67 citations · 90 across the 14 of their papers we have counts for
11 papers · 1 filter
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…
Deformba: Vision State Space Model with Adaptive State Fusion
Hongyu Ke, Jack Morris, Yongkang Liu +4
State Space Models (SSMs) have emerged as a powerful and efficient alternative to Transformers, demonstrating linear-time complexity and exceptional sequence modeling capabilities.…
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…
MamBEV: Enabling State Space Models to Learn Birds-Eye-View Representations
Hongyu Ke, Jack Morris, Kentaro Oguchi +4
3D visual perception tasks, such as 3D detection from multi-camera images, are essential components of autonomous driving and assistance systems. However, designing computationally…
Cooperverse: A Mobile-Edge-Cloud Framework for Universal Cooperative Perception with Mixed Connectivity and Automation
Zhengwei Bai, Guoyuan Wu, Matthew J. Barth +3
Cooperative perception (CP) is attracting increasing attention and is regarded as the core foundation to support cooperative driving automation, a potential key solution to address…
VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detection
Zhengwei Bai, Guoyuan Wu, Matthew J. Barth +3
Utilizing the latest advances in Artificial Intelligence (AI), the computer vision community is now witnessing an unprecedented evolution in all kinds of perception tasks, particul…
A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation
Zhengwei Bai, Guoyuan Wu, Matthew J. Barth +4
Perceiving the environment is one of the most fundamental keys to enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing the s…
PillarGrid: Deep Learning-based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDAR
Zhengwei Bai, Guoyuan Wu, Matthew J. Barth +3
3D object detection plays a fundamental role in enabling autonomous driving, which is regarded as the significant key to unlocking the bottleneck of contemporary transportation sys…
Spatiotemporal Transformer Attention Network for 3D Voxel Level Joint Segmentation and Motion Prediction in Point Cloud
Zhensong Wei, Xuewei Qi, Zhengwei Bai +6
Environment perception including detection, classification, tracking, and motion prediction are key enablers for automated driving systems and intelligent transportation applicatio…
Cyber Mobility Mirror: A Deep Learning-based Real-World Object Perception Platform Using Roadside LiDAR
Zhengwei Bai, Saswat Priyadarshi Nayak, Xuanpeng Zhao +6
Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for the next-generation transportation systems. How…
Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey
Zhengwei Bai, Guoyuan Wu, Xuewei Qi +3
Object detection plays a fundamental role in enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing safety, mobility, and sust…