Recent advances in Large Vision–Language Models (LVLMs) have demonstrated strong cross-modal reasoning capabilities, offering new opportunities for decision-making in autonomous driving. However, existing end-to-end approaches still suffer from limited semantic consistency, weak task controllability, and insufficient interpretability. To address these challenges, we propose SemAlign-E2E (Semantic-Aligned End-to-End), a semantic-aligned multimodal LVLM framework that unifies visual, LiDAR, and task-oriented textual inputs through cross-modal attention. This design enables end-to-end reasoning from scene understanding to high-level driving command generation. Beyond producing structured control instructions, the framework also provides natural-language explanations to enhance interpretability. We conduct extensive evaluations on the nuScenes dataset and CARLA simulation platform. Experimental results show that SemAlign-E2E achieves substantial improvements in driving stability, safety, multi-task generalization, and semantic comprehension, consistently outperforming state-of-the-art baselines. Notably, the framework exhibits superior behavioral consistency and risk-aware decision-making in complex traffic scenarios. These findings highlight the potential of LVLM-driven semantic reasoning for autonomous driving and provide a scalable pathway toward future semantic-enhanced end-to-end driving systems.

This package contains the numerical propagated interaction-field model, context-conditioned physics-informed neural network (PINN), field-aware reinforcement-learning pipeline, and processed measurements supporting the principal validation and simulator findings. The statistical workflow preserves the original observation/reward factorial and replaces only its complete-model merge candidate with the selected 0.25-times comfort profile. All five calibrated merging Soft Actor-Critic checkpoints and their five paired stock checkpoints are included. Policy-training seeds are 0-4; each policy is evaluated on traffic seeds 10000-10049 at density 0.3. The calibrated profile uses jerk weight 0.000375 and steering/throttle-change weights 0.00025. An indexed mapping connects each checkpoint to its effective training settings and episode CSV. The PINN checkpoint is accompanied by its available architecture, input channels, normalization, numerical-solver settings, loss weights, calibration-cache signatures, hardware record, training duration and 6,000-step loss history.

At autonomous intersections in mixed traffic, where Connected and Autonomous Vehicles (CAVs) coexist with Human-Driven Vehicles (HDVs), scheduling must remain effective even when a reliable explicit HDV turning intention is not available sufficiently early for the scheduling decision. This paper proposes a learning-based platoon scheduling method for this information-limited setting. CAVs and HDVs are organized into mixed platoons or an HDV group, and a state-augmentation function is designed to encode their temporal relationship while preserving the priority of uncontrollable HDVs. An enumeration (EN)-based expert demonstration mechanism is further integrated into the training process to provide high-quality experience. In the representative training run reported in the manuscript, the expert-assisted model achieved a peak average reward approximately 14% higher than the model trained without demonstrations. Across the tested demand cases, the learned scheduler also obtained travel-cost performance close to the EN benchmark. The scope of the method is explicitly limited to the modeled lane-keeping and sensing assumptions; robustness to random seeds, unexpected HDV maneuvers, communication delays, and additional safety metrics requires dedicated validation.