Autonomous driving planning requires not only accurate trajectory prediction but also coherent semantic alignment across perception, decision making, and motion generation. Existing vision-language-based approaches predominantly focus on improving trajectory accuracy, which may lead to limited behavioral consistency. In this paper, we reformulate planning as a structured autoregressive generation task, where reasoning, actions, and future trajectories are jointly produced from multimodal observations. Based on this formulation, we propose Trajectory-Decision Joint Preference Optimization (TDJPO), which is a rollout-based alignment framework equipped with a unified reward that simultaneously captures physical trajectory quality and decision-level coherence. Starting from a supervised fine-tuned model, we construct preference pairs through stochastic rollouts and optimize the model using direct preference optimization. Experimental results on the NuScenes-TP benchmark demonstrate that our approach consistently enhances both trajectory accuracy and semantic consistency compared with supervised fine tuning, trajectory-only optimization, and lightweight vision-language baselines. These findings emphasize the necessity of jointly aligning physical feasibility and decision-level reasoning for achieving coherent and human-like autonomous driving behavior.
Liu et al. (Mon,) studied this question.