3 citations · 9 across the 11 of their papers we have counts for
12 papers
When the Score Becomes the Target: Rethinking Metric Validity in Autonomous Driving
Morui Zhu, Deyuan Qu, Qi Chen +2
Driving benchmark scores are increasingly used not only for evaluation but also as optimization targets. This raises a fundamental question: do score gains remain reliable evidence…
LANTERN: A Closed-Loop Benchmark for VLM-Based Cooperative Driving with Temporally Grounded Warnings
Yongshuo Liu, Xu Gao, Morui Zhu +5
We present LANTERN, a closed-loop benchmark for temporally grounded cooperative warnings. LANTERN separates warning onset, hazard onset, warning termination, and post-hazard recove…
FleetAgent: Teleoperation Assistant for Autonomous Fleets via Vectorized V2N Messages
Juntong Peng, Qi Chen, Deyuan Qu +3
Large-scale autonomous fleets rely on teleoperation to resolve rare failures, yet streaming raw sensor data from many vehicles is costly, and remote operators can only monitor a li…
Localization-Guided Foreground Augmentation in Autonomous Driving
Jiawei Yong, Deyuan Qu, Qi Chen +2
Autonomous driving systems often degrade under adverse visibility conditions-such as rain, nighttime, or snow-where online scene geometry (e.g., lane dividers, road boundaries, and…
CooperDrive: Enhancing Driving Decisions Through Cooperative Perception
Deyuan Qu, Qi Chen, Takayuki Shimizu +1
Autonomous vehicles equipped with robust onboard perception, localization, and planning still face limitations in occlusion and non-line-of-sight (NLOS) scenarios, where delayed re…
From Features to Reference Points: Lightweight and Adaptive Fusion for Cooperative Autonomous Driving
Yongqi Zhu, Morui Zhu, Qi Chen +4
We present RefPtsFusion, a lightweight and interpretable framework for cooperative autonomous driving. Instead of sharing large feature maps or query embeddings, vehicles exchange…
M3CAD: Towards Generic Cooperative Autonomous Driving Benchmark
Morui Zhu, Yongqi Zhu, Yihao Zhu +4
We introduce MCAD, a comprehensive benchmark designed to advance research in generic cooperative autonomous driving. MCAD comprises 204 sequences with 30,000 frames. Each s…
HEAD: A Bandwidth-Efficient Cooperative Perception Approach for Heterogeneous Connected and Autonomous Vehicles
Deyuan Qu, Qi Chen, Yongqi Zhu +4
In cooperative perception studies, there is often a trade-off between communication bandwidth and perception performance. While current feature fusion solutions are known for their…
Real-time Motion Planning for autonomous vehicles in dynamic environments
Mohammad Dehghani Tezerjani, Dominic Carrillo, Deyuan Qu +3
Recent advancements in self-driving car technologies have enabled them to navigate autonomously through various environments. However, one of the critical challenges in autonomous…
VirtualPainting: Addressing Sparsity with Virtual Points and Distance-Aware Data Augmentation for 3D Object Detection
Sudip Dhakal, Dominic Carrillo, Deyuan Qu +3
In recent times, there has been a notable surge in multimodal approaches that decorates raw LiDAR point clouds with camera-derived features to improve object detection performance.…
Facial Emotion Recognition using CNN in PyTorch
Deyuan Qu, Sudip Dhakal, Dominic Carrillo
In this project, we have implemented a model to recognize real-time facial emotions given the camera images. Current approaches would read all data and input it into their model, w…
SiCP: Simultaneous Individual and Cooperative Perception for 3D Object Detection in Connected and Automated Vehicles
Deyuan Qu, Qi Chen, Tianyu Bai +5
Cooperative perception for connected and automated vehicles is traditionally achieved through the fusion of feature maps from two or more vehicles. However, the absence of feature…