Interpretable Goal-based Prediction and Planning for Autonomous Driving
Open accessAbstract
We propose an integrated prediction and planning system for autonomous driving which uses rational inverse planning to recognise the goals of other vehicles. Goal recognition informs a Monte Carlo Tree Search (MCTS) algorithm to plan optimal maneuvers for the ego vehicle. Inverse planning and MCTS utilise a shared set of defined maneuvers and macro actions to construct plans which are explainable by means of rationality principles. Evaluation in simulations of urban driving scenarios demonstrate the system’s ability to robustly recognise the goals of other vehicles, enabling our vehicle to exploit non-trivial opportunities to significantly reduce driving times. In each scenario, we extract intuitive explanations for the predictions which justify the system’s decisions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The fore and aft autonomous driving objective tests using the Udacity autonomous vehicle database demonstrate the efficacy of the suggested approach for troubleshooting Neural's predictions.
“The fore and aft autonomous driving objective tests using Udacity autonomous vehicle database demonstrate the efficacy of the suggested approach for troubleshooting Neural's predictions [99].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [99]
The Introduction (p. 1) says the system’s interpretable predictions can aid analysis and debugging, and the Conclusion (p. 6) reports successful evaluation in urban driving simulations. The Scenarios section (p. 5) describes generated simulation instances, not tests using the Udacity database; the paper does not report troubleshooting a neural model’s predictions. (LLM, conf=0.86)
Experimental — accuracy not yet measuredExpense mapping visualization provides a top-down description of the traffic situation.
“Furthermore, expense mapping visualization provides a top-down description of the traffic situation [48], [99].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [99]
The Algorithms & Parameters section (p. 5) describes IGP2-MAP as a planning variant and labels Figure 3 as town layouts. Searches for expense or cost-map visualization and a top-down traffic description found no passage in our copy explaining such a visualization. (LLM, conf=0.70)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
Hart, P., Nilsson, N., & Raphael, B. (1968) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1968.300136 · Cited by 12,738 · see citing works
Wächter, A., & Biegler, L. T. (2006) · Mathematical Programming · doi:10.1007/s10107-004-0559-y · Cited by 9,483 · see citing works
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083 · see citing works
Finite-time Analysis of the Multiarmed Bandit Problem
Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002) · Machine Learning · doi:10.1023/a:1013689704352 · Cited by 6,033 · see citing works
Congested traffic states in empirical observations and microscopic simulations
Treiber, M., Hennecke, A., & Helbing, D. (2000) · Physical Review E · doi:10.1103/physreve.62.1805 · Cited by 4,762 · see citing works
Bandit Based Monte-Carlo Planning
Kocsis, L., & Szepesvari, C. (2006) · European Conference on Machine Learning · doi:10.1007/11871842_29 · Cited by 4,073 · see citing works
A Survey of Monte Carlo Tree Search Methods
Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., & Colton, S. (2012) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2012.2186810 · Cited by 2,970 · see citing works
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
Lee, N., Choi, W., Vernaza, P., Choy, C., Torr, P. H. S., & Chandraker, M. K. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.233 · Cited by 1,091 · see citing works
Planning and Decision-Making for Autonomous Vehicles
Schwarting, W., Alonso-Mora, J., & Rus, D. (2018) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105157 · Cited by 972 · see citing works
Action understanding as inverse planning
Baker, C. L., Saxe, R., & Tenenbaum, J. B. (2009) · Cognition · doi:10.1016/j.cognition.2009.07.005 · Cited by 920 · see citing works
Planning-based prediction for pedestrians
Ziebart, B. D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Bagnell, J. A., Hebert, M., Dey, A. K., & Srinivasa, S. (2009) · 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2009.5354147 · Cited by 456 · see citing works
Autonomous agents modelling other agents: A comprehensive survey and open problems
Albrecht, S. V., & Stone, P. (2018) · Artificial Intelligence · doi:10.1016/j.artint.2018.01.002 · Cited by 417 · see citing works
Self-citationPRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings
Rhinehart, N., McAllister, R., Kitani, K., & Levine, S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00291 · Cited by 351 · see citing works
Hubmann, C., Schulz, J., Becker, M., et al. (2018) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2788208 · Cited by 310 · see citing works
Probabilistic Plan Recognition Using Off-the-Shelf Classical Planners
Ramírez, M., & Geffner, H. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7745 · Cited by 280 · see citing works
IntentNet: Learning to Predict Intention from Raw Sensor DataPreprint
Casas, S., Luo, W., & Urtasun, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.07907 · Cited by 248 · see citing works
DESPOT: Online POMDP Planning with Regularization
Somani, A., Ye, N., Hsu, D., et al. (2013) · Cited by 240 · see citing works
Hubmann, C., Becker, M., Althoff, D., Lenz, D., & Stiller, C. (2017) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2017.7995949 · Cited by 230 · see citing works
Intention-Aware Motion Planning
Bandyopadhyay, T., Won, K. S., Frazzoli, E., Hsu, D., Lee, W. S., & Rus, D. L. (2013) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-36279-8_29 · Cited by 195 · see citing works
Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
Sadat, A., Casas, S., Ren, M., Wu, X., Dhawan, P., & Urtasun, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58592-1_25 · Cited by 183 · see citing works
Galceran, E., Cunningham, A. G., Eustice, R. M., & Olson, E. (2017) · Autonomous Robots · doi:10.1007/s10514-017-9619-z · Cited by 168 · see citing works
Contingency Planning Over Probabilistic Obstacle Predictions for Autonomous Road Vehicles
Hardy, J., & Campbell, M. (2013) · IEEE Transactions on Robotics · doi:10.1109/tro.2013.2254033 · Cited by 132 · see citing works
Watch this: Scalable cost-function learning for path planning in urban environments
Wulfmeier, M., Wang, D. Z., & Posner, I. (2016) · 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759328 · Cited by 116 · see citing works
Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection
Song, W., Xiong, G., & Chen, H. (2016) · Mathematical Problems in Engineering · doi:10.1155/2016/1025349 · Cited by 115 · see citing works
Belief and truth in hypothesised behaviours
Albrecht, S. V., Crandall, J. W., & Ramamoorthy, S. (2016) · Artificial Intelligence · doi:10.1016/j.artint.2016.02.004 · Cited by 61 · see citing works
Self-citationDynamic Speed Adaptation for Path Tracking Based on Curvature Information and Speed Limits
Serna, C. G., & Ruichek, Y. (2017) · Sensors · doi:10.3390/s17061383 · Cited by 61 · see citing works
Online Bayesian changepoint detection for articulated motion models
Niekum, S., Osentoski, S., Atkeson, C. G., & Barto, A. G. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139383 · Cited by 43 · see citing works
Zhou, B., Schwarting, W., Rus, D. L., et al. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461138 · Cited by 41 · see citing works
Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors
Hu, Y., Sun, L., & Tomizuka, M. (2019) · IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2019.8917105 · Cited by 28 · see citing works
Smooth Behavioral Estimation For Ramp Merging Control In Autonomous Driving
Dong, C., Dolan, J. M., & Litkouhi, B. B. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500576 · Cited by 16 · see citing works
Energy-Based Continuous Inverse Optimal Control
Xu, Y., Xie, J., Zhao, T., Baker, C. L., Zhao, Y., & Wu, Y. N. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3168795 · Cited by 15 · see citing works
Xu, Y., Zhao, T., Baker, C. L., Zhao, Y., & Wu, Y. N. (2019) · arXiv (Cornell University) · Cited by 9 · see citing works
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks
Albrecht, S. V., & Ramamoorthy, S. (2016) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5044 · Cited by 7 · see citing works
Self-citationKoopman, P., Hierons, R., Khastgir, S., et al. (2019) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 4 · see citing works
Energy-Based Continuous Inverse Optimal ControlPreprint
Xu, Y., Xie, J., Zhao, T., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.05453 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 7
- [1][1] W. Schwarting, J. Alonso-Mora, and D. Rus, 'Planning and decisionmaking for autonomous vehicles,' Annual Review of Control, Robotics, and Autonomous Systems , vol. 1, pp. 187-210, 2018. -
- [11][11] H. Zhao, J. Gao, T. Lan, C. Sun, B. Sapp, B. Varadarajan, Y. Shen, Y. Shen, Y. Chai, C. Schmid, C. Li, and D. Anguelov, 'TNT: Targetdriven trajectory prediction,' in Conference on Robot Learning , 2020. -
- [13][13] Y. Chai, B. Sappm, M. Bansal, and D. Anguelov, 'MultiPath: multiple probabilistic anchor trajectory hypotheses for behavior prediction,' in Conference on Robot Learning , 2019. -
- [22][22] M. Gadd, D. De Martini, L. Marchegiani, P. Newman, and L. Kunze, 'Sense-Assess-eXplain (SAX): Building trust in autonomous vehicles in challenging real-world driving scenarios,' in 2020 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2020, pp. 150-155. -
- [23][23] M. Ram´ ırez and H. Geffner, 'Probabilistic plan recognition using offthe-shelf classical planners,' in 24th AAAI Conference on Artificial Intelligence , 2010, pp. 1121-1126. -
- [33][33] C. Watkins and P. Dayan, 'Q-learning,' Machine Learning , vol. 8, no. 3-4, pp. 279-292, 1992. -
- [35][35] M. Kwon, E. Biyik, A. Talati, K. Bhasin, D. P. Losey, and D. Sadigh, 'When humans aren't optimal: Robots that collaborate with risk-aware humans,' in Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction , 2020. -
Cited by
Sorted by citations
A Review of Trustworthy and Explainable Artificial Intelligence (XAI)
Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023) · IEEE Access · doi:10.1109/access.2023.3294569 · Cited by 352 · see citing works
- Authors
- Albrecht, S. V., Brewitt, C., Wilhelm, J., Gyevnar, B., Eiras, F., Dobre, M., & Ramamoorthy, S.
- Affiliations
- FiveAI Ltd.,UK
- Venue
- 2021 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2021-05-30
- Pages
- 1043-1049
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W3096988257
- Cited by
- 12
- References
- 36
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 4 · 2022: 1 · 2021: 2 · 2020: 2
- FWCI
- 1.98
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Royal Society
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Keywords
- autonomous driving, goal recognition, Monte Carlo tree search, maneuver planning, explainable AI, interpretable prediction, macro-actions, urban driving conditions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
