让自动驾驶预测更可解释，能看清影响决策的关键环境因素。
Scene-Aware Explainable Multimodal Trajectory Prediction
- 用改进的扩散模型捕捉多种可能行驶路径
- 通过可解释性分析发现关键影响因素，准确率显著提升
- 结果符合人类驾驶经验，适合需要透明决策的自动驾驶场景
智能技术的进步显著提升了复杂交通环境中自动驾驶车辆的环境感知与轨迹预测能力。然而，现有研究常忽视场景中各类参与者之间的联合推理，且轨迹预测模型缺乏可解释性，限制了其在真实场景中的应用。为此，我们提出可解释的条件扩散多模态轨迹预测模型（DMTP），旨在揭示影响预测的环境因素及其作用机制。该模型采用改进的条件扩散方法捕捉多模态轨迹模式，并引入优化的谢尔利值模型评估全局与场景特异性特征的重要性。在Waymo Open Motion Dataset上的实验表明，该可解释模型能有效识别关键输入，显著优于基线模型，在准确性上表现突出。更重要的是，识别出的影响因素与人类驾驶经验高度一致，验证了模型学习到的预测具有实际合理性。代码已开源：https://github.com/ocean-luna/Explainable-Prediction。
原文摘要 · Abstract (English)
Advancements in intelligent technologies have significantly improved navigation in complex traffic environments by enhancing environment perception and trajectory prediction for automated vehicles. However, current research often overlooks the joint reasoning of scenario agents and lacks explainability in trajectory prediction models, limiting their practical use in real-world situations. To address this, we introduce the Explainable Conditional Diffusion-based Multimodal Trajectory Prediction (DMTP) model, which is designed to elucidate the environmental factors influencing predictions and reveal the underlying mechanisms. Our model integrates a modified conditional diffusion approach to capture multimodal trajectory patterns and employs a revised Shapley Value model to assess the significance of global and scenario-specific features. Experiments using the Waymo Open Motion Dataset demonstrate that our explainable model excels in identifying critical inputs and significantly outperforms baseline models in accuracy. Moreover, the factors identified align with the human driving experience, underscoring the model's effectiveness in learning accurate predictions. Code is available in our open-source repository: https://github.com/ocean-luna/Explainable-Prediction.
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