Collaborative perception (CP) enables connected vehicles to see beyond their own sensors but makes them dependent on messages they cannot independently verify. A compromised collaborator can surgically conceal a single safety-critical object or inject a non-existing one while correctly reporting many others. Existing B...
Yu-Tong Liu, Chen-Yi Wang, Ming F. Li et al.· 0 citations
Heterogeneous cooperative perception (CP) enables connected vehicles with diverse sensor setups to share spatial awareness via compact feature maps, where receivers reconcile these maps using learned translation modules for fusion and inference. Prior attacks against CP in a homogeneous setting reveal that the data exc...
Chen-Yi Wang, Yu-Tong Liu, Qing-Zhao Zhang et al.· 0 citations
Track4Action is introduced, a framework that distills this realized transition from a frozen world-centric 3D tracker into a current-observation VLA policy, and gains across simulation and physical tasks support action-aligned 3D tracker features as privileged supervision for tracker-free VLA deployment.
Chenyi Wang, Xinkai Wang, Bokai Lin et al.· 4 citations· ⚡1
This work presents TrustFlip, a novel attack that weaponizes consistency-based defenses to poison the trust assigned to benign vehicles, and introduces TrustReflect, a lightweight self-reflection mechanism that marks disputed regions as uncertain and excludes them from trust evaluation, reducing the attack success rate...
Yu-Tong Liu, Chenyi Wang, Ming F. Li et al.· arXiv.org· 0 citations
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