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--- en:safeav:softsys:summary [2026/04/09 12:29] – airi
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 ====== Summary ======
-**Conclusions:**
+This chapter traces the evolution of software from programmable hardware foundations to a dominant force in modern computing systems. Early advances in hardware programmability—through configuration, programmable logic (e.g., FPGAs), and stored-program processors—enabled a separation between physical implementation and functional behavior. The introduction of stable computer architectures (notably IBM System/360) and operating systems created enduring abstractions that allowed software portability, scalability, and rapid innovation. Over time, networking and open-source ecosystems further accelerated the growth of information technology, establishing software as the central driver of capability across computing platforms.
-This chapter explains how semiconductors and electronics became the foundation of modern autonomous systems across ground, airborne, marine, and space platforms. It shows a common historical pattern: systems began with mostly mechanical or isolated electronic functions, then evolved toward digitized control, networked subsystems, and increasingly autonomous operation. In cars, this meant moving from engine control to chassis, infotainment, electrification, and ADAS; in aircraft, ships, and spacecraft, it meant a similar shift from stand-alone avionics or navigation aids to integrated, safety-critical digital architectures.
+As software methods entered cyber-physical systems (CPS)—including ground, airborne, marine, and space domains—they followed a distinct trajectory shaped by real-time constraints, safety requirements, and physical interaction. Initially introduced to enhance control and diagnostics, software evolved into the core coordinating layer for sensing, decision-making, and actuation, enabling autonomy. This transition was supported by the emergence of real-time operating systems (RTOSes), middleware, and layered software architectures that ensured deterministic behavior and modularity. Across all domains, systems evolved from isolated, hardware-centric designs to distributed, software-intensive platforms, with increasing reliance on standardized frameworks and communication protocols.
-The chapter also emphasizes that autonomy is not just a matter of adding sensors. It requires a full ecosystem of hardware, computation, validation, and governance. Different domains rely on different sensor mixes—such as radar, cameras, LiDAR, GNSS, IMUs, sonar, or star trackers—but all must fuse data and convert it into safe decisions in real time. Because these systems are safety-critical, the chapter highlights the importance of standards such as ISO 26262, IEC 61508, and DO-254, along with validation processes that include calibration, timing analysis, scenario-based testing, simulation, and structured safety cases.
-Finally, the chapter argues that successful autonomous systems depend on more than technical performance: they must also navigate EMI regulation, health and safety oversight, and resilient supply chains. The discussion covers FCC spectrum and emissions compliance, EMC testing, and the role of accredited labs, then moves into supply-chain challenges such as component scarcity, cybersecurity, certification burdens, ethical sourcing, and technology obsolescence. The main takeaway is that autonomous systems are not just advanced machines—they are complex, tightly integrated products whose success depends on coordinated progress in electronics, sensing, safety, validation, and supply chain management.
-**Assessment:**
-^ # ^ Assessment Theme ^ Learning Objective ^ Deliverable ^
-| 1 | Evolution of Electronics in Autonomy | Understand how semiconductors and electronics transformed ground, airborne, marine, and space systems from isolated functions into integrated autonomous architectures. | Paper: comparative essay, or Project: presentation/timeline showing the historical evolution across the four domains. |
-| 2 | Sensor Fusion Design | Explain why autonomous systems require multiple complementary sensors and how sensing choices depend on mission, environment, redundancy, and compute constraints. | Paper: analysis of a sensor stack in one domain, or Project: design a sensing architecture with justification for each sensor and compute element. |
-| 3 | Safety and Governance | Analyze how standards and governance frameworks shape hardware design, certification, and risk management in autonomous systems. | Paper: standards comparison essay, or Project: briefing/chart mapping ISO 26262, IEC 61508, DO-254, and related frameworks to different domains. |
-| 4 | Validation and Verification | Evaluate how validation, timing, KPIs, scenario-based testing, and simulation contribute to trustworthy autonomy validation beyond simple model-level accuracy. | Paper: methodology critique, or Project: create a validation plan with KPIs, scenarios, and simulation/track-test workflow. |
-| 5 | Supply Chain and Productization | Understand how supply chain resilience, certification burden, EMI/EMC compliance, cybersecurity, and obsolescence affect real-world deployment of autonomous systems. | Paper: case-based analysis, or Project: risk-mitigation plan for launching and supporting an autonomous product. |
-**Industries and Companies:**
-^ Type ^ Description ^ Example Players (Companies) ^
-| Semiconductor Manufacturers (Logic & Compute) | Design and manufacture digital logic devices (MCUs, MPUs, SoCs, AI accelerators) that execute perception, planning, and control workloads in autonomous systems. | Intel, NVIDIA, Qualcomm, NXP Semiconductors |
-| Analog & Mixed-Signal Semiconductor Providers | Provide sensing interfaces, power management ICs, ADC/DACs, and signal conditioning required to convert physical signals into digital data. | Texas Instruments, Analog Devices, Infineon Technologies |
-| Power Semiconductor & Wide Bandgap Players | Develop Si, SiC, and GaN devices for high-efficiency power conversion in EVs, aircraft electrification, marine propulsion, and space systems. | Wolfspeed, onsemi, STMicroelectronics |
-| Sensor Manufacturers (Perception Hardware) | Build core sensing modalities (camera, radar, LiDAR, IMU, GNSS, sonar, star trackers) that define system observability and autonomy limits. | Bosch, Continental AG, Velodyne LiDAR, Teledyne Technologies |
-| RF & Communication Chip / Module Providers | Provide connectivity hardware (5G, V2X, satellite comms, radar front-ends) enabling communication and extended perception. | Skyworks Solutions, Qorvo, Broadcom |
-| FPGA & Reconfigurable Compute Vendors | Supply programmable logic for deterministic, safety-critical and adaptable processing in aerospace, defense, and space systems. | AMD, Intel |
-| EDA (Electronic Design Automation) Companies | Provide design, simulation, verification, and sign-off tools spanning chip, package, and PCB levels—critical for hardware validation and production. | Synopsys, Cadence Design Systems, Siemens |
-| Foundries & Advanced Packaging Providers | Fabricate semiconductors and provide advanced packaging technologies for high-performance and reliable systems. | TSMC, Samsung Foundry, Intel Foundry Services |
+The chapter further highlights how software has transformed product development, supply chains, and validation practices. Cyber-physical systems are increasingly influenced by the faster-moving IT ecosystem, adopting open-source components, layered stacks, and continuous update models (e.g., software-defined vehicles). At the same time, safety standards (e.g., ISO 26262, DO-178C) and rigorous verification methods—such as hardware/software co-simulation (MIL, SIL, HIL)—have evolved to address the risks of software-driven behavior. Modern software supply chains are complex, incorporating third-party and open-source dependencies, requiring strong configuration management, traceability, and cybersecurity practices. Overall, the chapter emphasizes a fundamental shift: engineered systems are no longer hardware products with embedded software, but increasingly software platforms embodied in hardware.
+^ Stack Framework ^ Type ^ Core Covered Layers ^ Key Technologies ^ Domain Focus ^ Notes / Differentiation ^
+| ROS 2 | Open-source middleware stack | Middleware, application | DDS, nodes, topics, Gazebo, RViz | Robotics, AV | De facto R&D standard; highly modular |
+| AUTOSAR Adaptive | Automotive software platform | OS, middleware, apps | POSIX OS, SOME/IP, service-oriented | Automotive (ADAS/AV) | Designed for ISO 26262 + OTA updates |
+| AUTOSAR Classic Platform | Embedded real-time stack | HAL, RTOS, basic software | OSEK or RTOS, CAN, ECU abstraction | Automotive ECUs | Deterministic, safety-certified |
+| Apollo | Full autonomy stack | Full stack (perception → control) | Cyber RT, AI models, HD maps | Autonomous driving (L2–L4) | One of the most complete open AV stacks |
+| Autoware | Open AV stack | Full autonomy pipeline | ROS 2, perception, planning modules | Automotive, robotics | Strong academic + industry ecosystem |
+| NVIDIA DRIVE OS | Integrated platform | OS, middleware, AI runtime | CUDA, TensorRT, DriveWorks | Automotive autonomy | Tight HW/SW co-design with GPUs |
+| QNX Neutrino | RTOS middleware | OS, safety layer | POSIX RTOS, microkernel | Automotive, industrial | Strong certification (ASIL-D) |
+| VxWorks | RTOS | OS, middleware | Deterministic RTOS, ARINC653 | Aerospace, defense | Widely used in safety-critical systems |
+| PX4 Autopilot | UAV autonomy stack | Control, middleware, perception | MAVLink, EKF, control loops | UAV / drones | Industry standard for drones |
+| ArduPilot | UAV autonomy stack | Control + navigation | Mission planning, sensor fusion | UAV, marine robotics | Broad vehicle support (air/land/sea) |
+| MOOS-IvP | Marine autonomy stack | Middleware | Behavior-based robotics | Marine robotics | Optimized for low bandwidth environments |
+| DDS (Data Distribution Service) | Middleware standard | Communication layer | QoS messaging, pub-sub | Cross-domain CPS | Backbone of ROS 2 and many systems |
+| AWS RoboMaker | Cloud robotics stack | Cloud, simulation | DevOps, ROS integration | Robotics, AV | Enables CI/CD + simulation workflows |
+| Microsoft AirSim | Simulation stack | Simulation layer | Unreal Engine, physics models | UAV, AV | High-fidelity perception simulation |
+| CARLA | Simulation stack | Simulation layer | OpenDRIVE, sensors, physics | Automotive | Widely used for AV validation |
+| Gazebo | Simulation stack | Simulation integration | Physics engine, ROS integration | Robotics | Standard for ROS-based systems |

en/safeav/softsys/summary.1775726955.txt.gz · Last modified: 2026/04/09 12:29 by airi