5 papers
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…
Mind the Hitch: Dynamic Calibration and Articulated Perception for Autonomous Trucks
Morui Zhu, Yongqi Zhu, Song Fu +1
Autonomous trucking poses unique challenges due to articulated tractor-trailer geometry, and time-varying sensor poses caused by the fifth-wheel joint and trailer flex. Existing pe…
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…