VIT-Ped: Visionary Intention Transformer for Pedestrian Behavior Analysis
This addresses safety in autonomous driving by improving pedestrian behavior analysis, but it appears incremental as it builds on existing transformer methods for a specific domain.
The paper tackled pedestrian intention prediction for autonomous driving by introducing a transformer-based algorithm using multiple data modalities, achieving state-of-the-art performance on the JAAD dataset with improvements in accuracy, AUC, and F1-score.
Pedestrian Intention prediction is one of the key technologies in the transition from level 3 to level 4 autonomous driving. To understand pedestrian crossing behaviour, several elements and features should be taken into consideration to make the roads of tomorrow safer for everybody. We introduce a transformer / video vision transformer based algorithm of different sizes which uses different data modalities .We evaluated our algorithms on popular pedestrian behaviour dataset, JAAD, and have reached SOTA performance and passed the SOTA in metrics like Accuracy, AUC and F1-score. The advantages brought by different model design choices are investigated via extensive ablation studies.