ARXIV:2605.30576 · REINFORCEMENT LEARNING FOR AUTONOMOUS DRIVING · SUBMITTED 01 JUN · 20:29 UTC · FRESHNESS STALE

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Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving

Ahmed Abouelazm · Felix Klingebiel · Philip Schörner · J. Marius Zöllner · arXiv

An uncertainty-aware reinforcement learning framework guides exploration in autonomous driving using expert advice and adaptive thresholds to improve safety and efficiency.

Ship in 2-4 weeks›Score4.0Evidence unverified

Opportunity summary

Pain An uncertainty-aware reinforcement learning framework guides exploration in autonomous driving using expert advice and adaptive thresholds to improve safety and efficiency.

Evidence 0 refs | 3 sources | 50% coverage

Blocker Evidence unverified

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PROBLEM

An uncertainty-aware reinforcement learning framework guides exploration in autonomous driving using expert advice and adaptive thresholds to improve safety and efficiency. We propose an uncertainty-aware framework that leverages expert advice to guide exploration while…

METHOD

Full abstract

Exploration in reinforcement learning for autonomous driving is inherently unsafe: agents must experience novel behaviors to learn, yet exploration can lead to collisions or off-road driving. We propose an uncertainty-aware framework that leverages expert advice to guide exploration while avoiding long-term dependence. Advice is triggered when epistemic or aleatoric uncertainty exceeds adaptive thresholds derived from rolling buffers, ensuring advice evolves with the agent's confidence. A commitment-cooldown strategy with a stochastic early-stop heuristic regulates the duration and frequency of guidance, exposing the agent to coherent maneuvers without exhausting the advice budget. Expert and agent experiences are combined in a shared replay buffer within an off-policy implicit quantile network (IQN) backbone, enabling efficient reuse of expert trajectories. Experiments in CARLA show that our method outperforms the IQN baseline, improving success by 5-7% and reducing failures, demonstrating that risk-sensitive uncertainty coupled with regulated expert integration enables safer and more efficient exploration for sensor-based RL policy learning in unsignalized intersection navigation.

RESULT

ScienceToStartup currently rates this 4.0/10 on the public viability pass. Experiments in CARLA show that our method outperforms the IQN baseline, improving success by 5-7% and reducing failures, demonstrating that risk-sensitive uncertainty coupled with…

WHY NOW

Reinforcement Learning for Autonomous Driving moved forward this cycle; last verified June 2026. Public score 4.0/10. Production flags indicate code availability.

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Opportunity summary

Score4.0

PainAn uncertainty-aware reinforcement learning framework guides exploration in autonomous driving using expert advice and adaptive thresholds to improve safety and efficiency.

Evidence0 refs | 3 sources | 50% coverage

Blockerno shell-level blocker reported

Analysis summary

An uncertainty-aware reinforcement learning framework guides exploration in autonomous driving using expert advice and adaptive thresholds to improve safety and efficiency.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

An uncertainty-aware reinforcement learning framework guides exploration in autonomous driving using expert advice and adaptive thresholds to improve safety and efficiency.

Segment

Reinforcement Learning for Autonomous Driving

Adoption evidence

No public code link in the paper record yet

Commercial read

4.0/10 public viability

Direct

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Substitute

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Unknown

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Competitive landscape

An uncertainty-aware reinforcement learning framework guides exploration in autonomous driving using expert advice and adaptive thresholds to improve safety and efficiency.

Segment

Reinforcement Learning for Autonomous Driving

Adoption evidence

No public code link in the paper record yet

Commercial read

4.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving

Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving

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