Feature Importance in Pedestrian Intention Prediction: A Context-Aware Review

Mohsen Azarmi, Mahdi Rezaei, He Wang, Ali Arabian
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Abstract

Recent advancements in predicting pedestrian crossing intentions for Autonomous Vehicles using Computer Vision and Deep Neural Networks are promising. However, the black-box nature of DNNs poses challenges in understanding how the model works and how input features contribute to final predictions. This lack of interpretability delimits the trust in model performance and hinders informed decisions on feature selection, representation, and model optimisation; thereby affecting the efficacy of future research in the field. To address this, we introduce Context-aware Permutation Feature Importance (CAPFI), a novel approach tailored for pedestrian intention prediction. CAPFI enables more interpretability and reliable assessments of feature importance by leveraging subdivided scenario contexts, mitigating the randomness of feature values through targeted shuffling. This aims to reduce variance and prevent biased estimations in importance scores during permutations. We divide the Pedestrian Intention Estimation (PIE) dataset into 16 comparable context sets, measure the baseline performance of five distinct neural network architectures for intention prediction in each context, and assess input feature importance using CAPFI. We observed nuanced differences among models across various contextual characteristics. The research reveals the critical role of pedestrian bounding boxes and ego-vehicle speed in predicting pedestrian intentions, and potential prediction biases due to the speed feature through cross-context permutation evaluation. We propose an alternative feature representation by considering proximity change rate for rendering dynamic pedestrian-vehicle locomotion, thereby enhancing the contributions of input features to intention prediction. These findings underscore the importance of contextual features and their diversity to develop accurate and robust intent-predictive models.
行人意向预测中的特征重要性:情境感知回顾
利用计算机视觉和深度神经网络为自动驾驶汽车预测行人过马路意图的最新进展令人期待。然而,深度神经网络的黑箱性质给理解模型如何工作以及输入特征如何有助于最终预测带来了挑战。这种不可解释性限制了对模型性能的信任,阻碍了在特征选择、表示和模型优化方面做出明智的决策,从而影响了该领域未来研究的效率。为了解决这个问题,我们引入了上下文感知突变特征重要性(CAPFI),这是一种为行人意图预测量身定制的新方法。CAPFI 利用细分的场景上下文,通过有针对性的洗牌减轻特征值的随机性,从而提高特征重要性的可解释性和可靠评估。这样做的目的是减少差异,防止在排列过程中对重要度分数的估计出现偏差。我们将行人意向预测(PIE)数据集分为 16 个可比较的情境集,测量了五种不同神经网络架构在每个情境中的意向预测基准性能,并使用 CAPFI 评估了输入特征的重要性。我们观察到了不同语境特征下模型之间的细微差别。研究揭示了行人边界框和自我车辆速度在预测行人意图中的关键作用,并通过跨情境排列评估发现了速度特征可能导致的预测偏差。我们提出了另一种特征表示方法,即考虑近距离变化率来呈现动态的行人-车辆运动,从而提高输入特征对意图预测的贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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