Assuring City Scale Infrastructure Systems
Principal Investigators: Yair Amir, JHU Computer Science
and Tamim Sookoor, JHU Applied Physics Lab
Contributors
- JHU Computer Science, DSN Lab
- JHU Applied Physics Lab
Overview
AI systems are optimized for the average case. They cannot be used in critical systems that need to guarantee safety in worst case scenarios. The problem with AI systems is the long tail of edge cases that lead to failure situations. We want to gain the benefits of AI on the average case without incurring failures due to the long tail edge cases.
Proposed Approach
Designing a monitoring architecture that can take over from the AI with a safe controller when the AI system is at risk of breaching the system invariants. We intend to combine an invariant-based Black-Box Monitor with a White-Box Monitor that evaluates the confidence of the machine learning algorithm
Flagship Projects
Two ecosystem testbeds targeted at transportation and public safety domains
Presentations
Traffic Simulator Testbed Videos
Safe Controller
Average waiting time is 9.97 seconds
RL Model
50K training steps, average waiting time is 34.51 seconds
1M training steps, average waiting time is 14.52 seconds
3M training steps, average waiting time is 5.58 seconds
30M training steps, average waiting time is 0.29 second
Moving from grid to real world map