BVR-AD: Bridging Virtual and Real for Autonomous Driving with Environment Adaptation

In the realm of autonomous driving, bridging the gap between virtual simulations and real-world environments remains a critical challenge. This paper introduces BVR-AD, an advanced framework that employs environment adaptation techniques to integrate virtual and real driving data into a unified semantic space. By leveraging generative adversarial networks (GANs), BVR-AD facilitates the transfer of learned driving knowledge across different environments, thereby enhancing model robustness and applicability. The proposed method focuses on maintaining the semantic integrity of key driving elements, such as lanes, pedestrians, roads, and buildings, across both virtual and real environments. Extensive evaluations on diverse datasets, including Airsim, Udacity, and CARLA, demonstrate BVR-AD's capability to achieve significant performance gains and improved interpretability in autonomous driving tasks. Our results highlight the model's effectiveness in ensuring that representations learned in simulated environments are transferable and effective in real-world applications.