Evaluation as Evolution: Transforming Adversarial Diffusion into Closed-Loop Curricula for Autonomous Vehicles

• URL: http://arxiv.org/abs/2604.07378v1
• Date: Wed, 08 Apr 2026 01:34:05 GMT
• Title: Evaluation as Evolution: Transforming Adversarial Diffusion into Closed-Loop Curricula for Autonomous Vehicles
• Abstract summary: We introduce Evaluation as Evolution ($E2$), a closed-loop framework that transforms adversarial generation into an adaptive evolutionary curriculum.<n>We utilize topology-driven support selection to identify critical interacting agents, and introduce Topological Anchoring to stabilize the process.<n> Empirically, $E2$ improves collision failure discovery by 9.01% on the nuScenes dataset and up to 21.43% on the nuPlan dataset.
• Score: 43.930171390617055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous vehicles in interactive traffic environments are often limited by the scarcity of safety-critical tail events in static datasets, which biases learned policies toward average-case behaviors and reduces robustness. Existing evaluation methods attempt to address this through adversarial stress testing, but are predominantly open-loop and post-hoc, making it difficult to incorporate discovered failures back into the training process. We introduce Evaluation as Evolution ($E^2$), a closed-loop framework that transforms adversarial generation from a static validation step into an adaptive evolutionary curriculum. Specifically, $E^2$ formulates adversarial scenario synthesis as transport-regularized sparse control over a learned reverse-time SDE prior. To make this high-dimensional generation tractable, we utilize topology-driven support selection to identify critical interacting agents, and introduce Topological Anchoring to stabilize the process. This approach enables the targeted discovery of failure cases while strictly constraining deviations from realistic data distributions. Empirically, $E^2$ improves collision failure discovery by 9.01% on the nuScenes dataset and up to 21.43% on the nuPlan dataset over the strongest baselines, while maintaining low invalidity and high realism. It further yields substantial robustness gains when the resulting boundary cases are recycled for closed-loop policy fine-tuning.

Related papers

• IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning [57.16513298507272]
  We propose IADD-TR, a unified framework combining Intervention-Aware Dynamics Decoupling (IADD) and Targeted Regularization (TR)<n>IADD factorizes transitions into an action-intervention stage and an action-free natural evolution stage, using a zero-action anchor to resolve the non-uniqueness of this two-stage factorization for robust generalization.<n>For policy learning, we derive TR from the efficient influence function of a replay-state policy-gradient functional.
  arXiv  Detail & Related papers  (2026-08-11T08:20:02Z)
• From Attacks to Curricula: Learnability-Guided Adversarial Training for Safe Autonomous Driving [56.30087557121323]
  AlignADV is a learnability-guided closed-loop adversarial training framework.<n>It converts adversarial scenarios into resolvable and capability-aligned curricula.<n> Experiments show it reduces training steps by up to 40.6 percent.
  arXiv  Detail & Related papers  (2026-06-12T02:13:55Z)
• Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry [46.0185525503119]
  Implicit Drifting Policy (IDP) is a one-step imitation learning framework for robot control.<n>IDP brings the training-time correction of Drifting into policy learning without explicit vector field estimation.
  arXiv  Detail & Related papers  (2026-05-31T08:39:57Z)
• Real-Time Evaluation of Autonomous Systems under Adversarial Attacks [0.29360071145551064]
  This work presents an offline trajectory-learning and adversarial robustness evaluation framework grounded in real-world intersection driving data.<n>We train and compare three trajectory-learning paradigms: Multi-Layer Perceptron (MLP)-based Behavior Cloning (BC), Transformer-based object-tokenized BC, and inverse reinforcement learning (IRL)<n>Inference-time robustness is assessed by subjecting trained policies to gradient-based adversarial perturbations across multiple intersection scenarios.
  arXiv  Detail & Related papers  (2026-05-05T08:30:18Z)
• Rectify, Don't Regret: Avoiding Pitfalls of Differentiable Simulation in Trajectory Prediction [8.730908751307737]
  Current open-loop trajectory models struggle in real-world autonomous driving because minor initial deviations cascade into compounding errors.<n>We introduce a detached receding horizon rollout to explicitly sever the graph between simulation steps.<n>Our approach yields more robust recovery strategies, reducing target collisions by up to 33.24%.
  arXiv  Detail & Related papers  (2026-03-24T16:28:09Z)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• ELLIPSE: Evidential Learning for Robust Waypoints and Uncertainties [15.18822929289763]
  We present textitELLIPSE, a method building on multivariate deep evidential regression to output waypoints and multivariate Student-t predictive distributions in a single forward pass.<n>To reduce co-shift-induced overconfidence under viewpoint and pose perturbations near expert trajectories, we introduce a lightweight domain augmentation procedure.<n>We show that textitELLIPSE improves both task success rate and uncertainty coverage compared to baselines.
  arXiv  Detail & Related papers  (2026-03-04T20:29:19Z)
• Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving [54.46325690390831]
  We propose Model-based Policy Adaptation (MPA), a general framework that enhances the robustness and safety of pretrained E2E driving agents during deployment.<n>MPA first generates diverse counterfactual trajectories using a geometry-consistent simulation engine.<n>MPA trains a diffusion-based policy adapter to refine the base policy's predictions and a multi-step Q value model to evaluate long-term outcomes.
  arXiv  Detail & Related papers  (2025-11-26T17:01:41Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification [3.126303871979975]
  SHIFT (Spectral Heteroscedastic Informed Forecasting for Trajectories) is a novel framework that combines well-calibrated uncertainty modeling with informative priors.<n>Our model excels in complex scenarios, such as intersections, where uncertainty is inherently higher.
  arXiv  Detail & Related papers  (2025-04-17T17:24:50Z)
• Time-series Generation by Contrastive Imitation [87.51882102248395]
  We study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy. At inference, the learned policy serves as the generator for iterative sampling, and the learned energy serves as a trajectory-level measure for evaluating sample quality.
  arXiv  Detail & Related papers  (2023-11-02T16:45:25Z)
• Off-Policy Evaluation for Large Action Spaces via Policy Convolution [60.6953713877886]
  Policy Convolution family of estimators uses latent structure within actions to strategically convolve the logging and target policies. Experiments on synthetic and benchmark datasets demonstrate remarkable mean squared error (MSE) improvements when using PC.
  arXiv  Detail & Related papers  (2023-10-24T01:00:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.