Autonomous vehicle perception models often prioritize benchmark performance over code quality, production readiness, and long-term maintainability, creating a significant gap between research excellence and real-world deployment in safety-critical systems. This disparity is particularly concerning given the stringent international safety standards that govern such systems. Researchers have identified this issue and are working to address it, recognizing that code quality is crucial for ensuring the reliability and safety of autonomous vehicles. The lack of attention to code quality can lead to difficulties in deployment, maintenance, and updates, which can have serious consequences in safety-critical systems. For instance, a single vulnerability in a perception model can compromise the entire autonomous vehicle system, highlighting the need for robust code quality and security measures. A recent study1 highlights the importance of bridging this gap, emphasizing the need for a more holistic approach to evaluating autonomous vehicle perception models. This approach should consider not only performance metrics but also code quality, production readiness, and long-term maintainability. So what matters to practitioners is that prioritizing code quality and production readiness is essential for ensuring the safe and reliable deployment of autonomous vehicles, which in turn has significant implications for the development of safety-critical systems.