45 citations · 64 across the 11 of their papers we have counts for
11 papers
A Vision-Language-Action Model with Visual Prompt for OFF-Road Autonomous Driving
Liangdong Zhang, Yiming Nie, Haoyang Li +6
Efficient trajectory planning in off-road terrains presents a formidable challenge for autonomous vehicles, often necessitating complex multi-step pipelines. However, traditional a…
Advancing Off-Road Autonomous Driving: The Large-Scale ORAD-3D Dataset and Comprehensive Benchmarks
Chen Min, Jilin Mei, Heng Zhai +12
A major bottleneck in off-road autonomous driving research lies in the scarcity of large-scale, high-quality datasets and benchmarks. To bridge this gap, we present ORAD-3D, which,…
Autonomous Driving in Unstructured Environments: How Far Have We Come?
Chen Min, Shubin Si, Xu Wang +14
Research on autonomous driving in unstructured outdoor environments is less advanced than in structured urban settings due to challenges like environmental diversities and scene co…
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving
Chen Min, Dawei Zhao, Liang Xiao +10
Vision-centric autonomous driving has recently raised wide attention due to its lower cost. Pre-training is essential for extracting a universal representation. However, current vi…
UniWorld: Autonomous Driving Pre-training via World Models
Chen Min, Dawei Zhao, Liang Xiao +2
In this paper, we draw inspiration from Alberto Elfes' pioneering work in 1989, where he introduced the concept of the occupancy grid as World Models for robots. We imbue the robot…
Contrastive Label Disambiguation for Self-Supervised Terrain Traversability Learning in Off-Road Environments
Hanzhang Xue, Xiaochang Hu, Rui Xie +4
Discriminating the traversability of terrains is a crucial task for autonomous driving in off-road environments. However, it is challenging due to the diverse, ambiguous, and platf…
UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction for Autonomous Driving
Chen Min, Liang Xiao, Dawei Zhao +2
Multi-camera 3D perception has emerged as a prominent research field in autonomous driving, offering a viable and cost-effective alternative to LiDAR-based solutions. The existing…
ORFD: A Dataset and Benchmark for Off-Road Freespace Detection
Chen Min, Weizhong Jiang, Dawei Zhao +4
Freespace detection is an essential component of autonomous driving technology and plays an important role in trajectory planning. In the last decade, deep learning-based free spac…
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders
Chen Min, Xinli Xu, Dawei Zhao +3
Current perception models in autonomous driving heavily rely on large-scale labelled 3D data, which is both costly and time-consuming to annotate. This work proposes a solution to…
Trajectory Prediction for Autonomous Driving with Topometric Map
Jiaolong Xu, Liang Xiao, Dawei Zhao +2
State-of-the-art autonomous driving systems rely on high definition (HD) maps for localization and navigation. However, building and maintaining HD maps is time-consuming and expen…
Attentional Graph Neural Network for Parking-slot Detection
Chen Min, Jiaolong Xu, Liang Xiao +3
Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection. However, very few existing methods explicitly take into account learning…