3 citations · 3 across the 9 of their papers we have counts for
10 papers
CrashDiffuser: VLM-Guided Collision Intent Reasoning for Fine-Grained Safety-Critical Traffic Scenario Generation
Shucheng Zhang, Yuang Zhang, Bingzhang Wang +3
Generating safety-critical scenarios is essential for evaluating autonomous driving systems. However, existing generators primarily focus on inducing collisions and offer limited c…
TAU-R1: Visual Language Model for Traffic Anomaly Understanding
Yuqiang Lin, Kehua Chen, Sam Lockyer +12
Traffic Anomaly Understanding (TAU) is important for traffic safety in Intelligent Transportation Systems. Recent vision-language models (VLMs) have shown strong capabilities in vi…
Pix2Fact: When Vision Is Not Enough -- Benchmarking Fine-Grained VQA with Web Verification on High-Resolution Real-World Scenes
Yifan Jiang, Cong Zhang, Bofei Zhang +4
Despite progress on general tasks, vision-language models (VLMs) still struggle with challenges that demand both fine-grained visual grounding and external knowledge, a synergy ove…
From Camera-Based Sensing to Reasoning: A Comprehensive Review Toward Proactive Vulnerable Road User Safety
Shucheng Zhang, Yan Shi, Bingzhang Wang +6
Ensuring the safety of vulnerable road users (VRUs), such as pedestrians and cyclists, remains a critical challenge, as conventional infrastructure-based measures are often insuffi…
Collaborative-Distilled Diffusion Models (CDDM) for Accelerated and Lightweight Trajectory Prediction
Bingzhang Wang, Kehua Chen, Yinhai Wang
Trajectory prediction is a fundamental task in Autonomous Vehicles (AVs) and Intelligent Transportation Systems (ITS), supporting efficient motion planning and real-time traffic sa…
Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Review
Muhammad Monjurul Karim, Yan Shi, Shucheng Zhang +3
Roadway safety and mobility remain critical challenges for modern transportation systems, demanding innovative analytical frameworks capable of addressing complex, dynamic, and het…
Independent Mobility GPT (IDM-GPT): A Self-Supervised Multi-Agent Large Language Model Framework for Customized Traffic Mobility Analysis Using Machine Learning Models
Fengze Yang, Xiaoyue Cathy Liu, Lingjiu Lu +2
With the urbanization process, an increasing number of sensors are being deployed in transportation systems, leading to an explosion of big data. To harness the power of this vast…
PreMixer: MLP-Based Pre-training Enhanced MLP-Mixers for Large-scale Traffic Forecasting
Tongtong Zhang, Zhiyong Cui, Bingzhang Wang +4
In urban computing, precise and swift forecasting of multivariate time series data from traffic networks is crucial. This data incorporates additional spatial contexts such as sens…
Traffic Performance GPT (TP-GPT): Real-Time Data Informed Intelligent ChatBot for Transportation Surveillance and Management
Bingzhang Wang, Zhiyu Cai, Muhammad Monjurul Karim +2
The digitization of traffic sensing infrastructure has significantly accumulated an extensive traffic data warehouse, which presents unprecedented challenges for transportation ana…
RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization
Hongyi Yao, Pu Li, Jian Cao +3
We introduce a Power-of-Two low-bit post-training quantization(PTQ) method for deep neural network that meets hardware requirements and does not call for long-time retraining. Powe…