用可解释的神经网络模型预测行人移动，比传统方法更准
From GEV to ResLogit: Spatially Correlated Discrete Choice Models for Pedestrian Movement Prediction
- 用残差神经网络改进逻辑回归，学习不同选择间的关联
- 在nuScenes和Argoverse 2数据上误差降低，邻近网格错误集中
- 适合自动驾驶中行人行为预测，兼顾准确与可解释性
高频率行人运动预测在与自动驾驶汽车交互时，可通过离散选择模型等行为框架提升。本文将行人下一步选择建模为基于速度调整与航向变化网格的时空离散选择。利用nuScenes和Argoverse 2中的自然交互数据（1秒决策间隔），评估了多项式逻辑回归基线及四种空间广义极值（GEV）结构（SCL、GSCL、SCNL、GSCNL）。进一步对比一种残差神经网络逻辑回归（ResLogit）模型，该模型在保留可解释线性效用项的同时学习跨选择影响。结果表明，空间GEV结构相比多项式逻辑回归仅带来微弱改进，而ResLogit显著提升拟合效果，并产生集中在邻近网格的行为一致误差。说明在密集高频率的空间选择集下，基于学习的残差修正能更有效捕捉邻近诱导的相关性，同时保持模型可解释性。
原文摘要 · Abstract (English)
High frequency pedestrian motion forecasting when interacting with autonomous vehicles (AVs) can be enhanced through the use of behavioural frameworks, such as discrete choice models, that can explicitly account for correlation among similar movement alternatives. We formulate the pedestrian next step choice as a spatial discrete choice defined by a grid of speed adjustment and heading change. Using naturalistic pedestrian-AV encounters from nuScenes and Argoverse 2 (1 sec decision interval), we estimate a multinomial logit baseline and four spatial generalized extreme value (GEV) specifications (SCL, GSCL, SCNL, and GSCNL). We then compare them to a residual neural network logit (ResLogit) model that learns cross alternative effects while retaining an interpretable linear utility component. Across the evaluated data, spatial GEV structures yield only marginal improvements over multinomial logit, whereas ResLogit achieves a substantially better fit and produces behaviourally coherent errors concentrated among neighbouring grid cells. The results suggest that in dense, high frequency spatial choice sets, learning based residual corrections can capture proximity induced correlation more effectively than analyst specified GEV nesting structures, while maintaining interpretability.
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