2.4k citations · 12.3k across the 115 of their papers we have counts for
6 papers · 2 filters
Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps
Katie Z Luo, Xinshuo Weng, Yan Wang +5
Autonomous driving has traditionally relied heavily on costly and labor-intensive High Definition (HD) maps, hindering scalability. In contrast, Standard Definition (SD) maps are m…
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
Katie Z Luo, Zhenzhen Liu, Xiangyu Chen +7
Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences. Alt…
Pre-Training LiDAR-Based 3D Object Detectors Through Colorization
Tai-Yu Pan, Chenyang Ma, Tianle Chen +7
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train. In this work, we in…
Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features
Travis Zhang, Katie Luo, Cheng Perng Phoo +5
The rapid development of 3D object detection systems for self-driving cars has significantly improved accuracy. However, these systems struggle to generalize across diverse driving…
Unsupervised Adaptation from Repeated Traversals for Autonomous Driving
Yurong You, Cheng Perng Phoo, Katie Z Luo +5
For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment -- ideally without additional annotation efforts. One potential solu…
Unsupervised Out-of-Distribution Detection with Diffusion Inpainting
Zhenzhen Liu, Jin Peng Zhou, Yufan Wang +1
Unsupervised out-of-distribution detection (OOD) seeks to identify out-of-domain data by learning only from unlabeled in-domain data. We present a novel approach for this task - Li…