Abstract
End-to-end motion planning based on visual conditional imitation learning includes control prediction and trajectory planning. The former lacks global environment perception, the latter does not consider vehicle kinematic modeling, and both lack interpretability. This paper proposes a method that combines the two. Interpretability is provided for end-to-end networks by generating a semantic bird’s eye view. The high-level features of the bird’s eye view are fed into the trajectory planning branch and the control prediction branch, while the control prediction branch obtains trajectory guidance, making the two tightly integrated and mutually beneficial. In comparison experiments with vision-advanced methods LBC, TCP, and DLSSCIL on the NoCrash benchmark of the Carla simulation, the proposed method obtains an average gain of 13% in complex scenarios.
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References
Argyriou, A., Evgeniou, T., Pontil, M.: Multi-task feature learning. In: Advances in Neural Information Processing Systems 19 (2006)
Bojarski, M., et al.: End to end learning for self-driving cars. CoRR abs/1604.07316 (2016). http://arxiv.org/abs/1604.07316
Casas, S., Sadat, A., Urtasun, R.: MP3: a unified model to map, perceive, predict and plan. CoRR abs/2101.06806 (2021). https://arxiv.org/abs/2101.06806
Chen, D., Zhou, B., Koltun, V., Krähenbühl, P.: Learning by cheating. CoRR abs/1912.12294 (2019). http://arxiv.org/abs/1912.12294
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1724–1734. Association for Computational Linguistics, October 2014. https://doi.org/10.3115/v1/D14-1179. https://aclanthology.org/D14-1179
Codevilla, F., Müller, M., López, A., Koltun, V., Dosovitskiy, A.: End-to-end driving via conditional imitation learning. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 4693–4700 (2018). https://doi.org/10.1109/ICRA.2018.8460487
Codevilla, F., Santana, E., López, A.M., Gaidon, A.: Exploring the limitations of behavior cloning for autonomous driving. CoRR abs/1904.08980 (2019). http://arxiv.org/abs/1904.08980
Dosovitskiy, A., Ros, G., Codevilla, F., López, A.M., Koltun, V.: CARLA: an open urban driving simulator. CoRR abs/1711.03938 (2017). http://arxiv.org/abs/1711.03938
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Laskey, M., et al.: Iterative noise injection for scalable imitation learning. CoRR abs/1703.09327 (2017). http://arxiv.org/abs/1703.09327
Philion, J., Fidler, S.: Lift, splat, shoot: encoding images from arbitrary camera rigs by implicitly unprojecting to 3D. CoRR abs/2008.05711 (2020). https://arxiv.org/abs/2008.05711
Tampuu, A., Matiisen, T., Semikin, M., Fishman, D., Muhammad, N.: A survey of end-to-end driving: architectures and training methods. IEEE Trans. Neural Netw. Learn. Syst. 33(4), 1364–1384 (2022). https://doi.org/10.1109/TNNLS.2020.3043505
Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. CoRR abs/1905.11946 (2019). http://arxiv.org/abs/1905.11946
Wang, T.: Research on motion planning methods based on semantic bird’s eye view (2023)
Wu, P., Jia, X., Chen, L., Yan, J., Li, H., Qiao, Y.: Trajectory-guided control prediction for end-to-end autonomous driving: a simple yet strong baseline (2022)
Yurtsever, E., Lambert, J., Carballo, A., Takeda, K.: A survey of autonomous driving: common practices and emerging technologies. IEEE Access 8, 58443–58469 (2020). https://doi.org/10.1109/ACCESS.2020.2983149
Zhang, J., Ohn-Bar, E.: Learning by watching. CoRR abs/2106.05966 (2021). https://arxiv.org/abs/2106.05966
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Gao, Y., Shi, C., Wang, Y. (2024). End-to-End Motion Planning Based on Visual Conditional Imitation Learning and Trajectory-Guide. In: Jin, H., Pan, Y., Lu, J. (eds) Computer Networks and IoT. IAIC 2023. Communications in Computer and Information Science, vol 2060. Springer, Singapore. https://doi.org/10.1007/978-981-97-1332-5_3
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DOI: https://doi.org/10.1007/978-981-97-1332-5_3
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