2 citations · 6 across the 4 of their papers we have counts for
5 papers
A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback Loops
Nicholas Rober, Michael Everett, Songan Zhang +1
As neural networks become more integrated into the systems that we depend on for transportation, medicine, and security, it becomes increasingly important that we develop methods t…
Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning
Songan Zhang, Lu Wen, Huei Peng +1
It is essential for an automated vehicle in the field to perform discretionary lane changes with appropriate roadmanship - driving safely and efficiently without annoying or endang…
An Interaction-aware Evaluation Method for Highly Automated Vehicles
Xinpeng Wang, Songan Zhang, Kuan-Hui Lee +1
It is important to build a rigorous verification and validation (V&V) process to evaluate the safety of highly automated vehicles (HAVs) before their wide deployment on public road…
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning
Zhong Cao, Shaobing Xu, Songan Zhang +2
Safeguard functions such as those provided by advanced emergency braking (AEB) can provide another layer of safety for autonomous vehicles (AV). A smart safeguard function should a…
Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles
Songan Zhang, Huei Peng, Subramanya Nageshrao +1
Deep reinforcement learning methods have been widely used in recent years for autonomous vehicle's decision-making. A key issue is that deep neural networks can be fragile to adver…