8 citations · 9 across the 5 of their papers we have counts for
5 papers
From Driving Videos to Simulatable Scenarios
Alexandre Levy, Ernest Valveny Llobet, Antonio Manuel López
Autonomous vehicles (AVs) face driving scenarios ranging from routine traffic to rare events. To assess safety it is crucial to reproduce these scenarios in a controllable, repeata…
ARCANE-PedSynth: Synthetic Multi-Pedestrian Datasets with Behavioural Crossing Annotations
Muhammad Naveed Riaz, Maciej Wielgosz, Antonio M. López Peña
We present ARCANE-PedSynth, an open-source CARLA-based software framework for generating synthetic multi-pedestrian datasets with dense behavioural annotations for pedestrian cross…
GTransPDM: A Graph-embedded Transformer with Positional Decoupling for Pedestrian Crossing Intention Prediction
Chen Xie, Ciyun Lin, Xiaoyu Zheng +2
Understanding and predicting pedestrian crossing behavioral intention is crucial for the driving safety of autonomous vehicles. Nonetheless, challenges emerge when using promising…
Divide, Ensemble and Conquer: The Last Mile on Unsupervised Domain Adaptation for Semantic Segmentation
Tao Lian, Jose L. Gómez, Antonio M. López
The last mile of unsupervised domain adaptation (UDA) for semantic segmentation is the challenge of solving the syn-to-real domain gap. Recent UDA methods have progressed significa…
Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction
Muhammad Naveed Riaz, Maciej Wielgosz, Abel Garcia Romera +1
Pedestrian intention prediction is crucial for autonomous driving. In particular, knowing if pedestrians are going to cross in front of the ego-vehicle is core to performing safe a…