Perception Sensor Model

Perception sensor models aim to accurately simulate the output of sensors like cameras and lidar, crucial for training autonomous driving systems and other applications requiring robust environmental perception. Current research emphasizes learning-based approaches, including generative models and contrastive learning frameworks, to bridge the gap between simulated and real-world sensor data, often focusing on improving realism and addressing challenges like sim-to-real transfer and environmental effects (e.g., road spray). These models are vital for creating large, diverse training datasets and enabling effective testing of perception algorithms in simulated environments, ultimately improving the safety and reliability of autonomous systems.