用隐式行为克隆提升自动驾驶多模式决策能力
Exploring multimodal implicit behavior learning for vehicle navigation in simulated cities
- 引入能量模型实现隐式行为克隆，捕捉多种合理驾驶行为
- 通过扰动专家动作生成反例，提升训练效果
- 在CARLA模拟城市中验证，优于传统行为克隆
标准行为克隆（BC）无法学习多模式驾驶决策，即同一场景下存在多种合理操作。本文探索基于能量模型（EBM）的隐式行为克隆（IBC），以更好地捕捉这种多模态特性。提出数据增强隐式行为克隆（DA-IBC），通过扰动专家动作生成IBC训练中的反例，并采用更优初始化进行无导数推理。在使用鸟瞰视角输入的CARLA模拟器中进行实验，结果表明DA-IBC在评估多模式行为学习的城市驾驶任务中优于标准IBC。所学能量景观能有效表示多模态动作分布，而传统BC无法实现。
原文摘要 · Abstract (English)
Standard Behavior Cloning (BC) fails to learn multimodal driving decisions, where multiple valid actions exist for the same scenario. We explore Implicit Behavioral Cloning (IBC) with Energy-Based Models (EBMs) to better capture this multimodality. We propose Data-Augmented IBC (DA-IBC), which improves learning by perturbing expert actions to form the counterexamples of IBC training and using better initialization for derivative-free inference. Experiments in the CARLA simulator with Bird's-Eye View inputs demonstrate that DA-IBC outperforms standard IBC in urban driving tasks designed to evaluate multimodal behavior learning in a test environment. The learned energy landscapes are able to represent multimodal action distributions, which BC fails to achieve.
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