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Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction (2302.10873v3)

Published 21 Feb 2023 in cs.CV and cs.LG

Abstract: Real-time, accurate prediction of human steering behaviors has wide applications, from developing intelligent traffic systems to deploying autonomous driving systems in both real and simulated worlds. In this paper, we present ContextVAE, a context-aware approach for multi-modal vehicle trajectory prediction. Built upon the backbone architecture of a timewise variational autoencoder, ContextVAE observation encoding employs a dual attention mechanism that accounts for the environmental context and the dynamic agents' states, in a unified way. By utilizing features extracted from semantic maps during agent state encoding, our approach takes into account both the social features exhibited by agents on the scene and the physical environment constraints to generate map-compliant and socially-aware trajectories. We perform extensive testing on the nuScenes prediction challenge, Lyft Level 5 dataset and Waymo Open Motion Dataset to show the effectiveness of our approach and its state-of-the-art performance. In all tested datasets, ContextVAE models are fast to train and provide high-quality multi-modal predictions in real-time. Our code is available at: https://github.com/xupei0610/ContextVAE.

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References (46)
  1. 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 IEEE Conf. Comp. Vis. Patt. Recogn., 2020, pp. 11 621–11 631.
  2. J. Houston, G. Zuidhof, L. Bergamini, Y. Ye, L. Chen, A. Jain, S. Omari, V. Iglovikov, and P. Ondruska, “One thousand and one hours: Self-driving motion prediction dataset,” in Conf. Robot Learn., 2021, pp. 409–418.
  3. S. Ettinger, S. Cheng, B. Caine, C. Liu, H. Zhao, S. Pradhan, Y. Chai, B. Sapp, C. R. Qi, Y. Zhou, Z. Yang, A. Chouard, P. Sun, J. Ngiam, V. Vasudevan, A. McCauley, J. Shlens, and D. Anguelov, “Large scale interactive motion forecasting for autonomous driving: The Waymo open motion dataset,” in IEEE Int. Conf. Comput. Vis., 2021, pp. 9710–9719.
  4. S. Kolekar, S. Gite, B. Pradhan, and K. Kotecha, “Behavior prediction of traffic actors for intelligent vehicle using artificial intelligence techniques: A review,” IEEE Access, vol. 9, pp. 135 034–135 058, 2021.
  5. S. Konev, K. Brodt, and A. Sanakoyeu, “MotionCNN: A strong baseline for motion prediction in autonomous driving,” in IEEE Conf. Comp. Vis. Patt. Recogn. Workshops, 2021.
  6. Y. Huang, J. Du, Z. Yang, Z. Zhou, L. Zhang, and H. Chen, “A survey on trajectory-prediction methods for autonomous driving,” IEEE Trans. Intell. Veh., vol. 7, no. 3, pp. 652–674, 2022.
  7. A. Kamenev, L. Wang, O. B. Bohan, I. Kulkarni, B. Kartal, A. Molchanov, S. Birchfield, D. Nistér, and N. Smolyanskiy, “PredictionNet: Real-time joint probabilistic traffic prediction for planning, control, and simulation,” in IEEE Int. Conf. Robot. Autom., 2022.
  8. N. Lee, W. Choi, P. Vernaza, C. B. Choy, P. H. S. Torr, and M. Chandraker, “Desire: Distant future prediction in dynamic scenes with interacting agents,” IEEE Conf. Comp. Vis. Patt. Recogn., pp. 2165–2174, 2017.
  9. T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone, “Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data,” in European Conf. Comput. Vis., 2020, pp. 683–700.
  10. Y. Yao, E. Atkins, M. Johnson-Roberson, R. Vasudevan, and X. Du, “BiTraP: Bi-directional pedestrian trajectory prediction with multi-modal goal estimation,” IEEE Robot. Autom. Lett., vol. 6, no. 2, pp. 1463–1470, 2021.
  11. P. Xu, J.-B. Hayet, and I. Karamouzas, “SocialVAE: Human trajectory prediction using timewise latents,” in European Conf. Comput. Vis., 2022, pp. 511–528.
  12. J. L. V. Espinoza, A. Liniger, W. Schwarting, D. Rus, and L. Van Gool, “Deep interactive motion prediction and planning: Playing games with motion prediction models,” in Learning for Dynamics and Control Conference, 2022, pp. 1006–1019.
  13. A. Sadeghian, V. Kosaraju, A. Sadeghian, N. Hirose, H. Rezatofighi, and S. Savarese, “SoPhie: An attentive GAN for predicting paths compliant to social and physical constraints,” in IEEE Conf. Comp. Vis. Patt. Recogn., 2019, pp. 1349–1358.
  14. A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social LSTM: Human trajectory prediction in crowded spaces,” in IEEE Conf. Comp. Vis. Patt. Recogn., 2016, pp. 961–971.
  15. A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi, “Social GAN: Socially acceptable trajectories with generative adversarial networks,” in IEEE Conf. Comp. Vis. Patt. Recogn., 2018, pp. 2255–2264.
  16. ——, “Social GAN: Socially acceptable trajectories with generative adversarial networks,” in IEEE Conf. Comp. Vis. Patt. Recogn., 2018, pp. 2255–2264.
  17. J. Amirian, J.-B. Hayet, and J. Pettré, “Social Ways: Learning multi-modal distributions of pedestrian trajectories with GANs,” in IEEE Conf. Comp. Vis. Patt. Recogn. Workshops, 2019.
  18. N. Deo and M. M. Trivedi, “Convolutional social pooling for vehicle trajectory prediction,” in IEEE Conf. Comp. Vis. Patt. Recogn. Workshops, 2018, pp. 1468–1476.
  19. N. Rhinehart, R. McAllister, K. Kitani, and S. Levine, “PRECOG: Prediction conditioned on goals in visual multi-agent settings,” in IEEE Int. Conf. Comput. Vis., October 2019.
  20. A. Rudenko, L. Palmieri, M. Herman, K. M. Kitani, D. M. Gavrila, and K. O. Arras, “Human motion trajectory prediction: a survey,” Int. J. Rob. Res., vol. 39, no. 8, pp. 895–935, 2020.
  21. T. Phan-Minh, E. C. Grigore, F. A. Boulton, O. Beijbom, and E. M. Wolff, “CoverNet: Multimodal behavior prediction using trajectory sets,” in IEEE Conf. Comp. Vis. Patt. Recogn., 2020, pp. 14 074–14 083.
  22. Y. Chai, B. Sapp, M. Bansal, and D. Anguelov, “Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction,” arXiv preprint arXiv:1910.05449, 2019.
  23. H. Cui, V. Radosavljevic, F.-C. Chou, T.-H. Lin, T. Nguyen, T.-K. Huang, J. Schneider, and N. Djuric, “Multimodal trajectory predictions for autonomous driving using deep convolutional networks,” in IEEE Int. Conf. Robot. Autom., 2019, pp. 2090–2096.
  24. N. Deo and M. M. Trivedi, “Trajectory forecasts in unknown environments conditioned on grid-based plans,” arXiv preprint arXiv:2001.00735, 2020.
  25. R. Girgis, F. Golemo, F. Codevilla, M. Weiss, J. A. D’Souza, S. E. Kahou, F. Heide, and C. Pal, “Latent variable sequential set transformers for joint multi-agent motion prediction,” in Int. Conf. Learn. Repres., 2021.
  26. L. Zhang, P.-H. Su, J. Hoang, G. C. Haynes, and M. Marchetti-Bowick, “Map-adaptive goal-based trajectory prediction,” in Conf. Robot Learn., 2021, pp. 1371–1383.
  27. X. Huang, S. G. McGill, J. A. DeCastro, L. Fletcher, J. J. Leonard, B. C. Williams, and G. Rosman, “DiversityGAN: Diversity-aware vehicle motion prediction via latent semantic sampling,” IEEE Robot. Autom. Lett., vol. 5, no. 4, pp. 5089–5096, 2020.
  28. C. Wang, Y. Wang, M. Xu, and D. Crandall, “Stepwise goal-driven networks for trajectory prediction,” IEEE Robot. Autom. Lett., 2022.
  29. J. Gao, C. Sun, H. Zhao, Y. Shen, D. Anguelov, C. Li, and C. Schmid, “VectorNet: Encoding HD maps and agent dynamics from vectorized representation,” in IEEE Conf. Comp. Vis. Patt. Recogn., 2020, pp. 11 525–11 533.
  30. M. Liang, B. Yang, R. Hu, Y. Chen, R. Liao, S. Feng, and R. Urtasun, “Learning lane graph representations for motion forecasting,” in European Conf. Comput. Vis.   Springer, 2020, pp. 541–556.
  31. S. Shi, L. Jiang, D. Dai, and B. Schiele, “Motion transformer with global intention localization and local movement refinement,” Advances in Neural Information Processing Systems, 2022.
  32. T. Gilles, S. Sabatini, D. Tsishkou, B. Stanciulescu, and F. Moutarde, “Gohome: Graph-oriented heatmap output for future motion estimation,” in IEEE Int. Conf. Robot. Autom., 2022, pp. 9107–9114.
  33. Z. Zhou, J. Wang, Y.-H. Li, and Y.-K. Huang, “Query-centric trajectory prediction,” in Proceedings of the IEEE Conf. Comp. Vis. Patt. Recogn., 2023, pp. 17 863–17 873.
  34. S. Khandelwal, W. Qi, J. Singh, A. Hartnett, and D. Ramanan, “What-if motion prediction for autonomous driving,” arXiv preprint arXiv:2008.10587, 2020.
  35. B. Kim, S. H. Park, S. Lee, E. Khoshimjonov, D. Kum, J. Kim, J. S. Kim, and J. W. Choi, “LaPred: Lane-aware prediction of multi-modal future trajectories of dynamic agents,” in IEEE Conf. Comp. Vis. Patt. Recogn., 2021, pp. 14 636–14 645.
  36. C. Luo, L. Sun, D. Dabiri, and A. Yuille, “Probabilistic multi-modal trajectory prediction with lane attention for autonomous vehicles,” in IEEE/RSJ Int. Conf. Intell. Robots Syst.., 2020, pp. 2370–2376.
  37. H. Zhao, J. Gao, T. Lan, C. Sun, B. Sapp, B. Varadarajan, Y. Shen, Y. Shen, Y. Chai, C. Schmid et al., “TNT: Target-driven trajectory prediction,” in Conf. Robot Learn., 2021, pp. 895–904.
  38. Q. Sun, X. Huang, J. Gu, B. C. Williams, and H. Zhao, “M2I: From factored marginal trajectory prediction to interactive prediction,” in IEEE Conf. Comp. Vis. Patt. Recogn., 2022, pp. 6543–6552.
  39. N. Deo, E. Wolff, and O. Beijbom, “Multimodal trajectory prediction conditioned on lane-graph traversals,” in Conf. Robot Learn., 2021.
  40. B. Varadarajan, A. Hefny, A. Srivastava, K. S. Refaat, N. Nayakanti, A. Cornman, K. Chen, B. Douillard, C. P. Lam, D. Anguelov et al., “Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction,” in IEEE Int. Conf. Robot. Autom., 2022, pp. 7814–7821.
  41. S. Casas, C. Gulino, S. Suo, and R. Urtasun, “The importance of prior knowledge in precise multimodal prediction,” in IEEE/RSJ Int. Conf. Intell. Robots Syst.., 2020, pp. 2295–2302.
  42. W. Zeng, M. Liang, R. Liao, and R. Urtasun, “LaneRCNN: Distributed representations for graph-centric motion forecasting,” in IEEE/RSJ Int. Conf. Intell. Robots Syst.., 2021, pp. 532–539.
  43. T. Gilles, S. Sabatini, D. Tsishkou, B. Stanciulescu, and F. Moutarde, “Home: Heatmap output for future motion estimation,” in IEEE Int. Intell. Transp. Syst. Conf., 2021, pp. 500–507.
  44. J. Ngiam, V. Vasudevan, B. Caine, Z. Zhang, H.-T. L. Chiang, J. Ling, R. Roelofs, A. Bewley, C. Liu, A. Venugopal et al., “Scene Transformer: A unified architecture for predicting future trajectories of multiple agents,” in Int. Conf. Learn. Repres., 2021.
  45. M. Liu, H. Cheng, L. Chen, H. Broszio, J. Li, R. Zhao, M. Sester, and M. Y. Yang, “Laformer: Trajectory prediction for autonomous driving with lane-aware scene constraints,” arXiv preprint arXiv:2302.13933, 2023.
  46. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” in IEEE Int. Conf. Comput. Vis, 2017, pp. 618–626.
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Authors (3)
  1. Pei Xu (18 papers)
  2. Jean-Bernard Hayet (10 papers)
  3. Ioannis Karamouzas (13 papers)
Citations (9)
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Summary

Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction: A Comprehensive Overview

The research paper titled "Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction" explores the field of autonomous vehicle technology. It presents a novel methodology aimed at enhancing the prediction accuracy and real-time performance for vehicle trajectory forecasting. This paper introduces an innovative framework named ContextVAE, which utilizes the architecture of timewise variational autoencoders (VAE) augmented with dual-attention mechanisms to integrate environmental and social contextual data effectively.

The key proposition of the paper is centered around the ContextVAE's ability to provide high-fidelity, multimodal trajectory predictions, which are critical to safely navigating the complexities of real-world traffic environments. This is particularly applicable in scenarios populated with heterogeneous agents such as vehicles, pedestrians, and cyclists. The fundamental challenge addressed is the integration and processing of complex contextual cues—both social interactions and static environmental constraints—within a unified prediction model.

Methodological Advancement

The backbone of ContextVAE is a timewise VAE framework, which stands out from conventional methods by sampling latent variables in a sequence. This design efficiently captures the intricate and dynamic nature of vehicular interactions over time, handling uncertainty in agent decision-making processes. The integration of dual-attention mechanisms enhances the predictive model's focus, actively weighting both map contexts and agent interactions to inform predictions.

A notable departure from existing VAE methods is the implementation of a unified scheme for observation encoding. This efficiently synthesizes map-derived environmental features with neighboring agent dynamics. This contrasts with traditional decoupled encoding strategies where environmental and social data are processed independently.

Experimental Results

The efficacy of ContextVAE is empirically validated across diverse datasets: nuScenes, Lyft Level 5, and Waymo Open Motion, showcasing its generalizability and robustness. Remarkably, the ContextVAE consistently achieved state-of-the-art performance on these benchmarks in terms of both deterministic and multimodal metrics (minADE and minFDE). For instance, on the nuScenes dataset, ContextVAE attained a minADE of 1.59 when extending predictions to a challenging 6-second horizon with five output predictions (k=5).

Further, the paper underscores ContextVAE's computational efficiency; inference times are maintained under 30 milliseconds, rendering it suitable for real-time applications—a cardinal requirement for operational autonomous systems. For a deployment context, this performance is achieved with a compact neural architecture that avoids high memory footprints and complex pre- or post-processing steps typically associated with competitive methodologies.

Implications and Future Directions

From a practical standpoint, the introduction of ContextVAE offers a robust solution to the unpredictable and multimodal nature of vehicle trajectories within autonomous systems. The real-time capabilities position it as a valuable tool in traffic navigation systems and intelligent transport frameworks where rapid response times to predictive cues markedly enhance safety and reliability.

Theoretical implications include reinforcing the importance of context-integrated models within trajectory prediction fields. This approach provides a compelling argument for the adoption of fully integrated processing schemes as standard practice, potentially influencing future VAE-based innovations and trajectory prediction algorithms.

Speculative future developments could explore incorporating evolving environmental dynamics, such as variable traffic signals or changing road conditions, directly into the encoding frameworks. Additionally, adapting this model to multi-agent trajectory prediction might yield insights into collective agent behaviors in shared environments, thereby broadening its potential applications.

In conclusion, ContextVAE exemplifies forward-looking research that merges real-time prediction capabilities with sophisticated context-awareness, setting a solid foundation for future exploration and advancements in intelligent vehicular systems.

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