2023/06/18 by Seongjeong Park, Park, Seongjeong, Jinu Pahk +9 · 1 citation
Arts and Humanities · Health Professions · Social Sciences · #Computer Vision and Pattern Recognition (cs.CV) #Cultural and Historical Studies #Diverse Topics in Contemporary Research #FOS: Computer and information sciences #Innovation in Digital Healthcare Systems #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2306.10491
openalex publication_date 2023/06/18 · openalex created_date 2023/06/22 · openalex updated_date 2026/07/28
Autonomous driving simulations require highly realistic images. Our preliminary study found that when the CARLA Simulator image was made more like reality by using DCLGAN, the performance of the lane recognition model improved to levels comparable to real-world driving. It was also confirmed that the vehicle's ability to return to the center of the lane after deviating from it improved significantly. However, there is currently no agreed-upon metric for quantitatively evaluating the realism of simulation images. To address this issue, based on the idea that FID (Fréchet Inception Distance) measures the feature vector distribution distance using a pre-trained model, this paper proposes a metric that measures the similarity of simulation road images using the attention map from the self-attention distillation process of ENet-SAD. Finally, this paper verified the suitability of the measurement method by applying it to the image of the CARLA map that implemented a realworld autonomous driving test road.