AI 中文总结
研究无人驾驶时谁理解并回应乘客，提出Intent2Drive框架，将意图建模为潜在认知状态，构建数据集和推理器，引入分层规划器，提升意图推理等能力，迈向响应乘客的自动驾驶系统。
AI 中文摘要
随着自动驾驶车辆向完全无人驾驶发展，出现关键问题：驾驶员不在时谁理解并回应乘客？现有自动驾驶系统感知和推理车内人类意图有限。本文提出Intent2Drive框架，将意图建模为潜在认知状态，构建数据集和推理器，引入分层规划器，实验表明其提升了意图推理等能力，迈向响应乘客的自动驾驶系统。
英文摘要
As autonomous vehicles advance toward driverless mobility, understanding and responding to passenger needs and intentions becomes increasingly important in the absence of a human driver. We propose Intent2Drive, a unified framework for holistic passenger intent understanding and passenger-aligned planning. Unlike existing methods that rely on explicit commands, Intent2Drive models passenger intent as a latent cognitive state inferred from language, personal attributes, emotions, behaviors, and situational context. To support this task, we construct the Holistic Passenger Intent Dataset (HPID) with structured annotations of explicit and implicit passenger-intent cues. A Theory-of-Mind-inspired Passenger Intent Reasoner (PIR) infers a Latent Passenger State (LPS) and converts it into a planner-compatible Passenger Intent Objective (PIO). We validate the downstream utility of PIO by conditioning an existing hierarchical planning pipeline at the route and trajectory levels. Experiments demonstrate that the proposed method understands and responds to passenger needs, enabling passenger-aligned driving while maintaining competitive closed-loop planning performance.
