Self-Aware Active Learning Enables Continual Improvement in Autonomous Driving

• URL: http://arxiv.org/abs/2608.29772v1
• Date: Sun, 30 Aug 2026 13:01:32 GMT
• Title: Self-Aware Active Learning Enables Continual Improvement in Autonomous Driving
• Abstract summary: Self-aware guided exploration (SAGE) is an active learning framework for post-training adaptation in autonomous driving.<n>Curiosity adaptively calibrates the intervention threshold for fear, allowing the agent to regulate risk in a context-dependent manner.<n>Fear is integrated into policy optimization and evaluation as a safety-oriented constraint to reduce performance regressions.
• Score: 12.669051115615176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based autonomous driving (AD) systems can perform reliably in familiar conditions, yet rare distribution shifts and long-tail events remain a major source of abrupt failure. A central limitation is that most agents learn primarily from passive experience and lack mechanisms to estimate when their competence is insufficient, seek timely assistance, and convert safety-critical encounters into targeted improvement. Here we present self-aware guided exploration (SAGE), an active learning framework for post-training adaptation in AD. SAGE learns a predictive world model that generates two online intrinsic signals: fear, which estimates short-horizon predictive risk and model uncertainty, and curiosity, which measures novelty through prediction error. Curiosity adaptively calibrates the intervention threshold for fear, allowing the agent to regulate risk in a context-dependent manner. When predicted fear exceeds this adaptive threshold, the agent transfers control to an expert or fallback policy and uses the resulting takeover trajectories for focused imitation learning. In parallel, fear is integrated into policy optimization and evaluation as a safety-oriented constraint to reduce performance regressions during adaptation. We evaluate SAGE in simulated route-transfer tasks, Waymo-based logged driving scenarios, CARLA occlusion hazards, and real-world mobile robot navigation tests. Across these settings, SAGE improves robustness in novel and safety-critical scenarios, reduces safety violations, and maintains task performance comparable to strong baseline policies. These results suggest that agents can improve after initial training by estimating the limits of their competence, requesting guidance when needed, and learning selectively from rare high-value events.

Related papers

• 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)
• Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving [9.708935332287265]
  Exploration in reinforcement learning for autonomous driving is inherently unsafe.<n>We propose an uncertainty-aware framework that leverages expert advice to guide exploration while avoiding long-term dependence.<n>Our method outperforms the IQN baseline, improving success by 5-7% and reducing failures.
  arXiv  Detail & Related papers  (2026-05-28T21:09:44Z)
• Learning to Stay Safe: Adaptive Regularization Against Safety Degradation during Fine-Tuning [2.9184958249079975]
  Existing defenses offer limited protection or force a trade-off between safety and utility.<n>We introduce a training framework that adapts regularization in response to safety risk.<n>We empirically verify that harmful intent signals are predictable from pre-generation activations.
  arXiv  Detail & Related papers  (2026-02-19T16:59:54Z)
• Steering Externalities: Benign Activation Steering Unintentionally Increases Jailbreak Risk for Large Language Models [62.16655896700062]
  Activation steering is a technique to enhance the utility of Large Language Models (LLMs)<n>We show that it unintentionally introduces critical and under-explored safety risks.<n>Experiments reveal that these interventions act as a force multiplier, creating new vulnerabilities to jailbreaks and increasing attack success rates to over 80% on standard benchmarks.
  arXiv  Detail & Related papers  (2026-02-03T12:32:35Z)
• Sparse Threats, Focused Defense: Criticality-Aware Robust Reinforcement Learning for Safe Autonomous Driving [11.62520853262219]
  We introduce criticality-aware robust RL (CARRL) for handling sparse, safety-critical risks in autonomous driving.<n>CARRL consists of two interacting components: a risk exposure adversary (REA) and a risk-targeted robust agent (RTRA)<n>We show that our approach reduces the collision rate by at least 22.66% across all cases compared to state-of-the-art baseline methods.
  arXiv  Detail & Related papers  (2026-01-05T05:20:16Z)
• Preliminary Investigation into Uncertainty-Aware Attack Stage Classification [81.28215542218724]
  This work addresses the problem of attack stage inference under uncertainty.<n>We propose a classification approach based on Evidential Deep Learning (EDL), which models predictive uncertainty by outputting parameters of a Dirichlet distribution over possible stages.<n>Preliminary experiments in a simulated environment demonstrate that the proposed model can accurately infer the stage of an attack with confidence.
  arXiv  Detail & Related papers  (2025-08-01T06:58:00Z)
• Active Test-time Vision-Language Navigation [60.69722522420299]
  ATENA is a test-time active learning framework that enables a practical human-robot interaction via episodic feedback on uncertain navigation outcomes.<n>In particular, ATENA learns to increase certainty in successful episodes and decrease it in failed ones, improving uncertainty calibration.<n>In addition, we propose a self-active learning strategy that enables an agent to evaluate its navigation outcomes based on confident predictions.
  arXiv  Detail & Related papers  (2025-06-07T02:24:44Z)
• Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning [11.602831593017427]
  This paper addresses the challenges of training end-to-end autonomous driving agents using Reinforcement Learning (RL)<n>RL agents are typically trained in a fixed set of scenarios and nominal behavior of surrounding road users in simulations.<n>We propose an automatic curriculum learning framework that dynamically generates driving scenarios with adaptive complexity based on the agent's evolving capabilities.
  arXiv  Detail & Related papers  (2025-05-13T06:26:57Z)
• Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings [129.80279257258098]
  Reinforcement learning (RL) in real-world safety-critical target settings like urban driving is hazardous. We propose a "safety-critical adaptation" task setting: an agent first trains in non-safety-critical "source" environments. We propose a solution approach, CARL, that builds on the intuition that prior experience in diverse environments equips an agent to estimate risk.
  arXiv  Detail & Related papers  (2020-08-15T01:40:59Z)
• Safe Reinforcement Learning via Curriculum Induction [94.67835258431202]
  In safety-critical applications, autonomous agents may need to learn in an environment where mistakes can be very costly. Existing safe reinforcement learning methods make an agent rely on priors that let it avoid dangerous situations. This paper presents an alternative approach inspired by human teaching, where an agent learns under the supervision of an automatic instructor.
  arXiv  Detail & Related papers  (2020-06-22T10:48:17Z)

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.