1 citations · 1 across the 2 of their papers we have counts for
6 papers
Diff-Net: Image Feature Difference based High-Definition Map Change Detection for Autonomous Driving
Lei He, Shengjie Jiang, Xiaoqing Liang +2
Up-to-date High-Definition (HD) maps are essential for self-driving cars. To achieve constantly updated HD maps, we present a deep neural network (DNN), Diff-Net, to detect changes…
Exploring Imitation Learning for Autonomous Driving with Feedback Synthesizer and Differentiable Rasterization
Jinyun Zhou, Rui Wang, Xu Liu +5
We present a learning-based planner that aims to robustly drive a vehicle by mimicking human drivers' driving behavior. We leverage a mid-to-mid approach that allows us to manipula…
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images
Lei He, Jiwen Lu, Guanghui Wang +2
Depth estimation and semantic segmentation play essential roles in scene understanding. The state-of-the-art methods employ multi-task learning to simultaneously learn models for t…
TDR-OBCA: A Reliable Planner for Autonomous Driving in Free-Space Environment
Runxin He, Jinyun Zhou, Shu Jiang +6
This paper presents an optimization-based collision avoidance trajectory generation method for autonomous driving in free-space environments, with enhanced robustness, driving comf…
DA4AD: End-to-End Deep Attention-based Visual Localization for Autonomous Driving
Yao Zhou, Guowei Wan, Shenhua Hou +4
We present a visual localization framework based on novel deep attention aware features for autonomous driving that achieves centimeter level localization accuracy. Conventional ap…
DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
Weixin Lu, Guowei Wan, Yao Zhou +3
We present DeepICP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric meth…