Big Data-Driven Multi-Constraint Learning for Adaptive Safe Driving Control

End-to-end autonomous driving systems have demonstrated remarkable potential in navigating complex environments by directly mapping sensory inputs to control actions. However, many existing approaches rely on a single encoder to compress driving big data into a latent vector, which does not explicitly convert driving conditions into internal safety constraints. In addition, some approaches often depend on static, inflexible safety boundaries driven by mathematical equations, and they lack a clear mechanism to adapt control actions under varying conditions. This disconnection between conditions and control actions limits their ability to maintain safe operational controls in diverse kinds of scenarios. To address these limitations, we propose a Big Data-Driven Multi-Constraint Learning framework to generate safe driving action proxies derived from ego motion represented by steering and acceleration. Our architecture uses two encoders in cascade: a Temporal State Encoder that extracts a latent state representation from input driving states using temporal self-attention, and a Latent Constraint Generation Encoder that transforms this latent state into a compact internal constraint vector representing flexible and dynamic safety boundaries instead of static mathematical equations. This constraint vector is fused with the latent state representation to guide a Control Network for action generation, while a Condition Classifier and a Safety Discriminator enforce condition adaptability and safety. We evaluated the framework on the large-scale nuScenes big data repository, and experimental results demonstrated that our deep learning analysis successfully uncovers hidden safety patterns across massive driving logs, yielding stable convergence with low errors, notably a normalized Steering Mean Absolute Error (MAE) of 0.0189, a normalized Acceleration Mean Absolute Error of 0.0387, and strong safety discrimination with a Safety Accuracy of 0.9996, indicating that the learned internal safety constraints effectively modulate control actions under diverse driving states and uncertainties.