MTF‑Net is a Multi‑Modal Temporal Feature Fusion Network that jointly models kinematic, appearance, and contextual cues for pedestrian intention prediction. It fuses four modalities—bounding‑box dynamics, human pose keypoints, local context, and scene‑level semantics—within a recurrent framework enhanced by gated linear units (GLUs) and an attention‑guided fusion head. Evaluations on the PIE and JAAD benchmarks show that MTF‑Net outperforms recent transformer‑ and graph‑based models, achieving up to 0.95 AUC on PIE and 0.94 AUC on JAAD while maintaining real‑time performance.
By Md Mahfuzur Rahman, Pengzhan Zhou, A. F. M. Abdun Noor, Md Imam Ahasan, Md Mustafizur Rahman, Fang Qu
arXiv:2609.23507v1 Announce Type: new
Abstract: The increasing reliance on mobile phones has made phone-induced pedestrian distraction increasingly prevalent. Activities such as texting, watching vid...
By Yuanzhe Li, Hounian Liu, Xiaotong Chang, Yidi Huang
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
arXiv:2606. 18824v1 Announce Type: cross Abstract: Pedestrian trajectory prediction from an ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intention of the pedestrian.
By Yuxuan Xie, Nicolas Pugeault, Chongfeng Wei, Hubert P. H. Shum, Edmond S. L. Ho
The paper introduces STC-MGNet, a spatial‑temporal‑channel attention‑enhanced multimodal graph network designed to predict pedestrian crossing intentions. It employs a pose graph encoder with three attention branches that focus on skeletal joints, observation frames, and feature channels, and integrates visual and ego‑vehicle motion data through a multi‑stage cross‑modal channel modulation mechanism. Experiments on JAADbeh, JAADall, and PIE datasets show accuracy rates of 70%, 87%, and 88%, surpassing several baseline methods.
By Yuanzhe Li, Meng Wang
arXiv:2606. 01277v1 Announce Type: cross Abstract: Current end-to-end autonomous driving systems predominantly rely on frame-based sensors, which suffer from inherent perception latency and motion blur during highly dynamic encounters, specifically sudden pedestrian crossings.
By Oskar Natan, Andi Dharmawan, Aufaclav Zatu Kusuma Frisky, Jazi Eko Istiyanto, Jun Miura
arXiv:2603. 28251v3 Announce Type: replace-cross Abstract: Drivers' visual attention provides critical cues for anticipating latent hazards and directly shapes decision-making and control maneuvers, where its absence can compromise traffic safety.
By Weimin Liu, Qingkun Li, Jiyuan Qiu, Wenjun Wang, Joshua H. Meng
The paper presents an end‑to‑end system that converts driving footage into dynamic vision sensor (DVS) event streams, augments training with simulated DVS data, and trains a convolutional spiking neural network (Conv‑SNN) to classify pedestrian crossing intent as crossing or non‑crossing. The Conv‑SNN, trained with a class‑balanced loss and surrogate‑gradient learning, achieves high accuracy and F1 scores on JAAD and CARLA DVS datasets, outperforming or matching prior frame‑based methods while operating on sparse temporal representations. The study details architectural choices, neuron dynamics, and training protocols, and provides a convergence analysis and domain‑transfer evaluation.
By Henok Teklu, Mustafa Sakhai, Maciej Wielgosz, Matej Mertik
arXiv:2510. 13774v2 Announce Type: replace Abstract: Forecasting urban phenomena such as housing prices and public health indicators requires the effective integration of various geospatial data.
By Dominik J. M\"uhlematter, Lin Che, Ye Hong, Martin Raubal, Nina Wiedemann
arXiv:2608.06205v2 Announce Type: replace
Abstract: Multispectral object detection combines visible and thermal imagery to improve perception under challenging illumination and environmental conditio...
By Nima Hatami, Karim Faez, Saeed Sharifian, Hamidreza Amindavar
arXiv:2609.09881v1 Announce Type: new
Abstract: Semantic segmentation for autonomous driving requires reliable detection of vulnerable road users (VRUs) despite heavy class imbalance. We introduce CL...
By Toomas Tahves, Mauro Bellone, Raivo Sell
COMPASS is a completion-and-fusion framework designed for multimodal human activity recognition (HAR) and human pose estimation (HPE) when some modalities are missing at inference. It assigns each modality to a fixed slot, filling it with either an observed representation or a completion inferred from available inputs, and uses fusion‑matched supervision to train completions against real targets at the readout level. Experiments on XRF55 and MM‑Fi datasets show that COMPASS outperforms strong baselines and alternative matching strategies for both HAR and HPE.
By Hao Wang, Yanyu Qian, Pengcheng Weng, Zixuan Xia, William Dan, Yangxin Xu, Fei Wang