Pith. sign in

REVIEW 5 major objections 5 minor 124 references

Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions

T0 review · 5 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that expected-cost planning under a mixture of normal and collision-biased diffusion predictions makes autonomous driving robust to rare adversarial behaviors without the over-conservatism of risk-sensitive planners.

desk verdict A clean mixture-of-predictions idea for robust driving planning, but the experiments confound the adversarial input distribution with the risk measure, so the headline gain over CVaR is not yet pinned to the claimed mechanism. read the letter →

arxiv 2505.12327 v1 pith:ZED7WCFX submitted 2025-05-18 cs.RO cs.AIcs.LG

Albert Zhao , Stefano Soatto This is my paper · ORCID
classification cs.ROcs.AIcs.LG
keywords autonomousdrivingrobustplanningdiffusionmodelsmotionpredictionadversarialrisk-sensitiveclosed-loopevaluationjaywalkingscenarios
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that an autonomous-driving planner can be made robust to rare adversarial behaviors such as jaywalking pedestrians and red-light runners without becoming overly cautious in normal traffic. Its method trains a diffusion model to predict normal agent motion, then at test time biases the same model to generate predictions that are likely to collide with the candidate ego plan. Candidate plans are scored by expected cost under a mixture of the normal and biased prediction distributions. The authors report that this mixture planner outperforms risk-sensitive and safety-constrained baselines in simulated adversarial scenarios, cutting the error rate by 18.8 percent relative to the best baseline. The value of the claim is that a simple test-time mechanism, with no offline adversarial scenario collection and no hard safety constraints, can deliver robustness to out-of-distribution agent behaviors.

What carries the argument

The load-bearing object is the mixture distribution $p_{\mathrm{mix}}(y^a_t \mid s_t, y^{\mathrm{ego}}_t) = (1-w_b)p_\theta(y^a_t \mid s_t) + w_b p_{\theta,b}(y^a_t \mid s_t, y^{\mathrm{ego}}_t)$, where $p_\theta$ is the trained diffusion predictor for normal behavior and $p_{\theta,b}$ is the test-time biased distribution. The bias is implemented by adding a score term $\lambda \nabla_{y^a_t} \mathcal{L}(D_\theta(y^a_t;s_t,\sigma), y^{\mathrm{ego}}_t)$ to the diffusion sampling ODE, with $\mathcal{L}$ the average $\ell^1$ distance between the candidate plan and the closest predicted agent trajectory. This makes the adversarial samples plan-specific and collision-seeking, while the diffusion model's learned distribution keeps them realistic; expected cost under the mixture then places nonzero weight on both behavior types. An ablation shows the mixture weight matters: $w_b=0.8$ yields the best score, while $w_b=1.0$ (adversarial predictions only) performs poorly.

What would settle it

Run MAD and a normal-prediction expected-cost planner on logged real-world jaywalking and red-light-violation events; if the normal-only planner matches or exceeds MAD's closed-loop score on those logs, the reported benefit is an artifact of the synthetic biased samples rather than genuine robustness.

Watch

Extended reading notes

Core claim

The paper's central claim is that a planner can be made robust to rare adversarial agent behaviors at test time without training on offline adversarial scenarios or imposing hard safety constraints. The proposed method, MAD (mixed adversarial diffusion predictions), trains a diffusion model to produce an unbiased distribution of normal agent motions, then biases the same model during inference toward trajectories that are likely to collide with the candidate plan. Plans are scored by Monte-Carlo expected cost under a mixture of the normal and biased distributions, with mixture weight $w_b$ controlling how much probability mass is placed on adversarial behavior. The authors report a closed-loop score of 86.6 across single-agent jaywalking, multi-agent jaywalking, and red-light violation benchmarks, an 18.8 percent error-rate reduction over the second-best baseline (CVaR at 83.5), and show through ablations that mixtures outperform both normal-only and adversarial-only planning.

Load-bearing premise

The approach depends on the test-time biased diffusion samples being realistic adversarial behaviors that match the real threats; if those samples are physically implausible or aimed at the wrong failure mode, the planner's extra caution is spent on synthetic hazards rather than real ones.

Editorial extensions

If this is right

  • Because adversarial predictions are generated at test time rather than from offline scenario collections, the planner can in principle respond to adversarial behaviors never seen during training.
  • The mixture weight $w_b$ acts as a tuning knob for conservatism: $w_b=0.8$ outperformed both the normal-only baseline ($w_b=0$) and the adversarial-only baseline ($w_b=1.0$), so operators can trade robustness against normal driving performance.
  • The mechanism is planner-agnostic in that it only changes how agent predictions are sampled and how candidate plans are scored; the underlying candidate-plan generation and cost function remain those of the base planner.
  • The same recipe transfers across different adversarial failure modes, since the results cover single-agent jaywalking, multi-agent jaywalking, and red-light violation without scenario-specific hyperparameters.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If real logged adversarial trajectories were available, a natural next test would be whether the biased diffusion samples match those logs in acceleration, timing, and route choice; the paper's experiments use synthetic adversarial scenarios, so the realism of the biased distribution remains an open question.
  • The bias objective targets the single closest agent via average L1 distance, which is a proxy for collision; a planner-aware objective that optimizes actual collision margin or accounts for multiple simultaneous threats could behave differently in dense scenes, though it would likely require more computation.
  • Because the mixture distribution is defined per candidate plan, the method's cost scales with the number of plans times the number of diffusion samples; sharing adversarial samples across similar plans or distilling the biased distribution could make the approach cheaper in practice.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper proposes a robust planning method for autonomous driving, called mixed adversarial diffusion predictions (MAD). The authors train a diffusion motion predictor on normal driving data, then at test time bias the predictor to generate adversarial agent trajectories that are close to each candidate ego plan, and finally score candidate plans by expected cost under a mixture of normal and adversarial predictions. The method is evaluated in closed-loop NuPlan simulation on three constructed benchmarks: single-agent jaywalking, multi-agent jaywalking, and red light violation. The paper reports that MAD achieves the best overall closed-loop score (86.6 CLS), corresponding to an 18.8% error-rate reduction over the second-best baseline CVaR (83.5 CLS), and argues that this demonstrates robustness to adversarial behaviors without the over-conservatism of risk-sensitive planners.

Significance. If the claims are substantiated, the contribution is conceptually appealing: a test-time-only modification of a diffusion-based motion predictor, combined with a mixture expected-cost objective, that avoids offline adversarial scenario collection and hard safety constraints. The paper is clearly written, the method is well specified, and the choice of PDM-Closed as a base planner allows the effect of the prediction distribution to be isolated from planner architecture. However, the present experimental evidence does not fully support the central mechanism claim. The baselines consume a different input distribution than MAD, the mixture weight wb is selected on one of the evaluation benchmarks, and the 'not overly conservative' claim rests on near-saturated sub-benchmarks. With additional controlled ablations and statistical reporting, the contribution would be solid and of interest to the robotics and autonomous-driving communities.

major comments (5)
  1. [Section IV-C and Table I] The baseline comparison conflates the adversarial input distribution with the mixture-expected-cost rule. Section IV-C states that all baselines except constant velocity use only unbiased diffusion predictions from the normal behavior distribution, whereas MAD evaluates each candidate plan over two normal and eight adversarial biased samples (Section III-F, wb=0.8). The reported 18.8% error-rate reduction over CVaR therefore does not isolate the paper's central mechanism, which is the expected-cost aggregation under a mixture; the gain could be driven entirely by the presence of plan-specific adversarial samples. Please add a CVaR (or worst-case) baseline evaluated on the same biased adversarial samples, and an expected-cost baseline over the same eight-adversarial/two-normal sample mixture, to disentangle these effects.
  2. [Section IV-H and Table III] The mixture weight wb is selected by an ablation on the single-agent jaywalking benchmark (Table III), and the same benchmark is part of the headline overall comparison in Table I. This selection-on-evaluation creates a risk of overfitting the hyperparameter to that scenario, so the reported overall improvement may not generalize. Please report wb chosen on a separate validation set, or provide results for the full range of wb across all three benchmarks, so that the influence of this free parameter is transparent.
  3. [Section IV-B and Table II] The claim that MAD avoids over-conservatism is only weakly supported because the slow-jaywalker sub-benchmark is saturated. In the single-agent case, EC, CVaR, and Col-P 0.1 already achieve 100 CLS on the slow sub-benchmark, and EC achieves 100 CLS in the multi-agent slow case as well. Meanwhile, on the fast-jaywalker sub-benchmark, MAD (74.2 single-agent, 76.8 multi-agent) is not the best method, losing to WC (91.9 single-agent) and Col-P 0 (78.2 multi-agent). The evidence indicates a robustness/conservatism trade-off rather than a strict improvement in 'not overly conservative' behavior; additional non-saturated normal-behavior scenarios are needed to support the central claim.
  4. [Section III-C, Eq. (7)] The adversarial distribution is generated by an L1-distance loss between the closest agent's predicted trajectory and the candidate ego plan, with no validation that the biased samples are physically plausible or representative of real jaywalking and red-light violations. The diffusion prior is the only realism constraint, and the paper provides no qualitative examples, kinematic-feasibility check, or comparison against recorded adversarial trajectories. This is load-bearing because if the biased samples are unrealistic, the planner's extra caution is spent on synthetic threats; please add such validation or a sensitivity analysis demonstrating that the biased predictions are plausible.
  5. [Section IV-A and Tables I-II] The experimental section reports no error bars, number of seeds, or number of scenarios per benchmark. With constructed benchmarks and one-decimal CLS scores, differences such as the 10.4% single-agent error-rate reduction need to be assessed for statistical significance. Please report means and variances over multiple seeds, or at minimum the number of scenarios per benchmark, so that the robustness of the claimed improvements can be evaluated.
minor comments (5)
  1. [Section III-D] There is a typo in the sentence 'By computing expected cost using both normal and adverarial agent behaviors'; 'adverarial' should be 'adversarial'.
  2. [Section III-C] The term 'unbiased distribution' is used to describe the trained diffusion model; since the training data is NuPlan driving logs, the distribution is 'normal' relative to that dataset, but calling it 'unbiased' may overstate its neutrality. Consider using 'normal behavior distribution' throughout for clarity.
  3. [Section III-F] The paper states that the planner considers 10 sampled predictions per candidate plan (two normal, eight adversarial, wb=0.8), but it does not report the computational cost of generating eight biased samples per plan per step; a brief runtime comparison against the baselines would help assess practical deployability.
  4. [Section IV-H] In the ablation caption, the sentence 'all methods that use a mixture of normal and adversarial agent behaviors outperform the methods that use only normal agent predictions (EC) or only adversarial predictions' is not fully supported by the table, since wb=1.0 (only adversarial) is worse than EC; the text should clarify that the claim applies to mixture weights strictly between 0 and 1.
  5. [References] Reference [13] contains a typo in the title: 'validatio' should be 'validation'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the adversarial biasing loss and the planning cost are distinct functions, and the benchmark adversarial agents are scripted independently of the method's generated predictions.

full rationale

The paper's derivation chain is not circular in the sense required by the analysis. The normal behavior distribution comes from a diffusion model trained on NuPlan data, and the adversarial distribution is generated at test time by adding a biasing score based on L, the average L1 distance between the candidate plan and the closest predicted agent trajectory. The planner then minimizes expected PDM-Closed cost c under a Monte Carlo mixture of normal and adversarial samples. These are not equivalent by construction: L is a geometric collision proxy, while c includes collisions, time to collision, progress, comfort, speed-limit, and drivable-area terms. Minimizing expected c under the mixture is therefore not the same object as minimizing L, and the method does not rename a fitted quantity as a prediction. The benchmark adversarial behaviors (jaywalking and red-light violations) are scripted in the NuPlan simulator independently of the candidate plans and of the diffusion model's generated samples, so the reported closed-loop scores are not forced by the construction of the adversarial predictions. The only notable weakness is experimental: CVaR and other baselines use only unbiased normal predictions, while MAD evaluates with eight adversarial and two normal samples per candidate plan, so the headline 18.8% error-rate reduction may partly reflect the difference in input distributions rather than the mixture-expected-cost rule. That is a validity or attribution concern, not circularity. Citations to MotionDiffuser and PDM-Closed are external and provide the base model and planner; there is no load-bearing self-citation chain or imported uniqueness theorem. Accordingly, the appropriate finding is no significant circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The method's added value is the test-time mixture construction; what it pulls from the literature are the diffusion training pipeline, the guided sampling trick, and the rule-based planner. The only hand-set scalars are wb and lambda, plus the sample allocation.

free parameters (3)
  • wb (adversarial mixture weight) = 0.8
    Chosen from ablation on the single-agent jaywalking benchmark (Table III), the same benchmark used in the headline results; not selected on a held-out set.
  • lambda (biasing loss weight) = 0.5
    Set by hand in Section III-F; ablation (Table III) shows CLS insensitive for lambda > 0, so it is not highly load-bearing but still a chosen constant.
  • sample count and normal/adversarial ratio = 10 total; 2 normal, 8 adversarial
    Chosen in Section III-F; the 20/80 split implements wb=0.8 via Monte Carlo, which affects the variance of the cost estimator.
assumptions (5)
  • standard math Score function of the diffusion model equals (D_theta - y) / sigma^2 (Eq. 4).
    Standard result for denoising diffusion models; used in Section III-B and III-C to derive the guided sampling update.
  • standard math Reverse diffusion ODE with sigma(k)=k correctly samples from p_theta (Eq. 2).
    Follows the EDM framework [84]; assumed when solving the ODE with Heun's method.
  • domain assumption Adversarial behaviors are rare and out-of-distribution, so the trained predictor underestimates them.
    Motivates the whole approach in Section I; if the diffusion prior already captured adversarial behaviors, biasing would be unnecessary.
  • domain assumption The biasing loss L (minimum distance between the closest agent trajectory and the candidate plan, Eq. 7) is a sufficient operationalization of adversarial behavior for planning safety.
    Load-bearing premise in Section III-C: guiding predictions toward collision distance is assumed to produce realistic and relevant adversarial predictions. Not verified against real adversarial trajectories.
  • domain assumption Expected cost under the mixture distribution is a valid planning objective with a plan-dependent adversarial distribution; the Monte Carlo estimate with 10 samples is sufficient.
    Section III-D; the dependence of p_theta,b on the candidate plan makes the objective non-standard, and no variance analysis is provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions." pith.science (2026). https://pith.science/paper/ZED7WCFX

@misc{pith2026250512327,
  author       = {Pith},
  title        = {Pith review of: Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZED7WCFX}},
  note         = {Machine review of arXiv:2505.12327}
}
read the original abstract

We describe a robust planning method for autonomous driving that mixes normal and adversarial agent predictions output by a diffusion model trained for motion prediction. We first train a diffusion model to learn an unbiased distribution of normal agent behaviors. We then generate a distribution of adversarial predictions by biasing the diffusion model at test time to generate predictions that are likely to collide with a candidate plan. We score plans using expected cost with respect to a mixture distribution of normal and adversarial predictions, leading to a planner that is robust against adversarial behaviors but not overly conservative when agents behave normally. Unlike current approaches, we do not use risk measures that over-weight adversarial behaviors while placing little to no weight on low-cost normal behaviors or use hard safety constraints that may not be appropriate for all driving scenarios. We show the effectiveness of our method on single-agent and multi-agent jaywalking scenarios as well as a red light violation scenario.

Figures

Figures reproduced from arXiv: 2505.12327 by the authors.

Figure 1
Figure 1. Overview of Our Method (best viewed in color at 3x zoom). We [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

124 extracted references · 46 canonical work pages

  1. [1]

    Vectornet: Encoding hd maps and agent dynamics from vectorized representation,

    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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 522–11 530

    2020

  2. [2]

    Tra- jectron++: Dynamically-feasible trajectory forecasting with hetero- geneous data,

    T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone, “Tra- jectron++: Dynamically-feasible trajectory forecasting with hetero- geneous data,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVIII 16. Springer, 2020, pp. 683–700

    2020

  3. [3]

    Motion transformer with global intention localization and local movement refinement,

    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 , vol. 35, pp. 6531–6543, 2022

    2022

  4. [4]

    Motiondiffuser: Controllable multi-agent motion prediction using diffusion,

    C. Jiang, A. Cornman, C. Park, B. Sapp, Y . Zhou, D. Anguelov, et al. , “Motiondiffuser: Controllable multi-agent motion prediction using diffusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9644–9653

    2023

  5. [5]

    Scene transformer: A unified architecture for predicting multiple agent trajectories,

    J. Ngiam, B. Caine, V . Vasudevan, 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 multiple agent trajectories,” arXiv preprint arXiv:2106.08417 , 2021

    arXiv 2021

  6. [6]

    Home: Heatmap output for future motion estimation,

    T. Gilles, S. Sabatini, D. Tsishkou, B. Stanciulescu, and F. Moutarde, “Home: Heatmap output for future motion estimation,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE, 2021, pp. 500–507

    2021

  7. [7]

    Gorela: Go relative for viewpoint-invariant motion forecasting,

    A. Cui, S. Casas, K. Wong, S. Suo, and R. Urtasun, “Gorela: Go relative for viewpoint-invariant motion forecasting,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 7801–7807

    2023

  8. [8]

    End-to-end interpretable neural motion planner,

    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

    2019

Show all 124 references
  1. [9]

    Perceive, predict, and plan: Safe motion planning through interpretable semantic representations,

    A. Sadat, S. Casas, M. Ren, X. Wu, P. Dhawan, and R. Urta- sun, “Perceive, predict, and plan: Safe motion planning through interpretable semantic representations,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIII ...

    2020

  2. [10]

    Mp3: A unified model to map, perceive, predict and plan,

    S. Casas, A. Sadat, and R. Urtasun, “Mp3: A unified model to map, perceive, predict and plan,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 14 398–14 407

    2021

  3. [11]

    Rap: Risk-aware prediction for robust planning,

    H. Nishimura, J. Mercat, B. Wulfe, R. T. McAllister, and A. Gaidon, “Rap: Risk-aware prediction for robust planning,” in Conference on Robot Learning. PMLR, 2023, pp. 381–392

    2023

  4. [12]

    Learning to collide: An adaptive safety-critical scenarios generating method,

    W. Ding, B. Chen, M. Xu, and D. Zhao, “Learning to collide: An adaptive safety-critical scenarios generating method,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Sys- tems (IROS). IEEE, 2020, pp. 2243–2250

    2020

  5. [13]

    Adaptive stress testing with reward augmentation for autonomous vehicle validatio,

    A. Corso, P. Du, K. Driggs-Campbell, and M. J. Kochenderfer, “Adaptive stress testing with reward augmentation for autonomous vehicle validatio,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019, pp. 163–168

    2019

  6. [14]

    Constrained policy optimization,

    J. Achiam, D. Held, A. Tamar, and P. Abbeel, “Constrained policy optimization,” in International conference on machine learning . PMLR, 2017, pp. 22–31

    2017

  7. [15]

    Reward constrained policy optimization,

    C. Tessler, D. J. Mankowitz, and S. Mannor, “Reward constrained policy optimization,” arXiv preprint arXiv:1805.11074 , 2018

    2018 arXiv

  8. [16]

    A review of safe reinforcement learning: Methods, theory and applications,

    S. Gu, L. Yang, Y . Du, G. Chen, F. Walter, J. Wang, and A. Knoll, “A review of safe reinforcement learning: Methods, theory and applications,” arXiv preprint arXiv:2205.10330 , 2022

    2022 arXiv

  9. [17]

    Recovery rl: Safe reinforcement learning with learned recovery zones,

    B. Thananjeyan, A. Balakrishna, S. Nair, M. Luo, K. Srinivasan, M. Hwang, J. E. Gonzalez, J. Ibarz, C. Finn, and K. Goldberg, “Recovery rl: Safe reinforcement learning with learned recovery zones,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 4915–4922, 2021

    2021

  10. [18]

    Cimrl: Combining imitation and reinforcement learning for safe autonomous driving,

    J. Booher, K. Rohanimanesh, J. Xu, and A. Petiushko, “Cimrl: Combining imitation and reinforcement learning for safe autonomous driving,” arXiv preprint arXiv:2406.08878 , 2024

    2024 arXiv

  11. [19]

    Safe imitation learning via fast bayesian reward inference from preferences,

    D. Brown, R. Coleman, R. Srinivasan, and S. Niekum, “Safe imitation learning via fast bayesian reward inference from preferences,” in International Conference on Machine Learning . PMLR, 2020, pp. 1165–1177

    2020

  12. [20]

    Coin: Chance-constrained imitation learning for uncertainty-aware adaptive resource oversub- scription policy,

    L. Wang, M. Das, F. Yang, C. Duo, B. Qiao, H. Dong, S. Qin, C. Bansal, Q. Lin, S. Rajmohan, et al. , “Coin: Chance-constrained imitation learning for uncertainty-aware adaptive resource oversub- scription policy,” arXiv preprint arXiv:2401.07051 , 2024

    2024 arXiv

  13. [21]

    King: Generating safety-critical driving scenarios for robust imita- tion via kinematics gradients,

    N. Hanselmann, K. Renz, K. Chitta, A. Bhattacharyya, and A. Geiger, “King: Generating safety-critical driving scenarios for robust imita- tion via kinematics gradients,” in European Conference on Computer Vision. Springer, 2022, pp. 335–352

    2022

  14. [22]

    Advsim: Generating safety-critical scenarios for self-driving vehicles,

    J. Wang, A. Pun, J. Tu, S. Manivasagam, A. Sadat, S. Casas, M. Ren, and R. Urtasun, “Advsim: Generating safety-critical scenarios for self-driving vehicles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9904–9913

    2021

  15. [23]

    A bayesian approach to breaking things: efficiently predicting and repairing failure modes via sampling,

    C. Dawson and C. Fan, “A bayesian approach to breaking things: efficiently predicting and repairing failure modes via sampling,” arXiv preprint arXiv:2309.08052 , 2023

    2023 arXiv

  16. [24]

    Rocus: Robot controller understanding via sampling,

    Y . Zhou, S. Booth, N. Figueroa, and J. Shah, “Rocus: Robot controller understanding via sampling,” in Conference on Robot Learning . PMLR, 2022, pp. 850–860

    2022

  17. [25]

    Game-theoretic planning for risk-aware interactive agents,

    M. Wang, N. Mehr, A. Gaidon, and M. Schwager, “Game-theoretic planning for risk-aware interactive agents,” in 2020 IEEE/RSJ Inter- national Conference on Intelligent Robots and Systems (IROS), 2020, pp. 6998–7005

    2020

  18. [26]

    Marc: Multipolicy and risk- aware contingency planning for autonomous driving,

    T. Li, L. Zhang, S. Liu, and S. Shen, “Marc: Multipolicy and risk- aware contingency planning for autonomous driving,” IEEE Robotics and Automation Letters , 2023

    2023

  19. [27]

    Risk-sensitive linear/quadratic/gaussian control,

    P. Whittle, “Risk-sensitive linear/quadratic/gaussian control,” Advances in Applied Probability , vol. 13, no. 4, pp. 764–777, 1981. [Online]. Available: http://www.jstor.org/stable/1426972

    1981

  20. [28]

    G. C. Pflug, Some Remarks on the Value-at-Risk and the Conditional Value-at-Risk. Boston, MA: Springer US, 2000, pp. 272–281

    2000

  21. [29]

    Contingency planning over probabilistic obstacle predictions for autonomous road vehicles,

    J. Hardy and M. Campbell, “Contingency planning over probabilistic obstacle predictions for autonomous road vehicles,” IEEE Transac- tions on Robotics , vol. 29, no. 4, pp. 913–929, 2013

    2013

  22. [30]

    A non- conservatively defensive strategy for urban autonomous driving,

    W. Zhan, C. Liu, C.-Y . Chan, and M. Tomizuka, “A non- conservatively defensive strategy for urban autonomous driving,” in 2016 IEEE 19th International Conference on Intelligent Transporta- tion Systems (ITSC) , 2016, pp. 459–464

    2016

  23. [31]

    Exact and bounded collision probability for motion planning under gaussian uncertainty,

    A. Thomas, F. Mastrogiovanni, and M. Baglietto, “Exact and bounded collision probability for motion planning under gaussian uncertainty,” IEEE Robotics and Automation Letters , vol. 7, no. 1, pp. 167–174, 2022

    2022

  24. [32]

    Unfreezing the robot: Navigation in dense, interacting crowds,

    P. Trautman and A. Krause, “Unfreezing the robot: Navigation in dense, interacting crowds,” in 2010 IEEE/RSJ International Confer- ence on Intelligent Robots and Systems , 2010, pp. 797–803

    2010

  25. [33]

    Controllable safety-critical closed-loop traffic simulation via guided diffusion,

    W.-J. Chang, F. Pittaluga, M. Tomizuka, W. Zhan, and M. Chan- draker, “Controllable safety-critical closed-loop traffic simulation via guided diffusion,” arXiv preprint arXiv:2401.00391 , 2023

    2023 arXiv

  26. [34]

    Versatile scene-consistent traffic scenario generation as optimization with diffusion,

    Z. Huang, Z. Zhang, A. Vaidya, Y . Chen, C. Lv, and J. F. Fisac, “Versatile scene-consistent traffic scenario generation as optimization with diffusion,” arXiv preprint arXiv:2404.02524 , 2024

    2024

  27. [35]

    Diffscene: Diffusion-based safety-critical scenario generation for autonomous vehicles,

    C. Xu, D. Zhao, A. Sangiovanni-Vincentelli, and B. Li, “Diffscene: Diffusion-based safety-critical scenario generation for autonomous vehicles,” in The Second Workshop on New Frontiers in Adversarial Machine Learning , 2023. [Online]. Available: https://openreview.net/forum?id...

    2023

  28. [36]

    Social gan: Socially acceptable trajectories with generative adversarial net- works,

    A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi, “Social gan: Socially acceptable trajectories with generative adversarial net- works,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2255–2264

    2018

  29. [37]

    Motionlm: Multi-agent motion forecasting as language modeling,

    A. Seff, B. Cera, D. Chen, M. Ng, A. Zhou, N. Nayakanti, K. S. Refaat, R. Al-Rfou, and B. Sapp, “Motionlm: Multi-agent motion forecasting as language modeling,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 8545–8556

    2023

  30. [38]

    Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction,

    Y . Chai, B. Sapp, M. Bansal, and D. Anguelov, “Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction,” arXiv preprint arXiv:1910.05449 , 2019

    1910 arXiv

  31. [39]

    Diverse trajectory forecasting with determi- nantal point processes,

    Y . Yuan and K. Kitani, “Diverse trajectory forecasting with determi- nantal point processes,” arXiv preprint arXiv:1907.04967 , 2019

    1907 arXiv

  32. [40]

    Diversitygan: Diversity-aware ve- hicle motion prediction via latent semantic sampling,

    X. Huang, S. G. McGill, J. A. DeCastro, L. Fletcher, J. J. Leonard, B. C. Williams, and G. Rosman, “Diversitygan: Diversity-aware ve- hicle motion prediction via latent semantic sampling,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 5089–5096, 2020

    2020

  33. [41]

    Dlow: Diversifying latent flows for diverse human motion prediction,

    Y . Yuan and K. Kitani, “Dlow: Diversifying latent flows for diverse human motion prediction,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceed- ings, Part IX 16 . Springer, 2020, pp. 346–364

    2020

  34. [42]

    Accurate and diverse sampling of sequences based on a “best of many

    A. Bhattacharyya, B. Schiele, and M. Fritz, “Accurate and diverse sampling of sequences based on a “best of many” sample objective,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 8485–8493

    2018

  35. [43]

    R2p2: A reparame- terized pushforward policy for diverse, precise generative path fore- casting,

    N. Rhinehart, K. M. Kitani, and P. Vernaza, “R2p2: A reparame- terized pushforward policy for diverse, precise generative path fore- casting,” in Proceedings of the European Conference on Computer Vision (ECCV), September 2018

    2018

  36. [44]

    Social ways: Learning multi-modal distributions of pedestrian trajectories with gans,

    J. Amirian, J.-B. Hayet, and J. Pettr ´e, “Social ways: Learning multi-modal distributions of pedestrian trajectories with gans,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019, pp. 0–0

    2019

  37. [45]

    Scept: Scene-consistent, policy-based trajectory predictions for planning,

    Y . Chen, B. Ivanovic, and M. Pavone, “Scept: Scene-consistent, policy-based trajectory predictions for planning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition, 2022, pp. 17 082–17 091

    2022

  38. [46]

    Divide-and-conquer for lane-aware diverse trajectory prediction,

    S. Narayanan, R. Moslemi, F. Pittaluga, B. Liu, and M. Chandraker, “Divide-and-conquer for lane-aware diverse trajectory prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 15 799–15 808

    2021

  39. [47]

    Tnt: Target-driven trajectory prediction,

    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 Conference on Robot Learning . PMLR, 2021, pp. 895–904

    2021

  40. [48]

    Lookout: Diverse multi-future prediction and planning for self-driving,

    A. Cui, S. Casas, A. Sadat, R. Liao, and R. Urtasun, “Lookout: Diverse multi-future prediction and planning for self-driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 16 087–16 096

    2021

  41. [49]

    The importance of prior knowledge in precise multimodal prediction,

    S. Casas, C. Gulino, S. Suo, and R. Urtasun, “The importance of prior knowledge in precise multimodal prediction,” in2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 2295–2302

    2020

  42. [50]

    Control-aware prediction objectives for autonomous driving,

    R. McAllister, B. Wulfe, J. Mercat, L. Ellis, S. Levine, and A. Gaidon, “Control-aware prediction objectives for autonomous driving,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 01–08

    2022

  43. [51]

    Differentiable integrated motion prediction and planning with learnable cost function for autonomous driving,

    Z. Huang, H. Liu, J. Wu, and C. Lv, “Differentiable integrated motion prediction and planning with learnable cost function for autonomous driving,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 11, pp. 15 222–15 236, 2024

    2024

  44. [52]

    Get-dipp: Graph-embedded transformer for differen- tiable integrated prediction and planning,

    J. Sun, C. Yuan, S. Sun, Z. Liu, T. Goh, A. Wong, K. P. Tee, and M. H. Ang, “Get-dipp: Graph-embedded transformer for differen- tiable integrated prediction and planning,” in 2023 3rd International Conference on Computer, Control and Robotics (ICCCR) , 2023, pp. 414–421

    2023

  45. [53]

    Hybrid- prediction integrated planning for autonomous driving,

    H. Liu, Z. Huang, W. Huang, H. Yang, X. Mo, and C. Lv, “Hybrid- prediction integrated planning for autonomous driving,” 2024

    2024

  46. [54]

    Dtpp: Differentiable joint conditional prediction and cost evaluation for tree policy planning in autonomous driving,

    Z. Huang, P. Karkus, B. Ivanovic, Y . Chen, M. Pavone, and C. Lv, “Dtpp: Differentiable joint conditional prediction and cost evaluation for tree policy planning in autonomous driving,” 2024

    2024

  47. [55]

    Diffstack: A differentiable and modular control stack for autonomous vehicles,

    P. Karkus, B. Ivanovic, S. Mannor, and M. Pavone, “Diffstack: A differentiable and modular control stack for autonomous vehicles,” in Conference on robot learning . PMLR, 2023, pp. 2170–2180

    2023

  48. [56]

    Importance sampling for online planning under uncertainty,

    Y . Luo, H. Bai, D. Hsu, and W. S. Lee, “Importance sampling for online planning under uncertainty,” The International Journal of Robotics Research, vol. 38, no. 2-3, pp. 162–181, 2019

    2019

  49. [57]

    Scalable end-to-end autonomous vehicle testing via rare- event simulation,

    M. O' Kelly, A. Sinha, H. Namkoong, R. Tedrake, and J. C. Duchi, “Scalable end-to-end autonomous vehicle testing via rare- event simulation,” in Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa- Bianchi, and R. Garnet...

    2018

  50. [58]

    Neural bridge sampling for evaluating safety-critical autonomous systems,

    A. Sinha, M. O’Kelly, R. Tedrake, and J. C. Duchi, “Neural bridge sampling for evaluating safety-critical autonomous systems,” Ad- vances in Neural Information Processing Systems, vol. 33, pp. 6402– 6416, 2020

    2020

  51. [59]

    Model-based validation as probabilistic inference,

    H. Delecki, A. Corso, and M. Kochenderfer, “Model-based validation as probabilistic inference,” in Learning for Dynamics and Control Conference. PMLR, 2023, pp. 825–837

    2023

  52. [60]

    A survey of algorithms for black-box safety validation of cyber-physical systems,

    A. Corso, R. Moss, M. Koren, R. Lee, and M. Kochenderfer, “A survey of algorithms for black-box safety validation of cyber-physical systems,” Journal of Artificial Intelligence Research, vol. 72, pp. 377– 428, 2021

    2021

  53. [61]

    How should a robot assess risk? to- wards an axiomatic theory of risk in robotics,

    A. Majumdar and M. Pavone, “How should a robot assess risk? to- wards an axiomatic theory of risk in robotics,” in Robotics Research: The 18th International Symposium ISRR. Springer, 2020, pp. 75–84

    2020

  54. [62]

    Dynamically conservative self-driving planner for long-tail cases,

    W. Zhou, Z. Cao, N. Deng, X. Liu, K. Jiang, and D. Yang, “Dynamically conservative self-driving planner for long-tail cases,” IEEE Trans. Intell. Transport. Syst. , vol. 24, no. 3, pp. 3476–3488, 2023

    2023

  55. [63]

    Long-tail prediction uncertainty aware trajectory planning for self- driving vehicles,

    W. Zhou, Z. Cao, Y . Xu, N. Deng, X. Liu, K. Jiang, and D. Yang, “Long-tail prediction uncertainty aware trajectory planning for self- driving vehicles,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2022, pp. 1275– 1282

    2022

  56. [64]

    Game- theoretic planning for autonomous driving among risk-aware human drivers,

    R. Chandra, M. Wang, M. Schwager, and D. Manocha, “Game- theoretic planning for autonomous driving among risk-aware human drivers,” in 2022 International Conference on Robotics and Automa- tion (ICRA). IEEE, 2022, pp. 2876–2883

    2022

  57. [65]

    An efficient risk- aware branch mpc for automated driving that is robust to uncertain vehicle behaviors,

    L. Zhang, G. Pantazis, S. Han, and S. Grammatico, “An efficient risk- aware branch mpc for automated driving that is robust to uncertain vehicle behaviors,” arXiv preprint arXiv:2403.18695 , 2024

    2024 arXiv

  58. [66]

    Safe chance-constrained model predictive control under gaussian mixture model uncertainty,

    K. Ren, C. Chen, H. Sung, H. Ahn, I. Mitchell, and M. Kamgarpour, “Safe chance-constrained model predictive control under gaussian mixture model uncertainty,” arXiv preprint arXiv:2401.03799 , 2024

    2024 arXiv

  59. [67]

    From footprints to beliefprints: Motion planning under uncertainty for maneuvering automated vehicles in dense scenarios,

    H. Banzhaf, M. Dolgov, J. Stellet, and J. M. Z ¨ollner, “From footprints to beliefprints: Motion planning under uncertainty for maneuvering automated vehicles in dense scenarios,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) , 2018, pp. 1680–1687

    2018

  60. [68]

    Planning on a (risk) budget: Safe non-conservative planning in probabilistic dynamic environments,

    H.-J. Huang, K.-C. Huang, M. ˇC´ap, Y . Zhao, Y . N. Wu, and C. L. Baker, “Planning on a (risk) budget: Safe non-conservative planning in probabilistic dynamic environments,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 10 257–10 263

    2021

  61. [69]

    Risk-constrained interactive safety under behavior uncertainty for autonomous driving,

    J. Bernhard and A. Knoll, “Risk-constrained interactive safety under behavior uncertainty for autonomous driving,” in 2021 IEEE Intelli- gent Vehicles Symposium (IV) . IEEE, 2021, pp. 63–70

    2021

  62. [70]

    Efficient uncertainty-aware collision avoidance for autonomous driving using convolutions,

    C. Zhang, X. Wu, J. Wang, and M. Song, “Efficient uncertainty-aware collision avoidance for autonomous driving using convolutions,” IEEE Trans. Intell. Transport. Syst. , vol. 25, no. 10, pp. 13 805– 13 819, 2024

    2024

  63. [71]

    Racp: Risk-aware contingency planning with multi-modal predictions,

    K. A. Mustafa, D. J. Ornia, J. Kober, and J. Alonso-Mora, “Racp: Risk-aware contingency planning with multi-modal predictions,” IEEE Transactions on Intelligent Vehicles , pp. 1–16, 2024

    2024

  64. [72]

    Safe planning for self- driving via adaptive constrained ilqr,

    Y . Pan, Q. Lin, H. Shah, and J. M. Dolan, “Safe planning for self- driving via adaptive constrained ilqr,” in2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 2377–2383

    2020

  65. [73]

    Online update of safety assurances using confidence-based predictions,

    K. Nakamura and S. Bansal, “Online update of safety assurances using confidence-based predictions,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 12 765–12 771

    2023

  66. [74]

    Prediction- based reachability for collision avoidance in autonomous driving,

    A. Li, L. Sun, W. Zhan, M. Tomizuka, and M. Chen, “Prediction- based reachability for collision avoidance in autonomous driving,” in 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021, pp. 7908–7914

    2021

  67. [75]

    Multi-agent reachability calibration with conformal prediction,

    A. Muthali, H. Shen, S. Deglurkar, M. H. Lim, R. Roelofs, A. Faust, and C. Tomlin, “Multi-agent reachability calibration with conformal prediction,” in 2023 62nd IEEE Conference on Decision and Control (CDC). IEEE, 2023, pp. 6596–6603

    2023

  68. [76]

    Robust predictive motion planning by learning obstacle uncertainty,

    J. Zhou, Y . Gao, O. Johansson, B. Olofsson, and E. Frisk, “Robust predictive motion planning by learning obstacle uncertainty,” arXiv preprint arXiv:2403.06222, 2024

    2024 arXiv

  69. [77]

    Safe planning and control under uncertainty for self-driving,

    S. Khaitan, Q. Lin, and J. M. Dolan, “Safe planning and control under uncertainty for self-driving,” IEEE Trans. Veh. Technol. , vol. 70, no. 10, pp. 9826–9837, 2021

    2021

  70. [78]

    Safe planning in dynamic environments using conformal prediction,

    L. Lindemann, M. Cleaveland, G. Shim, and G. J. Pappas, “Safe planning in dynamic environments using conformal prediction,”IEEE Robotics and Automation Letters , 2023

    2023

  71. [79]

    Deep unsupervised learning using nonequilibrium thermodynamics,

    J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International conference on machine learning . PMLR, 2015, pp. 2256–2265

    2015

  72. [80]

    Denoising diffusion probabilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems, vol. 33, pp. 6840–6851, 2020

    2020

  73. [81]

    Denoising diffusion implicit models,

    J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” arXiv preprint arXiv:2010.02502 , 2020

    2010 arXiv

  74. [82]

    Score-based generative modeling through stochastic differential equations,

    Y . Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” arXiv preprint arXiv:2011.13456 , 2020

    2011 arXiv

  75. [83]

    Improved denoising diffusion prob- abilistic models,

    A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion prob- abilistic models,” in International Conference on Machine Learning . PMLR, 2021, pp. 8162–8171

    2021

  76. [84]

    Elucidating the design space of diffusion-based generative models,

    T. Karras, M. Aittala, T. Aila, and S. Laine, “Elucidating the design space of diffusion-based generative models,” Advances in Neural Information Processing Systems , vol. 35, pp. 26 565–26 577, 2022

    2022

  77. [85]

    Diffusion models beat gans on im- age synthesis,

    P. Dhariwal and A. Nichol, “Diffusion models beat gans on im- age synthesis,” Advances in neural information processing systems , vol. 34, pp. 8780–8794, 2021

    2021

  78. [86]

    Glide: Towards photorealistic image generation and editing with text-guided diffusion models,

    A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. Mc- Grew, I. Sutskever, and M. Chen, “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” arXiv preprint arXiv:2112.10741 , 2021

    2021 arXiv

  79. [87]

    Hier- archical text-conditional image generation with clip latents,

    A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hier- archical text-conditional image generation with clip latents,” arXiv preprint arXiv:2204.06125, vol. 1, no. 2, p. 3, 2022

    2022 arXiv

  80. [88]

    Photorealistic text-to-image diffusion models with deep language understanding,

    C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans, et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in Neural Information Processing Systems, vol. 35,...

    2022

  81. [89]

    High-resolution image synthesis with latent diffusion models,

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 10 674–10 685x

    2022

  82. [90]

    Imagen video: High definition video generation with diffusion models,

    J. Ho, W. Chan, C. Saharia, J. Whang, R. Gao, A. Gritsenko, D. P. Kingma, B. Poole, M. Norouzi, D. J. Fleet, et al. , “Imagen video: High definition video generation with diffusion models,” arXiv preprint arXiv:2210.02303, 2022

    arXiv

  83. [91]

    Video diffusion models,

    J. Ho, T. Salimans, A. Gritsenko, W. Chan, M. Norouzi, and D. J. Fleet, “Video diffusion models,” arXiv:2204.03458, 2022

    2022 arXiv

  84. [92]

    Diffusion probabilistic mod- eling for video generation,

    R. Yang, P. Srivastava, and S. Mandt, “Diffusion probabilistic mod- eling for video generation,” Entropy, vol. 25, no. 10, p. 1469, 2023

    2023

  85. [93]

    Align your latents: High-resolution video synthesis with latent diffusion models,

    A. Blattmann, R. Rombach, H. Ling, T. Dockhorn, S. W. Kim, S. Fidler, and K. Kreis, “Align your latents: High-resolution video synthesis with latent diffusion models,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2023

    2023

  86. [94]

    Stochastic trajectory prediction via motion indeterminacy diffusion,

    T. Gu, G. Chen, J. Li, C. Lin, Y . Rao, J. Zhou, and J. Lu, “Stochastic trajectory prediction via motion indeterminacy diffusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 17 092–17 101

    2022

  87. [95]

    Dice: Diverse diffusion model with scoring for trajectory prediction,

    Y . Choi, R. C. Mercurius, S. M. A. Shabestary, and A. Rasouli, “Dice: Diverse diffusion model with scoring for trajectory prediction,” arXiv preprint arXiv:2310.14570, 2023

    2023 arXiv

  88. [96]

    Equidiff: A condi- tional equivariant diffusion model for trajectory prediction,

    K. Chen, X. Chen, Z. Yu, M. Zhu, and H. Yang, “Equidiff: A condi- tional equivariant diffusion model for trajectory prediction,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023, pp. 746–751

    2023

  89. [97]

    Trace and pace: Controllable pedestrian animation via guided trajectory diffusion,

    D. Rempe, Z. Luo, X. B. Peng, Y . Yuan, K. Kitani, K. Kreis, S. Fidler, and O. Litany, “Trace and pace: Controllable pedestrian animation via guided trajectory diffusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 756–13 766

    2023

  90. [98]

    Bcdiff: Bidirectional consistent diffusion for instantaneous trajectory predic- tion,

    R. Li, C. Li, D. Ren, G. Chen, Y . Yuan, and G. Wang, “Bcdiff: Bidirectional consistent diffusion for instantaneous trajectory predic- tion,” in Advances in Neural Information Processing Systems, A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, Eds., vol. 3...

    2023

  91. [99]

    Leapfrog diffusion model for stochastic trajectory prediction,

    W. Mao, C. Xu, Q. Zhu, S. Chen, and Y . Wang, “Leapfrog diffusion model for stochastic trajectory prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023, pp. 5517–5526

    2023

  92. [100]

    Diffusion-based environment- aware trajectory prediction,

    T. Westny, B. Olofsson, and E. Frisk, “Diffusion-based environment- aware trajectory prediction,” arXiv preprint arXiv:2403.11643, 2024

    2024 arXiv

  93. [101]

    Guided conditional diffusion for controllable traffic simulation,

    Z. Zhong, D. Rempe, D. Xu, Y . Chen, S. Veer, T. Che, B. Ray, and M. Pavone, “Guided conditional diffusion for controllable traffic simulation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 3560–3566

    2023

  94. [102]

    Scenedm: Scene-level multi-agent trajectory generation with consistent diffusion models,

    Z. Guo, X. Gao, J. Zhou, X. Cai, and B. Shi, “Scenedm: Scene-level multi-agent trajectory generation with consistent diffusion models,” arXiv preprint arXiv:2311.15736 , 2023

    2023 arXiv

  95. [103]

    Tsdit: Traffic scene diffusion models with transformers,

    C. Yang and T. Shi, “Tsdit: Traffic scene diffusion models with transformers,” arXiv preprint arXiv:2405.02289 , 2023

    2023 arXiv

  96. [104]

    Drag- traffic: A non-expert interactive and point-based controllable traf- fic scene generation framework,

    S. Wang, G. Sun, F. Ma, T. Hu, Y . Song, L. Zhu, and M. Liu, “Drag- traffic: A non-expert interactive and point-based controllable traf- fic scene generation framework,” arXiv preprint arXiv:2404.12624 , 2024

    2024 arXiv

  97. [105]

    Language-guided traffic simulation via scene-level diffusion,

    Z. Zhong, D. Rempe, Y . Chen, B. Ivanovic, Y . Cao, D. Xu, M. Pavone, and B. Ray, “Language-guided traffic simulation via scene-level diffusion,” in Conference on Robot Learning . PMLR, 2023, pp. 144–177

    2023

  98. [106]

    Editing driver character: Socially-controllable behavior generation for interactive traffic simulation,

    W.-J. Chang, C. Tang, C. Li, Y . Hu, M. Tomizuka, and W. Zhan, “Editing driver character: Socially-controllable behavior generation for interactive traffic simulation,” IEEE Robotics and Automation Letters, vol. 8, no. 9, pp. 5432–5439, 2023

    2023

  99. [107]

    Scenario diffusion: Controllable driving sce- nario generation with diffusion,

    E. Pronovost, M. R. Ganesina, N. Hendy, Z. Wang, A. Morales, K. Wang, and N. Roy, “Scenario diffusion: Controllable driving sce- nario generation with diffusion,” in Advances in Neural Information Processing Systems, A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S....

    2023

  100. [108]

    Sledge: Synthesizing simula- tion environments for driving agents with generative models,

    K. Chitta, D. Dauner, and A. Geiger, “Sledge: Synthesizing simula- tion environments for driving agents with generative models,” arXiv preprint arXiv:2403.17933, 2024

    2024 arXiv

  101. [109]

    Planning with diffusion for flexible behavior synthesis,

    M. Janner, Y . Du, J. B. Tenenbaum, and S. Levine, “Planning with diffusion for flexible behavior synthesis,” arXiv preprint arXiv:2205.09991, 2022

    2022 arXiv

  102. [110]

    Diffusion policies as an expressive policy class for offline reinforcement learning,

    Z. Wang, J. J. Hunt, and M. Zhou, “Diffusion policies as an expressive policy class for offline reinforcement learning,” arXiv preprint arXiv:2208.06193, 2022

    2022 arXiv

  103. [111]

    Is conditional generative modeling all you need for decision- making?

    A. Ajay, Y . Du, A. Gupta, J. Tenenbaum, T. Jaakkola, and P. Agrawal, “Is conditional generative modeling all you need for decision- making?” arXiv preprint arXiv:2211.15657 , 2022

    2022 arXiv

  104. [112]

    Idql: Implicit q-learning as an actor-critic method with diffusion policies,

    P. Hansen-Estruch, I. Kostrikov, M. Janner, J. G. Kuba, and S. Levine, “Idql: Implicit q-learning as an actor-critic method with diffusion policies,” arXiv preprint arXiv:2304.10573 , 2023

    2023 arXiv

  105. [113]

    Diffusion model is an effective planner and data synthesizer for multi-task reinforcement learning,

    H. He, C. Bai, K. Xu, Z. Yang, W. Zhang, D. Wang, B. Zhao, and X. Li, “Diffusion model is an effective planner and data synthesizer for multi-task reinforcement learning,” arXiv preprint arXiv:2305.18459, 2023

    2023 arXiv

  106. [114]

    Imitating human behaviour with diffusion models,

    T. Pearce, T. Rashid, A. Kanervisto, D. Bignell, M. Sun, R. Georgescu, S. V . Macua, S. Z. Tan, I. Momennejad, K. Hofmann, et al. , “Imitating human behaviour with diffusion models,” arXiv preprint arXiv:2301.10677, 2023

    2023 arXiv

  107. [115]

    Diffusion-es: Gradient-free planning with diffusion for autonomous driving and zero-shot instruction following,

    B. Yang, H. Su, N. Gkanatsios, T.-W. Ke, A. Jain, J. Schneider, and K. Fragkiadaki, “Diffusion-es: Gradient-free planning with diffusion for autonomous driving and zero-shot instruction following,” arXiv preprint arXiv:2402.06559, 2024

    2024 arXiv

  108. [116]

    Safediffuser: Safe planning with diffusion probabilistic models,

    W. Xiao, T.-H. Wang, C. Gan, and D. Rus, “Safediffuser: Safe planning with diffusion probabilistic models,” arXiv preprint arXiv:2306.00148, 2023

    2023 arXiv

  109. [117]

    Ltldog: Satisfying temporally-extended symbolic constraints for safe diffusion-based planning,

    Z. Feng, H. Luan, P. Goyal, and H. Soh, “Ltldog: Satisfying temporally-extended symbolic constraints for safe diffusion-based planning,” IEEE Robotics and Automation Letters , vol. 9, no. 10, pp. 8571–8578, 2024

    2024

  110. [118]

    Cgd: Constraint-guided diffusion policies for uav trajectory planning,

    K. Kondo, A. Tagliabue, X. Cai, C. Tewari, O. Garcia, M. Espitia- Alvarez, and J. P. How, “Cgd: Constraint-guided diffusion policies for uav trajectory planning,” arXiv preprint arXiv:2405.01758, 2024

    2024 arXiv

  111. [119]

    Parting with misconceptions about learning-based vehicle motion planning,

    D. Dauner, M. Hallgarten, A. Geiger, and K. Chitta, “Parting with misconceptions about learning-based vehicle motion planning,” in Conference on Robot Learning (CoRL) , 2023

    2023

  112. [120]

    U. M. Ascher and L. R. Petzold, Computer Meth- ods for Ordinary Differential Equations and Differential- Algebraic Equations . Philadelphia, PA: Society for Indus- trial and Applied Mathematics, 1998. [Online]. Available: https://epubs.siam.org/doi/abs/10.1137/1.9781611971392

    1998 doi

  113. [121]

    Congested traffic states in empirical observations and microscopic simulations,

    M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Physical review E, vol. 62, no. 2, p. 1805, 2000

    2000

  114. [122]

    Nuplan: A closed-loop ml- based planning benchmark for autonomous vehicles,

    K. T. e. a. H. Caesar, J. Kabzan, “Nuplan: A closed-loop ml- based planning benchmark for autonomous vehicles,” in CVPR ADP3 workshop, 2021

    2021

  115. [123]

    Financial prediction with constrained tail risk,

    A. A. Trindade, S. Uryasev, A. Shapiro, and G. Zrazhevsky, “Financial prediction with constrained tail risk,” Journal of Banking & Finance, vol. 31, no. 11, pp. 3524–3538, 2007, risk Management and Quantitative Approaches in Finance. [Online]. Available: https://www.sciencedir...

    2007

  116. [124]

    Monte carlo methods for value-at-risk and conditional value-at-risk: A review,

    L. J. Hong, Z. Hu, and G. Liu, “Monte carlo methods for value-at-risk and conditional value-at-risk: A review,” ACM Trans. Model. Comput. Simul., vol. 24, no. 4, nov 2014. [Online]. Available: https://doi.org/10.1145/2661631

    2014 doi

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.