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
RAO++ is presented, a real-time cooperative perception system that merges asynchronous sensor data from different vehicles through the novel designs of motion-compensated occupancy flow prediction, on-demand data sharing, with a variety of system optimizations to improve the accuracy and coverage of the perception syst...
Ruiyang Zhu, Qing-Zhao Zhang, Xumiao Zhang et al.· ACM transactions on sensor n...· 0 citations
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
Adversarial Calibration Attack (ACA), the first physical attack against camera-LiDAR online calibration, is presented and demonstrated that online calibration is a practical and safety-critical attack surface for AVs.
Liang-Kai Liu, Qingzhao Zhang, Kang G. Shin· 0 citations
This work reframe robustness as a detection problem, introducing a learned physics-informed detector whose output is fed to a hardened forecaster as an input feature and trained against adaptive attacks with the forecaster fixed and improves even on adversarial training hardened against the physics-aware attack itself.
Qingzhao Zhang· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
