Abstract
To travel safely and efficiently in complex traffic scenarios, autonomous vehicles need to be able to accurately predict the intentions and future behaviors of surrounding traffic participants. Nowadays, the behavior prediction of autonomous driving is mostly achieved using machine learning and deep learning models. However, due to the complexity and opacity of these models, it is difficult to meet the transparency requirements of autonomous driving systems in terms of interpretability. Therefore, by changing the input data, that is, using the mask to add or delete the traffic participants in the driving scene and modify the operation of the lane structure, so as to explore the main factors affecting the behavior prediction and further improve the interpretability of the model. To verify the interpretability of counterfactual inference operations for behavioral prediction methods, this paper conducts experiments on a large-scale autonomous driving public dataset nuScenes. Through a detailed analysis of the experimental results, we can conclude that the proposed method is interpretable for the behavioral prediction results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Affenzeller, J.: ERTRAC: the European road transport research advisory council. In: Watzenig, D., Horn, M. (eds.) Automated Driving, pp. 607–610. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-31895-0_32
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should I trust you?” explaining the predictions of any classifier. In: SIGKDD, pp. 1135–1144 (2016)
Caesar, H., et al.: nuscenes: A multimodal dataset for autonomous driving. In: CVPR, pp. 11621–11631 (2020)
Deo, N., Wolff, E., Beijbom, O.: Multimodal trajectory prediction conditioned on lane-graph traversals. In: Conference on Robot Learning, pp. 203–212. PMLR (2022)
Deo, N., Trivedi, M.M.: Convolutional social pooling for vehicle trajectory prediction. In: CVPR Workshop (2018)
Li, X., Ying, X., Chuah, M.C.: Grip: graph-based interaction-aware trajectory prediction. In: ITSC, pp. 3960–3966. IEEE (2019)
Pereira, L.M., Lopes, A.B., Pereira, L.M., Lopes, A.B.: Cognitive prerequisites: the special case of counterfactual reasoning. Machine Ethics: From Machine Morals to the Machinery of Morality, pp. 97–102 (2020)
Ginsberg, M.L.: Counterfactuals. Artif. Intell. 30(1), 35–79 (1986)
Grahne, G.: Updates and counterfactuals. J. Log. Comput. 8(1), 87–117 (1998)
Aumann, R.J.: Backward induction and common knowledge of rationality. Games Econ. Behav. 8(1), 6–19 (1995)
Spohn, W.: A ranking-theoretic approach to conditionals. Cogn. Sci. 37(6), 1074–1106 (2013)
Hendrickson, N.: Army war Coll Carlisle barracks pa center for strategic leadership. Counterfactual reasoning: a basic guide for analysts, strategists, and decision makers (2008)
Chen, Y., Ivanovic, B., Pavone, M.: Scept: scene-consistent, policy-based trajectory predictions for planning. In: CVPR, pp. 17103–17112 (2022)
Hart, P., Knoll, A.: Counterfactual policy evaluation for decision-making in autonomous driving. ar**v preprint ar**v:2003.11919 (2020)
Hart, P.C.: Learning, evaluating and optimizing behavior policies for autonomous vehicles. Ph.D. thesis, Technische Universität München (2021)
Ivanovic, B., et al.: Heterogeneous-agent trajectory forecasting incorporating class uncertainty. In: IROS, pp. 12196–12203. IEEE (2022)
Chen, G., Li, J., Lu, J., Zhou, J.: Human trajectory prediction via counterfactual analysis. In: CVPR, pp. 9824–9833 (2021)
Phan-Minh, T., Grigore, E.C., Boulton, F.A., Beijbom, O., Wolff, E.M.: Covernet: multimodal behavior prediction using trajectory sets. In: CVPR, pp. 14074–14083 (2020)
Salzmann, T., Ivanovic, B., Chakravarty, P., Pavone, M.: Trajectron++: dynamically-feasible trajectory forecasting with heterogeneous data. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12363, pp. 683–700. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58523-5_40
Wang, C., Wang, Y., Xu, M., Crandall, D.J.: Stepwise goal-driven networks for trajectory prediction. IEEE Robot. Autom. Lett. 7(2), 2716–2723 (2022)
Messaoud, K., Deo, N., Trivedi, M.M., Nashashibi, F.: Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation. In: IV, pp. 165–170. IEEE (2021)
Luo, C., Sun, L., Dabiri, D., Yuille, A.: Probabilistic multi-modal trajectory prediction with lane attention for autonomous vehicles. In: IROS, pp. 2370–2376. IEEE (2020)
Deo, N., Trivedi, M.M.: Trajectory forecasts in unknown environments conditioned on grid-based plans. ar**v preprint ar**v:2001.00735 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Bei**g HIWING Scientific and Technological Information Institute
About this paper
Cite this paper
Huang, Y., Wang, F., Zhu, T. (2024). Prediction of Traffic Participant Behavior in Driving Environment Based on Counterfactual Inference. In: Qu, Y., Gu, M., Niu, Y., Fu, W. (eds) Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023). ICAUS 2023. Lecture Notes in Electrical Engineering, vol 1173. Springer, Singapore. https://doi.org/10.1007/978-981-97-1087-4_47
Download citation
DOI: https://doi.org/10.1007/978-981-97-1087-4_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-1086-7
Online ISBN: 978-981-97-1087-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)