Papers for

smart city traffic management teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Transformer model improves pedestrian crossing predictions for autonomous vehicles

TrajFusionNet+: Transformer-Based Prediction of Pedestrian Crossing Intention via Fusion of Trajectory Representations and Scene Graphs

Abstract: The pedestrian crossing intention task involves predicting whether pedestrians are likely to cross the road from the point of view of an autonomous vehicle. We introduce TrajFusionNet+, a novel transformer-based model for pedestrian crossing intention prediction. TrajFusionNet+ combines sequential and visual representations of pedestrian trajectory with a graph-based representation of the scene context in order to predict pedestrian crossing intention. The proposed architecture builds upon our previous model, TrajFusionNet, and comprises three branches: a Sequence Attention Module (SAM), which processes a sequential representation of past and predicted pedestrian trajectories; a Visual Attention Module (VAM), which utilizes a visual representation of the pedestrian trajectories by overlaying observed and predicted bounding boxes onto scene images; and a Graph Attention Module (GAM), which extracts pedestrian-centric graphs from segmented scene images and captures the relational dependencies between pedestrians and traffic elements. TrajFusionNet+ achieves improved state-of-the-art performance on the two most widely used pedestrian crossing intention datasets, PIE and JAAD. Furthermore, we introduce a new evaluation protocol in which models are trained jointly on the PIE and JAAD datasets but evaluated separately on each. Under this setting, TrajFusionNet+ demonstrates superior generalization compared to existing approaches.

Wed 9 SeptComputer Vision and Pattern Recognition

The gist

Predicting if a pedestrian will cross the road is important for self-driving cars to stay safe. The authors developed TrajFusionNet+, a new computer model that uses three kinds of information: past pedestrian movements, visuals of those movements overlaid on street scenes, and relationships between pedestrians and nearby traffic elements. This model combines all these views using transformers, a type of machine learning technique. It outperforms previous methods on popular pedestrian datasets and is better at handling different datasets, showing it can generalize well to new situations.

Open → 2609.10806v1