Reinforcement Learning-Based Post-Training for Robust End-to-End Autonomous Driving Under Dynamic Traffic Conditions

Abstract

End-to-end autonomous driving has advanced rapidly by coupling perception, prediction, planning, and control within unified neural architectures, yet its deployment under dynamic traffic conditions remains constrained by brittleness, distributional shift, and limited assurance. This paper examines reinforcement learning-based post-training as a system-level intervention for robust end-to-end driving. Rather than treating post-training as merely a final optimization step, the analysis positions it within a broader socio-technical architecture involving data pipelines, simulation infrastructure, safety governance, validation regimes, and operational oversight. The paper argues that post-training can improve robustness when it addresses rare interactive scenarios, reward misspecification, and long-horizon credit assignment, but that these benefits depend on careful separation between online adaptation and safety certification. Dynamic traffic conditions are analyzed as compound stressors that combine perceptual ambiguity, social negotiation, adversarial behavior, and infrastructure variability. The discussion considers architectural trade-offs among modular and monolithic policies, infrastructure requirements for replay and simulation, deployment constraints, sustainability costs, fairness concerns, and policy implications. Evaluation is framed as a multi-dimensional problem spanning stress testing, counterfactual simulation, edge-case discovery, and human factors. The conclusion is that reinforcement learning-based post-training is promising but not sufficient by itself; robust autonomy requires layered assurance, traceable adaptation, and governance institutions capable of managing continual learning in public spaces.