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Software quality control in industry often relies on expensive analyses that run continuously in the Continuous Integration (CI) pipeline. Those analyses, such as static analysis for rule-based compliance, can provide important quality signals and often serve as strict gating points in the CI pipeline to ensure strict quality standards. However, strict continuous gating can slow the pipeline especially in the shared development environment. This study investigates the adoption of different software quality control strategies in safety-critical domain. By changing when the analyses take place and how the information can be delivered, we aim to identify a strategy that improves CI throughput while maintaining high standards of software quality. We conducted action research at a large safety-critical software organization for autonomous driving. Through this process, we investigated existing quality strategies and designed a new strategy that combine nightly analysis with feedback mechanism. We evaluate this strategy using both longitudinal data on static analysis issues and development activities, together with interview data with practitioners. The evaluation shows that nightly CI runs together with feedback mechanism helped keep selected quality issues under control during daily development and reduced reliance on clean-up in later stages. It can also improve the teams' follow-up responsibility and development efficiency. This study contributes an empirically grounded quality control strategy that mitigates the tradeoff between strict quality gate and CI pipeline throughput in safety-critical software development. It also identifies the organizational preconditions, implementation instructions and possible social effects for applying similar mechanisms in other environments that rely on expensive quality analyses.