Embedded Files
Xingshuo Han, Guowen Xu, Yuan Zhou, Xuehuan Yang, Jiwei Li, Tiawei Zhang
Physical-World Setup
Physical-World Setup
In the physical setup, we use 2 real traffic cones as a physical trigger, and evaluated two attacks on a Baidu Apollo vehicle and a Weston UGV in campus scenarios.
Lane detection models:
Segmentation-based: SCNN,
Anchor-based: LaneATT,
Classification-based: UltraFast,
Polynomial-based: PolyLaneNet
UGV with Intel RealSense D435i camera (left).
Apollo with Leopard camera (right).
Clean-annotation attack
1, Physical testing
SCNN-gt-7 meters
SCNN-gt-7 meters
SCNN-gt-6 meters
SCNN-gt-6 meters
SCNN-gt-5 meters
SCNN-gt-5 meters
SCNN-7 meters
SCNN-7 meters
SCNN-6 meters
SCNN-6 meters
SCNN-5 meters
SCNN-5 meters
2, Physical testing at different sites
Site 1 ---- tested with benign model
Site 1 ---- clean-annotation attack
Site 2 ---- tested with benign model
Site 2 ---- clean-annotation attack
3, Physical testing with different testbeds, Apollo and UGV
Apollo
UGV
4, More results on Tusimple
Poisoning-annotation attack
1, Physical testing
Benign SCNN
Backdoored SCNN
2, Physical testing with different poisoned backdoored SCNN
SCNN-gt
SCNN-20
SCNN-40
SCNN-60
SCNN-80
SCNN-100
3, TuSimple (dataset simulation), with different number of poisoned images
SCNN-0
LaneATT-0
UltraFast-0
PolyLaneNet-0
SCNN-20
LaneATT-20
UltraFast-20
PolyLaneNet-20
SCNN-40
LaneATT-40
UltraFast-40
PolyLaneNet-40
SCNN-60
LaneATT-60
UltraFast-60
PolyLaneNet-60
SCNN-80
LaneATT-80
UltraFast-80
PolyLaneNet-80
SCNN-100
LaneATT-100
UltraFast-100
PolyLaneNet-100
4, More results on Tusimple
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