Decision-making and motion planning constitute critical components for ensuring the safety and efficiency of autonomous vehicles (AVs). Existing methodologies typically adopt two paradigms: decision then planning or generation then scoring. However, the former architecture often suffers from decision-planning misalignment that incurs risky situations. Meanwhile, the latter struggles to balance short-term operational metrics (e.g., immediate motion smoothness) with long-term tactical goals (e.g., route efficiency), resulting in myopic or overly conservative behaviors. To address these issues, we introduce CALMM-Drive, a novel Confidence-Aware Large Multimodal Model (LMM) empowered Autonomous Driving framework. Our approach integrates driving task-oriented Chain-of-Thought (CoT) reasoning coupled with Top-K confidence elicitation, which facilitates high-level reasoning to generate multiple candidate decisions with their confidence levels. Furthermore, we propose a novel planning module that integrates a diffusion model for trajectory generation and a hierarchical refinement process to find the optimal trajectory. This framework enables the selection over trajectory candidates accounting for both low-level solution quality and high-level tactical confidence, which avoids the risks within one-shot decisions and overcomes the limitations in short-sighted scoring mechanisms. Comprehensive evaluations in nuPlan closed-loop simulation environments demonstrate the competitive performance of CALMM-Drive across both common and long-tail benchmarks, showcasing a significant advancement in the integration of uncertainty in LMM-empowered AVs. The code will be released upon acceptance.

翻译：决策与运动规划是确保自动驾驶车辆安全与高效运行的关键组成部分。现有方法通常采用两种范式：先决策后规划，或先生成后评分。然而，前者架构常因决策与规划失配而引发风险；后者则难以平衡短期操作指标（如即时运动平顺性）与长期战术目标（如路径效率），导致短视或过于保守的行为。为解决这些问题，我们提出了CALMM-Drive，一种基于置信度感知大型多模态模型的新型自动驾驶框架。该方法集成了面向驾驶任务的链式推理与Top-K置信度激发机制，通过高层推理生成多个候选决策及其置信度。此外，我们提出了一种新颖的规划模块，该模块整合了用于轨迹生成的扩散模型与分层优化过程，以寻找最优轨迹。该框架能够基于底层解的质量与高层战术置信度对候选轨迹进行选择，从而避免单次决策的风险，并克服短视评分机制的局限。在nuPlan闭环仿真环境中的综合评估表明，CALMM-Drive在常规与长尾基准测试中均展现出竞争优势，标志着基于LMM的自动驾驶系统在不确定性整合方面取得了显著进展。代码将在论文录用后开源。