用类脑扩散模型提升自动驾驶多车交互预测精度与可解释性
Planning-Aware Diffusion Networks for Enhanced Motion Forecasting in Autonomous Driving
- 基于类脑扩散机制，融合道路结构与交通规则进行动态轨迹预测
- 参数极少却显著提升预测准确率，结果在主流数据集上优于现有方法
- 适合关注可解释性与高效推理的自动驾驶系统研发人员
自动驾驶技术虽有长足进步，但现有模型难以充分捕捉多智能体环境中的复杂交互。为此，我们提出受大脑决策与协同机制启发的规划融合预测模型（PIFM）。PIFM 利用道路结构、交通规则及周围车辆行为等丰富上下文信息，提升预测准确性和可解释性。采用类神经扩散架构，模拟大脑对环境刺激和他车行为的动态响应，实现对场景中所有智能体未来轨迹的预测。大量实验验证了 PIFM 在提供可解释、神经科学驱动的解决方案方面的有效性，且参数量极低。
原文摘要 · Abstract (English)
Autonomous driving technology has seen significant advancements, but existing models often fail to fully capture the complexity of multi-agent environments, where interactions between dynamic agents are critical. To address this, we propose the Planning-Integrated Forecasting Model (PIFM), a novel framework inspired by neural mechanisms governing decision-making and multi-agent coordination in the brain. PIFM leverages rich contextual information, integrating road structures, traffic rules, and the behavior of surrounding vehicles to improve both the accuracy and interpretability of predictions. By adopting a diffusion-based architecture, akin to neural diffusion processes involved in predicting and planning, PIFM is able to forecast future trajectories of all agents within a scenario. This architecture enhances model transparency, as it parallels the brain's method of dynamically adjusting predictions based on external stimuli and other agents'behaviors. Extensive experiments validate PIFM's capacity to provide interpretable, neuroscience-driven solutions for safer and more efficient autonomous driving systems, with an extremely low number of parameters.
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