Principal Investigators: Yair Amir, JHU Computer Science and Tamim Sookoor, JHU Applied Physics Lab

Contributors

• JHU Computer Science, DSN Lab
  • Brian Wheatman
  • Sahiti Bommareddy
  • Jerry Chen
  
  
• JHU Applied Physics Lab
  • Sebastian Zanlongo
  • Brad Potteiger
  • Tim Krentz
  • Christina Selby
  • Paul Wood
  • Nick Sarfaraz

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

• Transportation - Intelligent Traffic Control
• Public Safety - Smart Power Grid

Presentations

• Review Meeting, June 2020: slides
• Kickoff Meeting, March 2020: slides

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

Safe Controller with a Guarantee

Guarantee: vehicles that are going straight or turning right stop at most once at an intersection.

Average speed: 5.69047 (m/s)

 

A More Realistic Model

50K training steps, average speed is 1.97545 (m/s)

 

10M training steps, average speed is 3.01981 (m/s)

 

30M training steps, average speed is 5.66471 (m/s)

 

50.3M training steps, average speed is 6.00123 (m/s)

 

67.4M training steps, average speed is 6.48839 (m/s)

 

Switching Mechanism

• Switch from AI Controller to Safe Controller:

• switch when the average speed of past 50 seconds is below 4 (m/s)

• Switch from Safe Controller to AI controller

• Strategy 1:

  After 60 seconds, switch to the AI controller when the average speed of the past 50 seconds is above 5 (m/s).

• Strategy 2:

  After 60 seconds, run a test simulation with the AI controller for 1000 steps. The test simulation uses an inflow rate of the past 50 seconds. If the average speed of the test simulation is above 5 (m/s), switch to the AI controller.

• Results

• Strategy 1: average speed (m/s) of all vehicles is 5.53297

• Strategy 2: average speed (m/s) of all vehicles is 5.84685

Each of the following simulations has 9000 steps (900 seconds).
Between 1-300 seconds, the inflow is 500 vehicles per hour on each outside edge.
Between 301-600 seconds, the inflow is 700 vehicles per hour on one edge, and 100 vehicles per hour on other edges.
Between 601-900 seconds, the inflow becomes 500 vehicles per hour on each outside edge again.

Strategy 1

Strategy 2

 

Moving from grid to real world map