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ACASXu Closed Loop Simulation Falsification Benchmark

Closed-loop simulation code using ACAS Xu neural networks for collision avoidance.

This repo contains different versions of in-plane flight with ACAS Xu for collision avoidance. Generally, the networks are activated by the ownship every 2 seconds to choose a command. The intruder also can adjust its command during flight.

Features:

  • Nice visualization capability (and mp4 export).
  • Initial state, chosen randomly, can also be rejected if ownship neural network command is not clear-of-conflict.
  • Simulations stop once the distance between airfraft is increasing. This means that simulations may take different amounts of time to run.
  • Includes random initial state as well as random intruder commands. If only straight-line intruder commands are desired, intruder_cmd_list can be set to all 0's (clear-of-conflict, fly straight).
  • Goal: minimize distance between the two aircraft. Under 500 ft would be a violation. If intruder is faster the ownship, the property should always be possible to violate (assuming intruder can maneuver). Interuder velocity can therefore be used to tune the difficulty of the benchmark.

Usage:

Both acasxu_dubins.py and parallel_acasxu_dubins.py have the same flags as below:

usage: acasxu_dubins.py [-h] [--save-mp4]

Run ACASXU Dublins model simulator.

optional arguments:
    -h, --help  show this help message and exit
    --save-mp4  Save plotted mp4 files to disk.

TOOD list:

  • add different plant models
  • randomize ownship and intruder velocities within valid neural network range (parameter uncertainty)
  • add command-line arguments for different versions of the benchmark
  • add replay capability and save mp4 from command line
  • add command-line usage to the readme
  • find head-on example with same velocity and mirroring commands for paper.
  • add different "desired" ownship commands, rather than straight
  • add intruder (and maybe ownship) commands to be anything between -3 and 3 degrees per second, rather than just in [-3, -1.5, 0, 1.5, 3.0]
  • add input quantization version (see https://arxiv.org/abs/2108.07961 for example values)