4 papers
VLAD: A VLM-Augmented Autonomous Driving Framework with Hierarchical Planning and Interpretable Decision Process
Cristian Gariboldi, Hayato Tokida, Ken Kinjo +2
Recent advancements in open-source Visual Language Models (VLMs) such as LLaVA, Qwen-VL, and Llama have catalyzed extensive research on their integration with diverse systems. The…
Sparse Prototype Network for Explainable Pedestrian Behavior Prediction
Yan Feng, Alexander Carballo, Kazuya Takeda
Predicting pedestrian behavior is challenging yet crucial for applications such as autonomous driving and smart city. Recent deep learning models have achieved remarkable performan…
MulCPred: Learning Multi-modal Concepts for Explainable Pedestrian Action Prediction
Yan Feng, Alexander Carballo, Keisuke Fujii +3
Pedestrian action prediction is of great significance for many applications such as autonomous driving. However, state-of-the-art methods lack explainability to make trustworthy pr…
DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding
Yingjie Niu, Ming Ding, Keisuke Fujii +3
Traffic accidents frequently lead to fatal injuries, contributing to over 50 million deaths until 2023. To mitigate driving hazards and ensure personal safety, it is crucial to ass…