[论文解读] Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception

Yueyang Wang, Aravinda Ramakrishnan Srinivasan+3 more|arXiv (Cornell University)|Feb 6, 2024

Impact of Light on Environment and HealthEnvironmental Science被引用 3

一句话总结

本文提出了一种行人过街行为的有界最优决策模型，结合了有噪声的视觉感知与强化学习，以解释间隙接受行为与过街时机。研究结果表明，以往被视为认知偏差的行为——如与速度相关的间隙接受——实则是对感知限制的理性适应，该模型在复现经验现象方面优于先前的模型。

ABSTRACT

This paper presents a model of pedestrian crossing decisions, based on the theory of computational rationality. It is assumed that crossing decisions are boundedly optimal, with bounds on optimality arising from human cognitive limitations. While previous models of pedestrian behaviour have been either 'black-box' machine learning models or mechanistic models with explicit assumptions about cognitive factors, we combine both approaches. Specifically, we model mechanistically noisy human visual perception and assumed rewards in crossing, but we use reinforcement learning to learn bounded optimal behaviour policy. The model reproduces a larger number of known empirical phenomena than previous models, in particular: (1) the effect of the time to arrival of an approaching vehicle on whether the pedestrian accepts the gap, the effect of the vehicle's speed on both (2) gap acceptance and (3) pedestrian timing of crossing in front of yielding vehicles, and (4) the effect on this crossing timing of the stopping distance of the yielding vehicle. Notably, our findings suggest that behaviours previously framed as 'biases' in decision-making, such as speed-dependent gap acceptance, might instead be a product of rational adaptation to the constraints of visual perception. Our approach also permits fitting the parameters of cognitive constraints and rewards per individual, to better account for individual differences. To conclude, by leveraging both RL and mechanistic modelling, our model offers novel insights about pedestrian behaviour, and may provide a useful foundation for more accurate and scalable pedestrian models.

研究动机与目标

• 开发一种计算理性的行人过街决策模型，整合认知约束与环境动态。
• 解决黑箱机器学习模型与过于简化的机制模型在捕捉个体差异与现实世界复杂性方面的局限性。
• 探究传统上被标记为“偏差”的行人决策行为，是否实则是对感知噪声与认知限制的理性响应。
• 通过结合感知的机制建模与强化学习进行策略优化，提升模型的可解释性与可扩展性。
• 通过个体层面的参数拟合，更好地表征多样化的行人行为。

提出的方法

• 该模型采用计算理性假设，即行人在认知约束（如噪声视觉感知与运动延迟）下做出有界最优决策。
• 视觉感知被建模为对碰撞时间（TTA）的有噪声估计，参数包括感知方差（σv）与趋避逼近感（c）。
• 采用强化学习（RL）框架，学习在感知估计与环境参数条件下最优的间隙接受与过街时机策略。
• 将运动延迟（m）作为非策略参数，反映决策与动作执行之间的时滞。
• 测试了多种模型变体：VLM（基线）、VLDM（含运动延迟）、VNM（替代风险规避公式）、VLM(E)（共享参数）、以及VLM(S)（每个参数集对应独立策略）。
• 通过与人类行为数据对比，使用对数似然、AIC与平均绝对偏差（MAD）评估模型性能，涵盖恒速与让行车辆场景。

Figure 1: (a) Birds-eye view of the experiment. (b) A sample view of the virtual scene, as shown by the head-mounted display, at the beginning of each trial (inset) and as participants turned their heads to look for oncoming traffic. (Source: Pekkanen et al., 2021. Variable-drift diffusion models of

实验结果

研究问题

• RQ1 行人过街决策能否通过在有噪声视觉感知下的有界最优决策来解释？
• RQ2 车辆速度与停车距离如何影响行人对间隙的接受与过街时机？
• RQ3 如与速度相关的间隙接受等行为，是否更应被解释为对感知噪声的理性适应，而非认知偏差？
• RQ4 引入运动延迟与个体化感知参数在多大程度上提升了模型拟合度与预测准确性？
• RQ5 统一模型（VLM）在复现经验行人行为方面，与个体化或替代风险规避公式（如VLM(S)、VNM）相比表现如何？

主要发现

• VLM模型在拟合度与复杂度之间达到最佳平衡，对数似然为-536，AIC为1192，MAD为0.35秒，优于所有其他变体。
• 引入运动延迟（VLDM）并未提升性能，反而增加模型复杂度，尽管对数似然与MAD相近，但AIC更高。
• VLM(E)模型（所有参与者共享一组非策略参数）的AIC更低（1098），但对数似然更差，表明个体化参数可提升拟合度。
• VLM(S)模型（为每组参数独立训练RL策略）未提升性能，表明参数条件化策略学习更具效率。
• VNM模型（采用基于距离的风险规避而非逼近感）的MAD更高（0.99），AIC更差，表明时间-空间线索（如逼近感）比单纯空间接近性更为关键。
• 该模型成功复现了四个关键经验现象：(1) 间隙接受对TTA的依赖性，(2) 与速度相关的间隙接受，(3) 让行车辆场景下的过街时机，(4) 车辆停车距离对过街时机的影响。

Figure 2: Comparison of models. (a) BM: Baseline Model. (b) LM: Looming aversion only. (c) VM: Visual limitation only. (d) VLM: Looming aversion and visual limitations. The abbreviations were introduced in Section 2.2 . $\sigma_{\mathrm{v}}$ and $c$ represent the sensory noise and looming aversion w