让自动驾驶模仿用户驾驶风格，可调且安全。
STAGE: STyle-controllable Action GEneration for personalized autonomous driving
- 基于模仿学习与偏好判断，用连续风格值建模驾驶习惯。
- 在多种场景下生成动作与人类预期高度一致，显著提升代入感。
- 适合个性化需求强的用户，尤其关注驾驶风格适配的开发者。
驾驶风格指司机在行驶中保持的行为偏好，由个人经验、习惯和需求塑造，通常体现为激进程度差异。若人类使用自动驾驶系统，期望其驾驶风格贴近自身习惯，但当前工业级系统难以实现。为此，我们提出一种风格可控的动作生成方法 STAGE。训练阶段采用模仿学习，融合风格值与潜在动作模态编码；通过偏好学习识别用户驾驶风格为连续单调的风格值，并设计规则自动比较数据对中的驾驶风格以降低人工标注成本。推理时，用户输入风格值即可动态控制生成动作模式，满足个性化期望。实验验证，STAGE 在多个典型道路场景中生成动作与人类预期高度契合。对比分析表明，该方法具备风格可控性、连续性、风格对齐能力及驾驶安全性等独特优势。代码已开源：https://github.com/CarlDegio/STAGE。
原文摘要 · Abstract (English)
Driving style refers to the behavioral preferences that drivers maintain during driving, shaped by their diverse experiences, habits, and needs, and is typically reflected in varying levels of aggressiveness. If humans choose to use autonomous driving systems, they would expect the driving style of the systems to closely resemble their own habit. However, this is challenging for current industrial autonomous driving systems. To address this, we developed a style controllable action generation method, STAGE, for driving tasks. Its training process is based on imitation learning, incorporating both style value and latent value action modality encoding. Preference learning is then used to identify the user's driving style as a continuous, monotonic style value. And to reduce the cost of human involvement in the preference training process, we also developed a set of rules to compare driving style in data pairs. Then, during inference, the user inputs the style value to control the generated action patterns, dynamically meeting the user's expectations. Using the STAGE method, we verified that the style-controlled action generation results in several typical road scenarios significantly align with human expectations. Furthermore, through comparisons between the STAGE method and various other approaches, we reveal the unique functionalities of STAGE, including its style controllability, style continuity, driving style alignment capability and driving safety. The code for this work is available at: https://github.com/CarlDegio/STAGE
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