## 6 Discussion and Conclusions  
This study presents a dynamic weight model predictive control (DWMPC) strategy for adaptive cruise control (ACC) in electric vehicles, aimed at real-time optimization of tracking accuracy, safety, energy efficiency, and comfort. The core innovation lies in the integration of fuzzy logic with a four-mode switching mechanism, enabling context-aware adjustment of MPC weights. Simulations under WLTC and urban scenarios confirm that DWMPC consistently outperforms fixed-weight MPC and PI controllers in both control precision and comfort. Simulations confirm that DWMPC significantly reduces velocity tracking errors and mitigates abrupt acceleration changes during critical events such as emergency braking and stop-and-go traffic. Its dynamic weighting mechanism enables real-time prioritization among competing objectives, striking a flexible balance between safety and energy efficiency, which is an essential requirement for intelligent electric vehicle deployment. A key strength of the proposed approach is its interpretability. The fuzzy-evaluated emergency coefficient offers an interpretable mechanism for adjusting control sensitivity, enhancing reliability compared to black-box models in safety-critical contexts. The four-mode structure also promotes modularity and scalability, facilitating integration with high-level planning or cooperative driving frameworks. Nevertheless, some limitations remain. The fuzzy rule base and emergency thresholds depend on expert knowledge and may require re-tuning for different vehicle platforms. While simulation results confirm real-time feasibility, practical implementation on embedded systems must account for computational load, sensor noise, and actuator delays. Radar and camera-based relative measurements are susceptible to environmental interference, occlusion, and sensor drift, potentially compromising control performance. To mitigate these issues, signal processing techniques such as Kalman or complementary filtering can be adopted to improve perception robustness. Lightweight QP solvers (e.g., OSQP and qpOASES) with warm-start techniques can further reduce computation time and facilitate real-time deployment on embedded hardware. These improvements will be explored in future research to enhance robustness and practical applicability. From a control system design standpoint, this work highlights the effectiveness of scenario-adaptive weight adjustment for multi-objective optimization. The proposed metrics and architecture provide a reusable template for interpretable and flexible ACC design. For policymakers and practitioners, the interpretable structure and real-time feasibility of the proposed control strategy provide a practical foundation for deploying safety-compliant ACC systems under future regulatory frameworks. Future work will extend the framework using reinforcement learning and neural networks to replace static fuzzy mappings with data-driven, self-adaptive mechanisms. To preserve interpretability, hybrid structures that combine rule-based reasoning with learning will be prioritized. Hardware-in-the-loop and real-vehicle testing will also be conducted to assess system robustness under real-world uncertainties such as sensor noise, modeling errors, and actuation constraints, ultimately bridging the gap between simulation and real-world deployment in safety-critical intelligent driving applications. Conceptualization, W.W.; Methodology, W.W.; Software, W.W.; Validation, W.W., L.L. ([PERSON]) and L.L. ([PERSON]); Formal analysis, W.W. and Q.L. ; Investigation, W.W. and L.L. ([PERSON]); Data curation, W.W.; Writing--original draft, W.W.; Writing--review & editing, L.L. ([PERSON]); Visualization, W.W.; Supervision, Q.L. and L.L. ([PERSON]); Project administration, S.Z., C.F. and L.L. ([PERSON]); Funding acquisition, S.Z., C.F. and L.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.