4 citations · 6 across the 4 of their papers we have counts for
5 papers
Towards Collaborative Joint Perception and Prediction: Framework, Baseline Evaluation, and Deployment Perspectives
Lei Wan, Hannan Ejaz Keen, Alexey Vinel
Connected Autonomous Vehicles (CAVs) increasingly exploit Vehicle-to-Everything (V2X) communication to exchange multi-source sensor information, enabling advanced Collaborative Per…
VALISENS: A Validated Innovative Multi-Sensor System for Cooperative Automated Driving
Lei Wan, Prabesh Gupta, Andreas Eich +4
Reliable perception remains a key challenge for Connected Automated Vehicles (CAVs) in complex real-world environments, where varying lighting conditions and adverse weather degrad…
Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective
Lei Wan, Jianxin Zhao, Andreas Wiedholz +7
The effectiveness of autonomous vehicles relies on reliable perception capabilities. Despite significant advancements in artificial intelligence and sensor fusion technologies, cur…
R-LiViT: A LiDAR-Visual-Thermal Dataset Enabling Vulnerable Road User Focused Roadside Perception
Jonas Mirlach, Lei Wan, Andreas Wiedholz +2
In autonomous driving, the integration of roadside perception systems is essential for overcoming occlusion challenges and enhancing the safety of Vulnerable Road Users(VRUs). Whil…
The Components of Collaborative Joint Perception and Prediction -- A Conceptual Framework
Lei Wan, Hannan Ejaz Keen, Alexey Vinel
Connected Autonomous Vehicles (CAVs) benefit from Vehicle-to-Everything (V2X) communication, which enables the exchange of sensor data to achieve Collaborative Perception (CP). To…