activity
20242026
most citedDelving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments

Feng Liu, Kejia Li, Zhiwei Yang +5

Although Global Navigation Satellite Systems (GNSS) provide a general solution for bike tracking outdoors, there still exist complex riding environments where only inertial navigat…

cs.AI2026

A Progressive Visual-Logic-Aligned Framework for Ride-Hailing Adjudication

Weiming Wu, Zi-Jian Cheng, Jie Meng +5

The efficient adjudication of responsibility disputes is pivotal for maintaining marketplace fairness. However, the exponential surge in ride-hailing volume renders manual review i…

cs.CV2025

Accident Anticipation via Temporal Occurrence Prediction

Tianhao Zhao, Yiyang Zou, Zihao Mao +7

Accident anticipation aims to predict potential collisions in an online manner, enabling timely alerts to enhance road safety. Existing methods typically predict frame-level risk s…

cs.AI2024

Exploring the Interplay Between Video Generation and World Models in Autonomous Driving: A Survey

Ao Fu, Yi Zhou, Tao Zhou +5

World models and video generation are pivotal technologies in the domain of autonomous driving, each playing a critical role in enhancing the robustness and reliability of autonomo…

cs.CV2024★ 2 cited

Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives

Sheng Luo, Wei Chen, Wanxin Tian +12

Foundation models have indeed made a profound impact on various fields, emerging as pivotal components that significantly shape the capabilities of intelligent systems. In the cont…