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Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving (2410.22313v1)

Published 29 Oct 2024 in cs.CV and cs.RO

Abstract: End-to-end autonomous driving demonstrates strong planning capabilities with large-scale data but still struggles in complex, rare scenarios due to limited commonsense. In contrast, Large Vision-LLMs (LVLMs) excel in scene understanding and reasoning. The path forward lies in merging the strengths of both approaches. Previous methods using LVLMs to predict trajectories or control signals yield suboptimal results, as LVLMs are not well-suited for precise numerical predictions. This paper presents Senna, an autonomous driving system combining an LVLM (Senna-VLM) with an end-to-end model (Senna-E2E). Senna decouples high-level planning from low-level trajectory prediction. Senna-VLM generates planning decisions in natural language, while Senna-E2E predicts precise trajectories. Senna-VLM utilizes a multi-image encoding approach and multi-view prompts for efficient scene understanding. Besides, we introduce planning-oriented QAs alongside a three-stage training strategy, which enhances Senna-VLM's planning performance while preserving commonsense. Extensive experiments on two datasets show that Senna achieves state-of-the-art planning performance. Notably, with pre-training on a large-scale dataset DriveX and fine-tuning on nuScenes, Senna significantly reduces average planning error by 27.12% and collision rate by 33.33% over model without pre-training. We believe Senna's cross-scenario generalization and transferability are essential for achieving fully autonomous driving. Code and models will be released at https://github.com/hustvl/Senna.

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Bridging Vision-LLMs with Autonomous Driving: Insights from Senna

The paper "Senna: Bridging Large Vision-LLMs and End-to-End Autonomous Driving" explores a sophisticated approach to enhancing autonomous driving systems by integrating Large Vision-LLMs (LVLMs) with end-to-end models, specifically designed for improved planning and execution. This paradigm, referred to as Senna, strategically decouples high-level planning from low-level trajectory prediction, thereby leveraging the strengths of both LVLMs' scene understanding and end-to-end models' precision in trajectories.

Framework and Methodology

Senna consists of two primary modules: Senna-VLM and Senna-E2E. Senna-VLM is responsible for high-level planning by generating meta-actions in natural language, which harnesses the power of commonsense reasoning. This is achieved through the use of multi-view inputs and a specialized adapter that compresses image tokens for efficient processing in the LLM. The unique aspect of Senna-VLM is its reliance on language-based decision outputs, minimizing the shortcomings of LVLMs in numeric precision while capitalizing on their strengths in understanding and inferencing.

Senna-E2E, on the other hand, translates these high-level meta-actions into concrete trajectory predictions. This module is built upon established end-to-end models and is designed to interpret the meta-actions generated by Senna-VLM, ensuring that the trajectory predictions are grounded in both data-driven insights and integrated decision-making logic.

Experimental Results

Comprehensive experiments conducted on two extensive datasets, DriveX and nuScenes, illustrate the efficacy of Senna's framework. Notably, Senna demonstrates a significant reduction in planning errors and collision rates compared to existing methods. On the nuScenes validation dataset, Senna reports a 27.12% decrease in planning error and a 33.33% reduction in collision rates, highlighting its superior performance in real-world driving scenarios.

Moreover, the paper emphasizes the effective transferability and cross-scenario generalization of the proposed model, leveraging pre-trained models on larger datasets like DriveX to achieve adaptable performance in different environments.

Theoretical Implications and Future Directions

The integration of LVLMs with end-to-end models opens new avenues in autonomous driving, particularly in enhancing situational awareness and decision-making under complex conditions. The structured approach adopted by Senna allows for a reduction in learning complexity and enhances interpretability—a significant challenge in traditional end-to-end systems.

Looking ahead, the potential for further optimizing LVLMs specifically for multi-modal, multi-image tasks in driving contexts is immense. By advancing the training regimes and data curation strategies, the autonomous driving systems can be made to generalize better across diverse and rare scenarios. Exploring this path could improve model robustness and safety, inching closer towards fully autonomous capabilities.

Conclusion

Senna provides a tangible step forward in the integration of sophisticated LLMs into the field of autonomous driving, offering both strong empirical results and a framework that boasts enhanced interpretability and precision. This dual-layered approach to planning marks a pivotal point in understanding and executing driving tasks, illuminating promising pathways for future AI-enabled autonomous systems.

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References (73)
  1. Y. Wang, V. C. Guizilini, T. Zhang, Y. Wang, H. Zhao, and J. Solomon, “Detr3d: 3d object detection from multi-view images via 3d-to-2d queries,” in CoRL, 2022.
  2. Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang et al., “Planning-oriented autonomous driving,” in CVPR, 2023.
  3. B. Jiang, S. Chen, Q. Xu, B. Liao, J. Chen, H. Zhou, Q. Zhang, W. Liu, C. Huang, and X. Wang, “Vad: Vectorized scene representation for efficient autonomous driving,” in ICCV, 2023.
  4. J. Philion and S. Fidler, “Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,” in ECCV, 2020.
  5. Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Q. Yu, and J. Dai, “Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,” arXiv preprint arXiv:2203.17270, 2022.
  6. B. Liao, S. Chen, X. Wang, T. Cheng, Q. Zhang, W. Liu, and C. Huang, “Maptr: Structured modeling and learning for online vectorized hd map construction,” arXiv preprint arXiv:2208.14437, 2022.
  7. Y. Chai, B. Sapp, M. Bansal, and D. Anguelov, “Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction,” arXiv preprint arXiv:1910.05449, 2019.
  8. J. Gu, C. Hu, T. Zhang, X. Chen, Y. Wang, Y. Wang, and H. Zhao, “Vip3d: End-to-end visual trajectory prediction via 3d agent queries,” arXiv preprint arXiv:2208.01582, 2022.
  9. B. Jiang, S. Chen, X. Wang, B. Liao, T. Cheng, J. Chen, H. Zhou, Q. Zhang, W. Liu, and C. Huang, “Perceive, interact, predict: Learning dynamic and static clues for end-to-end motion prediction,” arXiv preprint arXiv:2212.02181, 2022.
  10. M. Toromanoff, E. Wirbel, and F. Moutarde, “End-to-end model-free reinforcement learning for urban driving using implicit affordances,” in CVPR, 2020.
  11. A. Prakash, K. Chitta, and A. Geiger, “Multi-modal fusion transformer for end-to-end autonomous driving,” in CVPR, 2021.
  12. S. Hu, L. Chen, P. Wu, H. Li, J. Yan, and D. Tao, “St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning,” in ECCV, 2022.
  13. H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” in NeurIPS, 2024.
  14. J. Bai, S. Bai, S. Yang, S. Wang, S. Tan, P. Wang, J. Lin, C. Zhou, and J. Zhou, “Qwen-vl: A frontier large vision-language model with versatile abilities,” arXiv preprint arXiv:2308.12966, 2023.
  15. W. Wang, Q. Lv, W. Yu, W. Hong, J. Qi, Y. Wang, J. Ji, Z. Yang, L. Zhao, X. Song et al., “Cogvlm: Visual expert for pretrained language models,” arXiv preprint arXiv:2311.03079, 2023.
  16. Z. Chen, J. Wu, W. Wang, W. Su, G. Chen, S. Xing, M. Zhong, Q. Zhang, X. Zhu, L. Lu et al., “Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks,” in CVPR, 2024.
  17. J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al., “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774, 2023.
  18. M. M. Botvinick, “Hierarchical models of behavior and prefrontal function,” Trends in cognitive sciences, 2008.
  19. E. Koechlin, C. Ody, and F. Kouneiher, “The architecture of cognitive control in the human prefrontal cortex,” Science, 2003.
  20. D. Badre, “Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes,” Trends in cognitive sciences, 2008.
  21. Z. Xu, Y. Zhang, E. Xie, Z. Zhao, Y. Guo, K. K. Wong, Z. Li, and H. Zhao, “Drivegpt4: Interpretable end-to-end autonomous driving via large language model,” arXiv preprint arXiv:2310.01412, 2023.
  22. L. Chen, O. Sinavski, J. Hünermann, A. Karnsund, A. J. Willmott, D. Birch, D. Maund, and J. Shotton, “Driving with llms: Fusing object-level vector modality for explainable autonomous driving,” arXiv preprint arXiv:2310.01957, 2023.
  23. W. Wang, J. Xie, C. Hu, H. Zou, J. Fan, W. Tong, Y. Wen, S. Wu, H. Deng, Z. Li et al., “Drivemlm: Aligning multi-modal large language models with behavioral planning states for autonomous driving,” arXiv preprint arXiv:2312.09245, 2023.
  24. S. Wang, Z. Yu, X. Jiang, S. Lan, M. Shi, N. Chang, J. Kautz, Y. Li, and J. M. Alvarez, “Omnidrive: A holistic llm-agent framework for autonomous driving with 3d perception, reasoning and planning,” arXiv preprint arXiv:2405.01533, 2024.
  25. S. Frieder, L. Pinchetti, R.-R. Griffiths, T. Salvatori, T. Lukasiewicz, P. Petersen, and J. Berner, “Mathematical capabilities of chatgpt,” in NeurIPS, 2024.
  26. D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt, “Measuring mathematical problem solving with the math dataset,” arXiv preprint arXiv:2103.03874, 2021.
  27. X. Tian, J. Gu, B. Li, Y. Liu, C. Hu, Y. Wang, K. Zhan, P. Jia, X. Lang, and H. Zhao, “Drivevlm: The convergence of autonomous driving and large vision-language models,” arXiv preprint arXiv:2402.12289, 2024.
  28. J. Lin, H. Yin, W. Ping, P. Molchanov, M. Shoeybi, and S. Han, “Vila: On pre-training for visual language models,” in CVPR, 2024.
  29. Y. Zhou, L. Huang, Q. Bu, J. Zeng, T. Li, H. Qiu, H. Zhu, M. Guo, Y. Qiao, and H. Li, “Embodied understanding of driving scenarios,” arXiv preprint arXiv:2403.04593, 2024.
  30. H. Liu, C. Li, Y. Li, and Y. J. Lee, “Improved baselines with visual instruction tuning,” in CVPR, 2024.
  31. C. Sima, K. Renz, K. Chitta, L. Chen, H. Zhang, C. Xie, P. Luo, A. Geiger, and H. Li, “Drivelm: Driving with graph visual question answering,” arXiv preprint arXiv:2312.14150, 2023.
  32. D. Wu, W. Han, T. Wang, Y. Liu, X. Zhang, and J. Shen, “Language prompt for autonomous driving,” arXiv preprint arXiv:2309.04379, 2023.
  33. T. Qian, J. Chen, L. Zhuo, Y. Jiao, and Y.-G. Jiang, “Nuscenes-qa: A multi-modal visual question answering benchmark for autonomous driving scenario,” in AAAI, 2024.
  34. H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in CVPR, 2020.
  35. B. Paden, M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli, “A survey of motion planning and control techniques for self-driving urban vehicles,” IEEE Transactions on intelligent vehicles, 2016.
  36. S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann et al., “Stanley: The robot that won the darpa grand challenge,” Journal of field Robotics, 2006.
  37. C. Urmson, J. Anhalt, D. Bagnell, C. Baker, R. Bittner, M. Clark, J. Dolan, D. Duggins, T. Galatali, C. Geyer et al., “Autonomous driving in urban environments: Boss and the urban challenge,” Journal of field Robotics, 2008.
  38. F. Codevilla, E. Santana, A. M. López, and A. Gaidon, “Exploring the limitations of behavior cloning for autonomous driving,” in ICCV, 2019.
  39. D. A. Pomerleau, “Alvinn: An autonomous land vehicle in a neural network,” in NeurIPS, 1988.
  40. Z. Zhang, A. Liniger, D. Dai, F. Yu, and L. Van Gool, “End-to-end urban driving by imitating a reinforcement learning coach,” in ICCV, 2021.
  41. X. Jia, Y. Gao, L. Chen, J. Yan, P. L. Liu, and H. Li, “Driveadapter: Breaking the coupling barrier of perception and planning in end-to-end autonomous driving,” in ICCV, 2023.
  42. B. Liao, S. Chen, Y. Zhang, B. Jiang, Q. Zhang, W. Liu, C. Huang, and X. Wang, “Maptrv2: An end-to-end framework for online vectorized hd map construction,” arXiv preprint arXiv:2308.05736, 2023.
  43. S. Chen, B. Jiang, H. Gao, B. Liao, Q. Xu, Q. Zhang, C. Huang, W. Liu, and X. Wang, “Vadv2: End-to-end vectorized autonomous driving via probabilistic planning,” arXiv preprint arXiv:2402.13243, 2024.
  44. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in NeurIPS, 2017.
  45. T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al., “Language models are few-shot learners,” in NeurIPS, 2020.
  46. H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar et al., “Llama: Open and efficient foundation language models,” arXiv preprint arXiv:2302.13971, 2023.
  47. R. Anil, A. M. Dai, O. Firat, M. Johnson, D. Lepikhin, A. Passos, S. Shakeri, E. Taropa, P. Bailey, Z. Chen et al., “Palm 2 technical report,” arXiv preprint arXiv:2305.10403, 2023.
  48. A. Yang, B. Yang, B. Hui, B. Zheng, B. Yu, C. Zhou, C. Li, C. Li, D. Liu, F. Huang et al., “Qwen2 technical report,” arXiv preprint arXiv:2407.10671, 2024.
  49. D. Zhu, J. Chen, X. Shen, X. Li, and M. Elhoseiny, “Minigpt-4: Enhancing vision-language understanding with advanced large language models,” arXiv preprint arXiv:2304.10592, 2023.
  50. W. Dai, J. Li, D. Li, A. M. H. Tiong, J. Zhao, W. Wang, B. Li, P. Fung, and S. Hoi, “Instructblip: Towards general-purpose vision-language models with instruction tuning,” 2023.
  51. A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., “Learning transferable visual models from natural language supervision,” in ICML, 2021.
  52. Y. Fang, W. Wang, B. Xie, Q. Sun, L. Wu, X. Wang, T. Huang, X. Wang, and Y. Cao, “Eva: Exploring the limits of masked visual representation learning at scale,” in CVPR, 2023.
  53. J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in ICML, 2022.
  54. J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” in ICML, 2023.
  55. C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. Le, Y.-H. Sung, Z. Li, and T. Duerig, “Scaling up visual and vision-language representation learning with noisy text supervision,” in ICML, 2021.
  56. J. Lu, D. Batra, D. Parikh, and S. Lee, “Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks,” in NeurIPS, 2019.
  57. H. Tan and M. Bansal, “Lxmert: Learning cross-modality encoder representations from transformers,” arXiv preprint arXiv:1908.07490, 2019.
  58. P. Wang, S. Bai, S. Tan, S. Wang, Z. Fan, J. Bai, K. Chen, X. Liu, J. Wang, W. Ge et al., “Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution,” arXiv preprint arXiv:2409.12191, 2024.
  59. H. Sha, Y. Mu, Y. Jiang, L. Chen, C. Xu, P. Luo, S. E. Li, M. Tomizuka, W. Zhan, and M. Ding, “Languagempc: Large language models as decision makers for autonomous driving,” arXiv preprint arXiv:2310.03026, 2023.
  60. A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in CoRL, 2017.
  61. D. Wu, W. Han, T. Wang, X. Dong, X. Zhang, and J. Shen, “Referring multi-object tracking,” in CVPR, 2023.
  62. T. Deruyttere, S. Vandenhende, D. Grujicic, L. Van Gool, and M.-F. Moens, “Talk2car: Taking control of your self-driving car,” arXiv preprint arXiv:1909.10838, 2019.
  63. J. Kim, A. Rohrbach, T. Darrell, J. Canny, and Z. Akata, “Textual explanations for self-driving vehicles,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 563–578.
  64. L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. Xing et al., “Judging llm-as-a-judge with mt-bench and chatbot arena,” in NeurIPS, 2023.
  65. D. Hendrycks and K. Gimpel, “Gaussian error linear units (gelus),” arXiv preprint arXiv:1606.08415, 2016.
  66. N. D. Ratliff, J. A. Bagnell, and M. A. Zinkevich, “Maximum margin planning,” in Proceedings of the 23rd international conference on Machine learning, 2006, pp. 729–736.
  67. W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, and R. Urtasun, “End-to-end interpretable neural motion planner,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 8660–8669.
  68. P. Hu, A. Huang, J. Dolan, D. Held, and D. Ramanan, “Safe local motion planning with self-supervised freespace forecasting,” in CVPR, 2021.
  69. T. Khurana, P. Hu, A. Dave, J. Ziglar, D. Held, and D. Ramanan, “Differentiable raycasting for self-supervised occupancy forecasting,” in ECCV, 2022.
  70. K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in ACL, 2002.
  71. R. Vedantam, C. Lawrence Zitnick, and D. Parikh, “Cider: Consensus-based image description evaluation,” in CVPR, 2015.
  72. S. Banerjee and A. Lavie, “Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,” in Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, 2005.
  73. Z. Li, Z. Yu, S. Lan, J. Li, J. Kautz, T. Lu, and J. M. Alvarez, “Is ego status all you need for open-loop end-to-end autonomous driving?” in CVPR, 2024.
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Authors (9)
  1. Bo Jiang (235 papers)
  2. Shaoyu Chen (26 papers)
  3. Bencheng Liao (20 papers)
  4. Xingyu Zhang (68 papers)
  5. Wei Yin (57 papers)
  6. Qian Zhang (308 papers)
  7. Chang Huang (46 papers)
  8. Wenyu Liu (146 papers)
  9. Xinggang Wang (163 papers)
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GitHub

  1. GitHub - hustvl/Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving (96 stars)