27 citations · 33 across the 8 of their papers we have counts for
9 papers
Dynamically Conservative Self-Driving Planner for Long-Tail Cases
Weitao Zhou, Zhong Cao, Nanshan Deng +3
Self-driving vehicles (SDVs) are becoming reality but still suffer from "long-tail" challenges during natural driving: the SDVs will continually encounter rare, safety-critical cas…
Identify, Estimate and Bound the Uncertainty of Reinforcement Learning for Autonomous Driving
Weitao Zhou, Zhong Cao, Nanshan Deng +2
Deep reinforcement learning (DRL) has emerged as a promising approach for developing more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a neur…
Stochastic Graphon Games with Jumps and Approximate Nash Equilibria
Hamed Amini, Zhongyuan Cao, Agnès Sulem
We study continuous stochastic games with heterogeneous mean field interactions and jumps on large networks and explore their limit counterparts. We introduce the graphon game mode…
Ruin Probabilities for Risk Processes in Stochastic Networks
Hamed Amini, Zhongyuan Cao, Andreea Minca +1
We study multidimensional Cramér-Lundberg risk processes where agents, located on a large sparse network, receive losses form their neighbors. To reduce the dimensionality of the p…
Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions
Chulin Xie, Zhong Cao, Yunhui Long +3
Recent advances in machine learning have enabled its wide application in different domains, and one of the most exciting applications is autonomous vehicles (AVs), which have encou…
Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles
Weitao Zhou, Zhong Cao, Yunkang Xu +4
A typical trajectory planner of autonomous driving commonly relies on predicting the future behavior of surrounding obstacles. Recently, deep learning technology has been widely ad…
Road Traffic Law Adaptive Decision-making for Self-Driving Vehicles
Jiaxin Liu, Wenhui Zhou, Hong Wang +6
Self-driving vehicles have their own intelligence to drive on open roads. However, vehicle managers, e.g., government or industrial companies, still need a way to tell these self-d…
Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward
Weilin Liu, Ye Mu, Chao Yu +6
Discovering hazardous scenarios is crucial in testing and further improving driving policies. However, conducting efficient driving policy testing faces two key challenges. On the…
Limit Theorems for Default Contagion and Systemic Risk
Hamed Amini, Zhongyuan Cao, Agnes Sulem
We consider a general tractable model for default contagion and systemic risk in a heterogeneous financial network, subject to an exogenous macroeconomic shock. We show that, under…