hardware-in-the-loop validation

Addressing the challenge of integrating virtual prototyping with multi-tool co-simulation in complex systems, this paper proposes a SystemC TLM-based virtual platform framework natively supporting the Functional Mock-up Interface (FMI) standard. The framework enables bidirectional data exchange between the virtual platform and external simulation tools—such as control models and physical environment simulators—without modifying the target software, thereby overcoming SystemC’s inherent lack of native FMI support. It represents the first deep integration of FMI at the TLM abstraction level, enabling full-system co-simulation and injection of real-world environmental inputs. Evaluated on a temperature sensor case study, the framework supports early software pre-testing and verification under functional safety standards (e.g., ISO 26262), significantly enhancing test completeness and trustworthiness of embedded software in virtual environments.

Existing CPS co-simulation tools suffer from limited portability, modularity, and automation. To address these limitations, this paper proposes a Python-based programmable co-simulation framework. The framework enables declarative orchestration and runtime dynamic substitution of multi-fidelity heterogeneous components—breaking away from conventional static configuration paradigms. It adopts a componentized architecture, supports distributed communication via ZeroMQ and ROS, and provides standardized adaptation interfaces for third-party platforms (e.g., PX4), thereby enabling cross-platform, reconfigurable co-simulation. Its core innovation is the first-ever declarative component orchestration mechanism, which significantly enhances simulation system reusability, reproducibility, and development efficiency. The framework is validated through co-simulation of unmanned aerial vehicles and autonomous controllers, demonstrating its flexibility and practicality. This work establishes a novel paradigm for CPS benchmark construction and automated evaluation.

This work addresses the integration bottleneck in automotive software development caused by reliance on costly hardware-in-the-loop (HiL) testing when physical hardware is unavailable. The authors propose a virtual testing and integration environment that leverages intelligent agents to automatically generate instruction-accurate SystemC/TLM 2.0 processor models, enabling execution of real software binaries prior to the availability of physical ECUs. A novel closed-loop modeling mechanism, driven by GDB feedback and enhanced through automated differential testing and iterative refinement, significantly improves the behavioral fidelity of virtual ECUs and facilitates shift-left testing. Prototype validation demonstrates that critical CPU behaviors remain within acceptable risk bounds, supporting reproducible testing, fault injection, and verification aligned with functional safety standards, thereby offering a viable pathway toward high-fidelity virtual ECU digital twins.

Embedded IoT system development faces significant challenges, including high cross-domain expertise barriers, heavy manual effort, low efficiency, and error-proneness. To address these, this paper proposes the first end-to-end automated embedded IoT software development framework, integrating large language models (LLMs) with domain-specific embedded knowledge to enable fully autonomous hardware-in-the-loop development. Our key contributions are: (1) a component-aware library parsing method; (2) a domain-knowledge-injected library knowledge generation mechanism; and (3) an automatic programming paradigm ensuring reliable deployment. We evaluate the framework across 71 modules, four hardware platforms, and over 350 tasks. Results show a code accuracy of 95.7% and an end-to-end task success rate of 86.5%, outperforming human experts by up to 53.4% in task completion.

To address the challenges of heterogeneity, fragmented resources, and inefficient collaboration in embedded-system virtual-prototype simulation tools, this paper proposes SUNRISE—a scalable infrastructure for distributed simulation. SUNRISE introduces the Simulation Adapter Abstraction Layer (SAAL), a novel abstraction enabling plug-and-play integration of seven major commercial and open-source simulators. It leverages lightweight containerization (Docker/Kubernetes) and a RESTful microservice architecture to dynamically orchestrate simulation tasks across decentralized computing resources. An open API gateway is designed to facilitate cross-organizational collaboration. Experimental evaluation demonstrates that SUNRISE reduces simulation-task deployment latency by 62%, improves cross-organizational collaboration efficiency by 3×, and achieves a 99.8% API call success rate.

This work addresses the validation gap between simulation and real-world deployment of autonomous driving algorithms, particularly the lack of efficient, high-fidelity testing platforms for safety-critical scenarios. To bridge this gap, the authors propose a mixed-reality hardware-in-the-loop testing framework that seamlessly integrates physical mobile robots with high-fidelity virtual environments, enabling multimodal sensing, vehicle-to-everything (V2X) communication, and large-scale multi-agent collaboration. A key innovation is the coexistence of physical and virtual agents within a unified architecture, coupled with an online learning controller based on control barrier functions (CBFs) that establishes an integrated perception-planning-control safety assurance mechanism. Experimental results demonstrate that the platform significantly enhances the reliability and efficiency of sim-to-real transfer and validates its effectiveness across diverse safety-critical scenarios.

This work proposes a personalized prototyping platform for circuit development to address the limitations of traditional tutorial-based approaches, which rely on rigid, fixed-step instructions that fail to accommodate makers’ individualized building and debugging practices. Central to the platform is a circuit-aware enhanced breadboard integrated with hardware-in-the-loop reconfiguration, context-aware guidance algorithms, and in-situ interactive testing techniques. This integration enables, for the first time, nonlinear, real-time, hardware-context-driven guidance and circuit validation. A user study (N=12) demonstrates that the system effectively aligns with users’ unique construction and troubleshooting behaviors, significantly improving both prototyping efficiency and user experience.

This work addresses the inefficiencies and semantic inconsistencies arising from separately implementing driver and monitor programs in traditional hardware module testing. To overcome this, the authors propose a domain-specific language (DSL) tailored to hardware communication protocols, which enables the unified specification of both driver and monitor logic through an imperative syntax, thereby ensuring their semantic consistency for the first time. Building upon this DSL, they develop a prototype tool that leverages waveform parsing and transaction-level trace inference techniques to accurately reconstruct protocol-compliant transaction sequences from raw signal waveforms. Experimental results demonstrate that the approach significantly improves development efficiency, with further validation planned on real-world interconnect protocols such as Wishbone and AXI-Stream.

This work proposes a novel approach to black-box testing of Functional Mock-up Units (FMUs) by integrating large language models (LLMs) with a human-in-the-loop mechanism. Addressing the inefficiency and poor interpretability of traditional FMU-based dynamic simulation testing—which relies on manually crafted scenarios—the method automatically generates structured Given-When-Then test objectives from FMU interface and functional specifications, and constructs complete test plans comprising input sequences and assertion oracles. Upon simulation execution, the framework produces visualizable logs and statistical evaluation metrics. The approach significantly enhances test design efficiency and result interpretability, facilitates test asset reuse, and demonstrates effectiveness on a lubricating oil cooling system by autonomously generating executable test scenarios and delivering objective-level pass-rate analysis.

This work addresses the challenge that existing AI methods struggle to jointly model the tight coupling between software logic and physical hardware behavior in hardware-in-the-loop (HIL) development of embedded and IoT systems, often leading to deployment failures. To tackle this, the authors propose a skill-oriented agent architecture tailored for HIL scenarios and introduce IoT-SkillsBench, a novel real-hardware evaluation benchmark. The framework systematically assesses AI agents across multiple platforms, peripherals, and task complexities through three agent configurations enhanced by skill augmentation, structured expert knowledge injection, and real-hardware validation. Experimental results demonstrate that, over 378 real-world deployments, agents equipped with human-expert-derived skills achieve near-perfect cross-platform task success rates, substantially outperforming baseline approaches.