From GEV to ResLogit: Spatially Correlated Discrete Choice Models for Pedestrian Movement Prediction
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 (ResLogi...
| Authors | Rulla Al-Haideri, Bilal Farooq |
|---|---|
| Primary category | physics.soc-ph |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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