13 citations · 16 across the 11 of their papers we have counts for
13 papers
EvoAD: Self-Evolving Autonomous Driving via Latent World-Model Guided Post-hoc Reflection
Junjie Yang, Qiming Zhang, Yuyan Li +12
End-to-end autonomous driving systems have achieved rapid progress by learning unified policies that map sensor observations directly to driving trajectories. However, once deploye…
From Likelihood to Utility: Quality-Axis Diffusion for End-to-End Autonomous Driving
Junjie Yang, Ziyi Shi, Shuqi Shen +3
Diffusion models have become a dominant paradigm for end-to-end autonomous driving, yet existing planners organize trajectory generation along the Gaussian-noise axis, treating dri…
EditWM: Event-Decomposed World Modeling with Incremental Correction for End-to-End Autonomous Driving
Junjie Yang, Qingwei Zeng, Youyou Li +5
World models support autonomous driving by predicting the scene evolution associated with candidate trajectories. Driving dynamics differ in predictability, motivating a distinctio…
Energy-Aware Wind-Resilient Routing for Truck-Assisted Multi-UAV Delivery under Wind Uncertainty
Tianshun Li, Yanggang Sheng, Hongliang Lu +3
Energy feasibility under wind uncertainty is a critical safety issue for low-altitude air-ground delivery. In truck-UAV systems, UAVs complete assigned deliveries and safely return…
Diffusion-based 4D Trajectory Prediction and Distributed Control for UAV Swarms
Tianshun Li, Hongliang Lu, Haoang Li +1
Accurate 4D trajectory prediction and closed-loop tracking are essential for Unmanned Aerial Vehicle (UAV) swarms to achieve safe and efficient operations in complex low-altitude e…
Robust Energy-Aware Routing for Air-Ground Cooperative Multi-UAV Delivery in Wind-Uncertain Environments
Tianshun Li, Hongliang Lu, Yanggang Sheng +3
Ensuring energy feasibility under wind uncertainty is critical for the safety and reliability of UAV delivery missions. In realistic truck-drone logistics systems, UAVs must delive…
Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensing
Xusen Guo, Mingxing Peng, Hongliang Lu +3
Participatory urban sensing leverages human mobility for large-scale urban data collection, yet existing methods typically rely on centralized optimization and assume homogeneous p…
Enhancing Urban Sensing Utility with Sensor-enabled Vehicles and Easily Accessible Data
Hui Zhong, Qing-Long Lu, Qiming Zhang +2
Urban sensing is essential for the development of smart cities, enabling monitoring, computing, and decision-making for urban management.Thanks to the advent of vehicle technologie…
Deployment-friendly Lane-changing Intention Prediction Powered by Brain-inspired Spiking Neural Networks
Shuqi Shen, Junjie Yang, Hui Zhong +3
Accurate and real-time prediction of surrounding vehicles' lane-changing intentions is a critical challenge in deploying safe and efficient autonomous driving systems in open-world…
High-resolution urban air pollution and thermal comfort mapping: an application of drive mobile sensing platform for smart city services
Hui Zhong, Hongliang Lu, Ting Gan +2
Air pollutant exposure exhibits significant spatial and temporal variability, with localized hotspots, particularly in traffic microenvironments, posing health risks to commuters.…
Automating Traffic Model Enhancement with AI Research Agent
Xusen Guo, Xinxi Yang, Mingxing Peng +3
Developing efficient traffic models is crucial for optimizing modern transportation systems. However, current modeling approaches remain labor-intensive and prone to human errors d…
EcoFollower: An Environment-Friendly Car Following Model Considering Fuel Consumption
Hui Zhong, Xianda Chen, PakHin Tiu +2
To alleviate energy shortages and environmental impacts caused by transportation, this study introduces EcoFollower, a novel eco-car-following model developed using reinforcement l…
BEVGPT: Generative Pre-trained Large Model for Autonomous Driving Prediction, Decision-Making, and Planning
Pengqin Wang, Meixin Zhu, Hongliang Lu +5
Prediction, decision-making, and motion planning are essential for autonomous driving. In most contemporary works, they are considered as individual modules or combined into a mult…