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20222026
most citedHeterogeneous Trajectory Forecasting via Risk and Scene Graph Learning

31 citations · 58 across the 21 of their papers we have counts for

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13 papers · 1 filter

cs.CV2026

4D-WAM: 4D Consistent World Modeling for Autonomous Driving

Jiacheng Fu, Yibo Yuan, Meng Tian +8

Emerging World-Action Models (WAMs) have demonstrated promising performance in autonomous driving by jointly modeling future driving scene evolution and trajectory planning. Howeve…

cs.CV2026

SUV: Future Scene Understanding as Video Generation for End-to-End Driving

Yibo Yuan, Jiacheng Fu, Jiangtong Zhu +8

End-to-end driving requires a coherent understanding of future scenes, yet existing methods model these scenes using task-specific heads and output formats, with limited scalabilit…

cs.CV2026

MetaDAT: Generalizable Trajectory Prediction via Meta Pre-training and Data-Adaptive Test-Time Updating

Yuning Wang, Pu Zhang, Yuan He +2

Existing trajectory prediction methods exhibit significant performance degradation under distribution shifts during test time. Although test-time training techniques have been expl…

cs.CV2025

IC-Mapper: Instance-Centric Spatio-Temporal Modeling for Online Vectorized Map Construction

Jiangtong Zhu, Zhao Yang, Yinan Shi +2

Online vector map construction based on visual data can bypass the processes of data collection, post-processing, and manual annotation required by traditional map construction, wh…

cs.CV2024

HeightMapNet: Explicit Height Modeling for End-to-End HD Map Learning

Wenzhao Qiu, Shanmin Pang, Hao zhang +2

Recent advances in high-definition (HD) map construction from surround-view images have highlighted their cost-effectiveness in deployment. However, prevailing techniques often fal…

cs.CV2024

Gating Syn-to-Real Knowledge for Pedestrian Crossing Prediction in Safe Driving

Jie Bai, Jianwu Fang, Yisheng Lv +3

Pedestrian Crossing Prediction (PCP) in driving scenes plays a critical role in ensuring the safe operation of intelligent vehicles. Due to the limited observations of pedestrian c…

cs.CV2024★ 1 cited

Abductive Ego-View Accident Video Understanding for Safe Driving Perception

Jianwu Fang, Lei-lei Li, Junfei Zhou +5

We present MM-AU, a novel dataset for Multi-Modal Accident video Understanding. MM-AU contains 11,727 in-the-wild ego-view accident videos, each with temporally aligned text descri…

cs.CV2023

Gated Driver Attention Predictor

Tianci Zhao, Xue Bai, Jianwu Fang +1

Driver attention prediction implies the intention understanding of where the driver intends to go and what object the driver concerned about, which commonly provides a driving task…

cs.CV2023★ 2 cited

Enhancing Mapless Trajectory Prediction through Knowledge Distillation

Yuning Wang, Pu Zhang, Lei Bai +1

Scene information plays a crucial role in trajectory forecasting systems for autonomous driving by providing semantic clues and constraints on potential future paths of traffic age…

cs.CV2023

FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction

Yuning Wang, Pu Zhang, Lei Bai +1

Predicting the future trajectories of the traffic agents is a gordian technique in autonomous driving. However, trajectory prediction suffers from data imbalance in the prevalent d…

cs.CV2022★ 10 cited

Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark

Jianwu Fang, Lei-Lei Li, Kuan Yang +3

Traffic accident prediction in driving videos aims to provide an early warning of the accident occurrence, and supports the decision making of safe driving systems. Previous works…

cs.CV2022★ 31 cited

Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning

Jianwu Fang, Chen Zhu, Pu Zhang +2

Heterogeneous trajectory forecasting is critical for intelligent transportation systems, but it is challenging because of the difficulty of modeling the complex interaction relatio…

cs.CV2022★ 14 cited

Behavioral Intention Prediction in Driving Scenes: A Survey

Jianwu Fang, Fan Wang, Jianru Xue +1

In the driving scene, the road agents usually conduct frequent interactions and intention understanding of the surroundings. Ego-agent (each road agent itself) predicts what behavi…