arXiv:2606. 09142v1 Announce Type: cross Abstract: Egocentric vision offers a first-person view of human perception and decision making, yet its potential for traffic-safety prediction remains underexplored.
By Danya Li, Xiang Su, Yan Feng, Rico Krueger
TrajFusionNet+ is a transformer-based model that predicts pedestrian crossing intention by fusing sequential trajectory data, visual trajectory overlays, and graph-based scene context. It extends the earlier TrajFusionNet with three attention modules—Sequence, Visual, and Graph—to capture temporal, visual, and relational cues. The model outperforms state‑of‑the‑art methods on the PIE and JAAD datasets and shows better generalization under a joint‑training, separate‑evaluation protocol.
By Fran\c{c}ois G. Landry, Moulay A. Akhloufi
The paper presents a decoupled framework for sim-to-real traffic scene understanding, separating semantic fact extraction from caption generation. It uses a frozen V-JEPA encoder for predictive scene representations and a lightweight Llama-based predictor for VQA, followed by a training-free structured refinement that leverages statistical priors, inter-question relationships, and temporal consistency. The refined facts are then fed to Qwen3-VL-8B to produce pedestrian and vehicle descriptions, achieving top performance on the 2026 AI City Challenge Track 2 benchmark with 87.09% VQA accuracy and an overall S2 score of 60.0853.
By Nguyen Hoai Thuong Bui, Thanh Nguyen Vo, Trinh Tra Giang Nguyen, Ha Duc Bui
arXiv:2603. 09731v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly considered as a foundation for embodied agents, yet it remains unclear whether they can reliably reason about the long-term physical consequences of actions from an egocentric viewpoint.
By Chengjun Yu, Xuhan Zhu, Chaoqun Du, Pengfei Yu, Wei Zhai, Yang Cao, Zheng-Jun Zha
arXiv:2608.28762v1 Announce Type: new
Abstract: Recent advances in visual question answering (VQA) and multimodal large language models (MLLMs) have enabled natural-language reasoning over traffic sc...
By Shaozu Ding, Linan Song, Dajiang Suo
arXiv:2511.20022v3 Announce Type: replace-cross
Abstract: Recent advancements in multimodal large language models (MLLMs) have shown strong understanding of driving scenes, drawing interest in their...
By Seungjun Yu, Seonho Lee, Namho Kim, Jaeyo Shin, Junsung Park, Wonjeong Ryu, Raehyuk Jung, Hyunjung Shim