Traditional modular pipelines in autonomous driving (AD) frequently suffer from error accumulation and delayed responsiveness during safety-critical events. Although Embodied Intelligence (EI) introduces a paradigm shift through internal “World Models” for proactive risk mitigation, a substantial gap remains between high-level cognitive theories and real-time, safety-certified deployment. This paper bridges that gap by proposing an Embodied System Integration Methodology (ESIM), which translates cognitive models into fielded robotic systems. Grounded in a “Perception-Imagination-Execution” (PIE) cognitive architecture, ESIM treats risk prediction as an uncertainty-driven, counterfactual closed-loop sensorimotor process. Unlike passive prediction models, the framework employs a Bayesian uncertainty-gated mechanism that selectively triggers a World Model to simulate future risk scenarios only when perceptual degradation occurs. We validate this methodology through a multi-paradigm study spanning three distinct levels: an academic prototype on edge computing platforms, an industrial implementation adhering to ASIL-D (Automotive Safety Integrity Level D) constraints, and an open-source simulation platform. The results demonstrate that by applying hardware acceleration and asynchronous pipelines, the ESIM framework consistently maintains end-to-end latencies within 10–20 ms across heterogeneous hardware. We explicitly address the engineering trade-offs in latency, hardware heterogeneity, and optimization, and establish mathematically grounded probabilistic safety boundaries for black-box neural architectures. Finally, we discuss the framework’s scalability in extreme scenarios, coupling with SLAM pipelines, privacy-preserving federated learning, and generalization potential in the low-altitude economy.
The dynamic job shop scheduling problem (DJSSP) is critical for optimizing production efficiency in intelligent manufacturing systems under dynamic constraints. Traditional approaches, including heuristic dispatching rules (HDRs) and evolutionary hyper-heuristics, often struggle to generalize across dynamic and unseen scheduling scenarios. The advent of large language models (LLMs) offers new opportunities for scheduling optimization, yet their deployment in real-world manufacturing is constrained by data privacy concerns that limit the use of online LLM services and by the restricted inference capability of local small-scale models. To address these challenges, this paper proposes a novel feedback-driven self-evolution framework employing dual-stage knowledge distillation. The framework utilizes a feedback-driven refinement mechanism to distill expert knowledge from advanced LLMs, which is then used to fine-tune the Qwen2.5-Coder-7B model into ModouGPT, a specialized model for generating HDRs. Experimental results indicate that our approach performs better than traditional methods and five state-of-the-art LLM-based evolutionary frameworks, particularly in dynamic and unseen scheduling scenarios. ModouGPT demonstrates competitive performance with online LLMs in complex dynamic environments, while ensuring data privacy. This study provides manufacturing enterprises, particularly small and medium-sized enterprises, with a deployable scheduling system that balances intelligence, privacy, and computational efficiency in dynamic job shop environments. Note to Practitioners—DJSSP involves constant disruptions such as machine breakdowns and dynamic order arrivals. Deploying artificial intelligence (AI)-based scheduling in real manufacturing environments remains difficult: cloud-based LLM services risk exposing sensitive production data, while large-scale local models demand prohibitive hardware investments, placing both options out of reach for most enterprises. This paper presents a practical solution. The proposed framework distills scheduling knowledge from a powerful online teacher model into a compact 7B local model, ModouGPT, deployable on a single consumer-grade graphics processing unit (GPU). All scheduling decisions are made entirely on-premises, with no data leaving the factory floor. Practitioners do not need domain expertise to design HDRs. The system automatically generates HDRs tailored to the specific production environment, covering job processing characteristics and dynamic disturbances. An offline HDR library can be built in advance and updated periodically without interrupting production, and new HDRs can be synthesized in under one minute during live operations. This makes the system immediately actionable for engineers seeking to upgrade scheduling capabilities without relying on external LLM services or specialized algorithm designers.
Jin Huang, Zhengqi Shi, Qihao Liu et al.· IEEE Transactions on Automat...· 0 citations
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