123 citations · 139 across the 3 of their papers we have counts for
3 papers
cs.RO2022★ 9 cited
Driving in Real Life with Inverse Reinforcement Learning
Tung Phan-Minh, Forbes Howington, Ting-Sheng Chu +10
In this paper, we introduce the first learning-based planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a d…
cs.HC2020★ 7 cited
VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection
Liang Gou, Lincan Zou, Nanxiang Li +4
Traffic light detection is crucial for environment perception and decision-making in autonomous driving. State-of-the-art detectors are built upon deep Convolutional Neural Network…
stat.ML2020★ 123 cited
Improve Unsupervised Domain Adaptation with Mixup Training
Shen Yan, Huan Song, Nanxiang Li +2
Unsupervised domain adaptation studies the problem of utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain. Recent…