This paper gives an in-depth study of hybrid deep learning methods for traffic sign recognition (TSR) considering challenges in different lighting, occlusions, and weather effects which are generally difficult to obtain using standard single-architecture models. We give comparisons of high-performance hybrid models combining CNNs and transformers, RNNs, GANs, and attention models on benchmark datasets such as GTSRB and BelgiumTS. Our organized examination establishes CNN-transformer hybrids registering higher accuracy (97.8%) than their non-hybrid CNN counterparts (94.3%), whereas CNN-attention mechanisms dominate in partial occlusions with 12% boost in detection rates. GAN-augmented approaches are demonstrated to be extremely invariant to illumination change with error reduction rates boosted by as much as 18% under low-light exposures. Most importantly, recurrent-convolutional hybrids provide inference 9% faster without substantial loss in accuracy. Computational cost analysis demonstrates that purposefully designed hybrid architectures are capable of achieving enhanced performance with small additional computational expense (average 1.3 × increase). The results confirm that hybrid architectures well outperform single-architecture strategies across a wide range of performance metrics and have wide-reaching implications for deployment of fault-resilient TSR systems in autonomous driving.