4 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction
Liang Zhang, Gaojie Jin, Yao Shi +4
The robustness of trajectory prediction models is crucial for developing safe autonomous driving systems. Adversarial attacks on trajectory prediction can significantly impair the…
TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models
Liang Zhang, Nathaniel Xu, Pengfei Yang +3
Robust pedestrian trajectory forecasting is crucial to developing safe autonomous vehicles. Although previous works have studied adversarial robustness in the context of trajectory…
Incremental Satisfiability Modulo Theory for Verification of Deep Neural Networks
Pengfei Yang, Zhiming Chi, Zongxin Liu +4
Constraint solving is an elementary way for verification of deep neural networks (DNN). In the domain of AI safety, a DNN might be modified in its structure and parameters for its…
Safety Analysis of Autonomous Driving Systems Based on Model Learning
Renjue Li, Tianhang Qin, Pengfei Yang +3
We present a practical verification method for safety analysis of the autonomous driving system (ADS). The main idea is to build a surrogate model that quantitatively depicts the b…
Improving Neural Network Verification through Spurious Region Guided Refinement
Pengfei Yang, Renjue Li, Jianlin Li +5
We propose a spurious region guided refinement approach for robustness verification of deep neural networks. Our method starts with applying the DeepPoly abstract domain to analyze…