Abstract
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 real-world autonomous driving test road.
Seongjeong Park and **u Pahk contributed equally to this work
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Acknowledgements
This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (21AMDP-C162419-01) and also supported by the DGIST R&D Program of the Ministry of Science and ICT of Korea (23-IT-03).
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Park, S., Pahk, J., Jahn, L.L.F., Lim, Y., An, J., Choi, G. (2024). A Study on Quantifying Sim2Real Image Gap in Autonomous Driving Simulations Using Lane Segmentation Attention Map Similarity. In: Lee, SG., An, J., Chong, N.Y., Strand, M., Kim, J.H. (eds) Intelligent Autonomous Systems 18. IAS 2023. Lecture Notes in Networks and Systems, vol 795. Springer, Cham. https://doi.org/10.1007/978-3-031-44851-5_16
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