A Study on Quantifying Sim2Real Image Gap in Autonomous Driving
  Simulations Using Lane Segmentation Attention Map Similarity
Seongjeong Park Jinu Pahk Lennart Lorenz Freimuth Jahn Yongseob Lim Jinung An Gyeungho Choi
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 (Fr\'echet 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.