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SLEDGE is the first generative simulator for vehicle motion planning trained on real-world driving logs.
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Wymann, B., Dimitrakakisy, C., Sumnery, A., Espié, E., Guionneauz, C.: Torcs: The open racing car simulator (2015)
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2016)
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Althoff, M., Koschi, M., Manzinger, S.: Commonroad: Composable benchmarks for motion planning on roads. In: Proc. IEEE Intelligent Vehicles Symposium (IV) (2017)
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Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: An open urban driving simulator. In: Proc. Conf. on Robot Learning (CoRL) (2017)
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Santurkar, S., Budden, D., Shavit, N.: Generative Compression. arXiv.org 1703.01467
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 5998–6008 (2017)
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Nunez-Iglesias, J., Blanch, A.J., Looker, O., Dixon, M.W.A., Tilley, L.: A new python library to analyse skeleton images confirms malaria parasite remodelling of the red blood cell membrane skeleton. PeerJ (2018)
2018
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Bansal, M., Krizhevsky, A., Ogale, A.S.: Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst. In: Proc. Robotics: Science and Systems (RSS) (2019)
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Chu, H., Li, D., Acuna, D., Kar, A., Shugrina, M., Wei, X., Liu, M.Y., Torralba, A., Fidler, S.: Neural Turtle Graphics for Modeling City Road Layouts. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2019)
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Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., Aila, T.: Improved precision and recall metric for assessing generative models. Advances in Neural Information Processing Systems (NeurIPS) (2019)
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Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. of the European Conf. on Computer Vision (ECCV) (2020)
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Elhousni, M., Lyu, Y., Zhang, Z., Huang, X.: Automatic Building and Labeling of HD Maps with Deep Learning. arXiv.org 2006.00644
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. arXiv.org 2006.11239
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Ding, W., Chen, B., Li, B., Eun, K.J., Zhao, D.: Multimodal safety-critical scenarios generation for decision-making algorithms evaluation. IEEE Robotics and Automation Letters (RA-L) (2021)
2021
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Luo, S., Hu, W.: Diffusion Probabilistic Models for 3D Point Cloud Generation. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
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Mi, L., Zhao, H., Nash, C., Jin, X., Gao, J., Sun, C., Schmid, C., Shavit, N., Chai, Y., Anguelov, D.: Hdmapgen: A hierarchical graph generative model of high definition maps. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
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Wang, J., Pun, A., Tu, J., Manivasagam, S., Sadat, A., Casas, S., Ren, M., Urtasun, R.: Advsim: Generating safety-critical scenarios for self-driving vehicles. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
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Hanselmann, N., Renz, K., Chitta, K., Bhattacharyya, A., Geiger, A.: King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients. In: Proc. of the European Conf. on Computer Vision (ECCV) (2022)
2022
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He, S., Balakrishnan, H.: Lane-level street map extraction from aerial imagery. In: Proc. of the IEEE Winter Conference on Applications of Computer Vision (WACV) (2022)
2022
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Li, Q., Peng, Z., Feng, L., Zhang, Q., Xue, Z., Zhou, B.: Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 45
2022
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Luo, S., Tan, Y., Huang, L., Li, J., Zhao, H.: Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference. arXiv.org 2310.04378
2023
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Peebles, W., Xie, S.: Scalable diffusion models with transformers. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2023)
2023
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Pronovost, E., Ganesina, M.R., Hendy, N., Wang, Z., Morales, A., Wang, K., Roy, N.: Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion. In: Advances in Neural Information Processing Systems (NeurIPS) (2023)
2023
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Shabani, M.A., Hosseini, S., Furukawa, Y.: Housediffusion: Vector floorplan generation via a diffusion model with discrete and continuous denoising. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2023)
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Liu, X., Gong, C., Liu, Q.: Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. arXiv.org 2209.03003
2022
Cited alongside, same era.
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., Van Gool, L.: RePaint: Inpainting using Denoising Diffusion Probabilistic Models. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
Cited alongside, same era.
Nichol, A., Jun, H., Dhariwal, P., Mishkin, P., Chen, M.: Point-E: A System for Generating 3D Point Clouds from Complex Prompts. arXiv.org 2212.08751
2022
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Renz, K., Chitta, K., Mercea, O.B., Koepke, S., Akata, Z., Geiger, A.: Plant: Explainable planning transformers via object-level representations. In: Proc. Conf. on Robot Learning (CoRL) (2022)
2022
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-Resolution Image Synthesis with Latent Diffusion Models. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
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Zeng, X., Vahdat, A., Williams, F., Gojcic, Z., Litany, O., Fidler, S., Kreis, K.: Lion: Latent point diffusion models for 3d shape generation. In: Advances in Neural Information Processing Systems (NeurIPS) (2022)
2022
Cited alongside, same era.
Blattmann, A., Dockhorn, T., Kulal, S., Mendelevitch, D., Kilian, M., Lorenz, D., Levi, Y., English, Z., Voleti, V., Letts, A., Jampani, V., Rombach, R.: Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets. arXiv.org 2311.15127
2023
Cited alongside, same era.
Chen, J., Deng, R., Furukawa, Y.: Polydiffuse: Polygonal shape reconstruction via guided set diffusion models. In: Advances in Neural Information Processing Systems (NeurIPS) (2023)
2023
Cited alongside, same era.
Sun, S., Gu, Z., Sun, T., Sun, J., Yuan, C., Han, Y., Li, D., Ang, Marcelo H., J.: DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch. arXiv.org 2309.14685
2023
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Xu, M., Powers, A., Dror, R., Ermon, S., Leskovec, J.: Geometric latent diffusion models for 3d molecule generation. In: Proc. of the International Conf. on Machine learning (ICML) (2023)
2023
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Zhai, J.T., Feng, Z., Du, J., Mao, Y., Liu, J.J., Tan, Z., Zhang, Y., Ye, X., Wang, J.: Rethinking the open-loop evaluation of end-to-end autonomous driving in nuscenes. arXiv.org 2305.10430
2023
Later among the works it cites.
Zhong, Z., Rempe, D., Chen, Y., Ivanovic, B., Cao, Y., Xu, D., Pavone, M., Ray, B.: Language-guided traffic simulation via scene-level diffusion. In: Proc. Conf. on Robot Learning (CoRL) (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Brooks, T., Peebles, B., Holmes, C., DePue, W., Guo, Y., Jing, L., Schnurr, D., Taylor, J., Luhman, T., Luhman, E., Ng, C., Wang, R., Ramesh, A.: Video generation models as world simulators (2024), https://openai.com/research/video-generation-models-as-world-simulators
2024
Closest in time.
Contributors, N.: Navsim: Data-driven non-reactive autonomous vehicle simulation. https://github.com/autonomousvision/navsim (2024)
2024
Closest in time.
Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F., Podell, D., Dockhorn, T., English, Z., Lacey, K., Goodwin, A., Marek, Y., Rombach, R.: Scaling Rectified Flow Transformers for High-Resolution Image Synthesis. arXiv.org 2403.03206
2024
Closest in time.
Karnchanachari, N., Geromichalos, D., Seang Tan, K., Li, N., Eriksen, C., Yaghoubi, S., Mehdipour, N., Bernasconi, G., Kit Fong, W., Guo, Y., Caesar, H.: Towards learning-based planning: The nuPlan benchmark for real-world autonomous driving. In: Proc. IEEE International Conf. on Robotics and Automation (ICRA) (2024)
2024
Closest in time.
Li, T., Jia, P., Wang, B., Chen, L., Jiang, K., Yan, J., Li, H.: LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving. In: Proc. of the International Conf. on Learning Representations (ICLR) (2024)
2024
Closest in time.
Lin, S., Wang, A., Yang, X.: SDXL-Lightning: Progressive Adversarial Diffusion Distillation. arXiv.org 2402.13929
2024
Closest in time.
Sauer, A., Boesel, F., Dockhorn, T., Blattmann, A., Esser, P., Rombach, R.: Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation. arXiv.org 2403.12015
2024
Closest in time.
Yang, J., Gao, S., Qiu, Y., Chen, L., Li, T., Dai, B., Chitta, K., Wu, P., Zeng, J., Luo, P., Zhang, J., Geiger, A., Qiao, Y., Li, H.: Generalized Predictive Model for Autonomous Driving. arXiv.org 2403.09630
2024
Closest in time.