activity
20122023
most citedCharacterizing Driving Styles with Deep Learning

91 citations · 146 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV202337 cited

Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Simian Luo, Yiqin Tan, Longbo Huang +2

Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and lea…

cs.CV20232 cited

ReIDTrack: Multi-Object Track and Segmentation Without Motion

Kaer Huang, Bingchuan Sun, Feng Chen +5

In recent years, dominant Multi-object tracking (MOT) and segmentation (MOTS) methods mainly follow the tracking-by-detection paradigm. Transformer-based end-to-end (E2E) solutions…

cs.RO2022

Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning

Gaurav Bagwe, Jian Li, Xiaoyong Yuan +1

Despite the success of AI-enabled onboard perception, on-ramp merging has been one of the main challenges for autonomous driving. Due to limited sensing range of onboard sensors, a…

cs.CV2016

Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene

Keyu Lu, Jian Li, Xiangjing An +1

Vision-based object detection is one of the fundamental functions in numerous traffic scene applications such as self-driving vehicle systems and advance driver assistance systems…

cs.AI201691 cited

Characterizing Driving Styles with Deep Learning

Weishan Dong, Jian Li, Renjie Yao +3

Characterizing driving styles of human drivers using vehicle sensor data, e.g., GPS, is an interesting research problem and an important real-world requirement from automotive indu…

cs.LG201216 cited

A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound

Shusen Wang, Zhihua Zhang, Jian Li

The CUR matrix decomposition is an important extension of Nyström approximation to a general matrix. It approximates any data matrix in terms of a small number of its columns and r…