# From GEV to ResLogit: Spatially Correlated Discrete Choice Models for Pedestrian Movement Prediction

arXiv: https://arxiv.org/abs/2603.01325 · HTML: https://mlsanity.com/paper/2603.01325/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 1 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [CoverNet: Multimodal Behavior Prediction using Trajectory Sets](https://mlsanity.com/paper/1911.10298/) (2019)
- [Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice](https://mlsanity.com/paper/1805.11773/) (2018)
- [A context-aware pedestrian trajectory prediction framework for automated vehicles](https://mlsanity.com/paper/2104.08123/) (2021)
- [Ordinal-ResLogit: Interpretable Deep Residual Neural Networks for Ordered Choices](https://mlsanity.com/paper/2204.09187/) (2022)
- [Modelling Pedestrian Behaviour in Autonomous Vehicle Encounters Using Naturalistic Dataset](https://mlsanity.com/paper/2602.05142/) (2026)

## Abstract (arXiv)

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.