Optical-Structural Optimization for Condensation Suppression in Automotive Camera Modules.

Cameras have become indispensable sensors in intelligent vehicles, with their deployment steadily increasing across modern automobiles. It is critical for camera modules to have reliable and accurate environmental perception, but a major challenge is condensation inside the modules that severely compromises imaging quality. To address this issue, we performed comprehensive thermodynamics-based simulations to clarify condensation mechanisms and evaluate their impact on optical imaging performance. Based on these insights, we proposed an integrated optical-structural optimization strategy that reduces the internal cavity volume adjacent to the first lens, simultaneously increasing the first lens thickness and the curvature of its internal surface. This strategy both reduces water vapor volume and elevates the temperature of potential condensation zones. The optimized module exhibits markedly improved resistance to condensation compared with the baseline design in the experiment, raising the critical condensation threshold from a sudden temperature drop of 42 °C to over 60 °C. This approach effectively mitigates condensation under harsh environmental conditions without additional cost. Our simple yet effective design is broadly applicable to diverse automotive camera module architectures, thereby enhancing system reliability and improving the overall safety of autonomous driving.