Active Automotive Augmented Reality Displays Using Reinforcement Learning

In order to enhance driving convenience and safety, automotive Augmented Reality displays, e.g., head-up displays, have garnered attention and are gradually being deployed. However, when vehicles encounter uneven roads, vertical vibrations lead to mismatches between external physical objects and augmented reality overlay images, adversely affect- ing the AR display’s visibility. Resolving the problem is quite challenging because the optical system operates on a nanometer scale and is highly sensitive due to its multifunctional nature involving reflection and refraction through an intermediate medium. This paper aims to address the newly emerging problem of vertical mismatches in automotive AR displays. To tackle this issue, we begin by defining the problem and then examine the effectiveness of traditional control methods, on- policy and off-policy reinforcement learning as potential solu- tions. Finally, we validate our approach through experiments, demonstrating a significant reduction in vertical mismatches and an improvement in the overall visibility of automotive AR displays. Our findings provide valuable insights for enhancing driving convenience and safety in real-world conditions.