9 citations · 18 across the 14 of their papers we have counts for
16 papers
4DLidarOpen: An Open 4D FMCW Lidar Dataset for Motion-Aware Autonomous Driving
Kane Qian, Xin Zhao, Yining Shi +7
We present 4DLidarOpen, a large-scale open multi-modal dataset for autonomous driving, centered on 4D frequency-modulated continuous-wave (FMCW) Lidar sensing. Unlike conventional…
COME: Adding Scene-Centric Forecasting Control to Occupancy World Model
Yining Shi, Kun Jiang, Qiang Meng +6
World models are critical for autonomous driving to simulate environmental dynamics and generate synthetic data. Existing methods struggle to disentangle ego-vehicle motion (perspe…
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving
Kangan Qian, Sicong Jiang, Yang Zhong +18
Vision-Language Models (VLMs) show promise for autonomous driving, yet their struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate…
CleanMAP: Distilling Multimodal LLMs for Confidence-Driven Crowdsourced HD Map Updates
Ankit Kumar Shaw, Kun Jiang, Tuopu Wen +5
The rapid growth of intelligent connected vehicles (ICVs) and integrated vehicle-road-cloud systems has increased the demand for accurate, real-time HD map updates. However, ensuri…
POD: Predictive Object Detection with Single-Frame FMCW LiDAR Point Cloud
Yining Shi, Kun Jiang, Xin Zhao +5
LiDAR-based 3D object detection is a fundamental task in the field of autonomous driving. This paper explores the unique advantage of Frequency Modulated Continuous Wave (FMCW) LiD…
FASIONAD++ : Integrating High-Level Instruction and Information Bottleneck in FAt-Slow fusION Systems for Enhanced Safety in Autonomous Driving with Adaptive Feedback
Kangan Qian, Ziang Luo, Sicong Jiang +16
Ensuring safe, comfortable, and efficient planning is crucial for autonomous driving systems. While end-to-end models trained on large datasets perform well in standard driving sce…
LEGO-Motion: Learning-Enhanced Grids with Occupancy Instance Modeling for Class-Agnostic Motion Prediction
Kangan Qian, Jinyu Miao, Ziang Luo +7
Accurate and reliable spatial and motion information plays a pivotal role in autonomous driving systems. However, object-level perception models struggle with handling open scenari…
How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic
Kangan Qian, Jinyu Miao, Xinyu Jiao +6
Understanding the spatial dynamics of cars within urban systems is essential for optimizing infrastructure management and resource allocation. Recent empirical approaches for analy…
FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback
Kangan Qian, Zhikun Ma, Yangfan He +13
Ensuring safe, comfortable, and efficient navigation is a critical goal for autonomous driving systems. While end-to-end models trained on large-scale datasets excel in common driv…
PanoSSC: Exploring Monocular Panoptic 3D Scene Reconstruction for Autonomous Driving
Yining Shi, Jiusi Li, Kun Jiang +4
Vision-centric occupancy networks, which represent the surrounding environment with uniform voxels with semantics, have become a new trend for safe driving of camera-only autonomou…
EFFOcc: Learning Efficient Occupancy Networks from Minimal Labels for Autonomous Driving
Yining Shi, Kun Jiang, Jinyu Miao +8
3D occupancy prediction (3DOcc) is a rapidly rising and challenging perception task in the field of autonomous driving. Existing 3D occupancy networks (OccNets) are both computatio…
Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review
Yining Shi, Kun Jiang, Jiusi Li +5
Grid-centric perception is a crucial field for mobile robot perception and navigation. Nonetheless, grid-centric perception is less prevalent than object-centric perception as auto…
StreamingFlow: Streaming Occupancy Forecasting with Asynchronous Multi-modal Data Streams via Neural Ordinary Differential Equation
Yining Shi, Kun Jiang, Ke Wang +4
Predicting the future occupancy states of the surrounding environment is a vital task for autonomous driving. However, current best-performing single-modality methods or multi-moda…
Map Container: A Map-based Framework for Cooperative Perception
Kun Jiang, Yining Shi, Benny Wijaya +4
The idea of cooperative perception is to benefit from shared perception data between multiple vehicles and overcome the limitations of on-board sensors on single vehicle. However,…
Bridging the View Disparity Between Radar and Camera Features for Multi-modal Fusion 3D Object Detection
Taohua Zhou, Yining Shi, Junjie Chen +3
Environmental perception with the multi-modal fusion of radar and camera is crucial in autonomous driving to increase accuracy, completeness, and robustness. This paper focuses on…
SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and Tracking
Yining Shi, Jingyan Shen, Yifan Sun +5
Detection and tracking of moving objects is an essential component in environmental perception for autonomous driving. In the flourishing field of multi-view 3D camera-based detect…