNX or VxWorks for safety-critical timing.
Containerization (Docker, Podman) for software isolation and modular deployment.

Time-Sensitive Networking (TSN) extensions and PREEMPT-RT patches ensure deterministic scheduling for mission-critical tasks ^[4].

Middleware / Communication Layer The middleware layer serves as the data backbone of the autonomy stack. It manages communication between distributed software modules, ensuring real-time, reliable, and scalable data flow. IN some of the mentioned architectures middleware is the central distinctive feature of the architecture. Popular middleware technologies:

ROS 2 (Robot Operating System 2): Uses DDS (Data Distribution Service) for modular publish–subscribe communication.
DDS (Data Distribution Service): Industry-standard middleware for real-time, QoS-based data exchange.
CAN, Ethernet, MQTT: Used for in-vehicle and external communication.
Logging and telemetry systems (ROS bags, DDS recorders).
Fault detection and recovery mechanisms.
QoS (Quality of Service) configuration for bandwidth and latency management.

Control & Execution Layer The control layer translates planned trajectories into actuator commands while maintaining vehicle stability. It closes the feedback loop between command and sensor response. Key modules:

Low-level control: PID, LQR, or MPC controllers for steering, throttle, braking, or propulsion.
High-level control: Converts trajectory plans into real-time setpoints.
State estimation: Uses Kalman filters or observers to correct control deviations.

Safety-critical systems often employ redundant controllers and monitor nodes to prevent hazardous conditions ^[5].

Autonomy Intelligence Layer This is the core of decision-making in the stack. It consists of several interrelated subsystems:

Subsystem	Function	Example Techniques / Tools
Perception	Detect and classify objects, lanes, terrain, or obstacles.	CNNs, LiDAR segmentation, sensor fusion.
Localization	Estimate position relative to a global or local map.	SLAM, GNSS, Visual Odometry, EKF.
Planning	Compute feasible, safe paths or behaviours.	A, D, RRT*, Behavior Trees.
Prediction	Provide the environmental behaviour forecast. Usually, it provides an internal dynamics forecast as well.	Recurrent Neural Networks, Bayesian inference.
Decision-making	Choose actions based on mission goals and context.	Finite State Machines, Reinforcement Learning.

These components interact through middleware and run either on edge computers (onboard) or cloud-assisted systems for extended processing ^[6].

Application & Cloud Layer At the top of the stack lies the application layer, which extends autonomy beyond individual vehicles:

Fleet management (monitoring, task assignment).
Over-the-air (OTA) updates for software and firmware.
Cloud-based simulation and analytics.
Data collection for machine learning retraining.

Frameworks like AWS RoboMaker, NVIDIA DRIVE Sim, and Microsoft AirSim bridge onboard autonomy with cloud computation.

^[1] Raj, A., & Saxena, P. (2022). Software architectures for autonomous vehicle development: Trends and challenges. IEEE Access, 10, 54321–54345

^[2] AUTOSAR Consortium. (2023). AUTOSAR Adaptive Platform Specification. AUTOSAR

^[3] Lee, E. A., & Seshia, S. A. (2020). Introduction to Embedded Systems: A Cyber-Physical Systems Approach (3rd ed.). MIT Press.

^[4] Baruah, S., Baker, T. P., & Burns, A. (2012). Real-time scheduling theory: A historical perspective. Real-Time Systems, 28(2–3), 101–155

^[5] Broy, M., et al. (2021). Modeling Automotive Software and Hardware Architectures with AUTOSAR. Springer

^[6] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

en/safeav/softsys/autonomysoftstack.1760702330.txt.gz · Last modified: 2025/10/17 11:58 by agrisnik