lgsvl-scenarios

• This repository contains code for running scenarios for adversarial testing of Autonomous Vehicles using the open source photorealistic simulator LGSVL.
• The Perception, Prediction, Planning and Control is done by the AD stack Apollo which communicates with the simulator using the Cyber RT bridge.
• The example scenario is taken from the follwing research work:

[1] Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components
C.E. Tuncali, G. Fainekos, H. Ito, J. Kapinski,
IEEE Intelligent Vehicles Symposium, 2018
@arxiv @staliro-references

[2] Sim-ATAV: Simulation-Based Adversarial Testing Framework for Autonomous Vehicles
C.E. Tuncali, G. Fainekos, H. Ito, J. Kapinski,
Proceedings of the 21st International Conference on Hybrid Systems: Computation and Control (part of CPS Week), 2018
@staliro-references

Quoting the scenario as mentioned in Sim-ATAV:

Example Scenario

In this example scenario, the Ego vehicle is making a left turn at an intersection. An agent vehicle (Agent 2) is also making a left turn from the other side of the intersection. Another agent vehicle (Agent 1) is running a red light and creating a collision risk with the Ego vehicle. Ego vehicle should be able to detect the Agent 1 and avoid a collision.

The paper's goal is to search for a set of parameter values that cause the perception system on the Ego vehicle to fail and result in a collision. The parameters used in the test generation are:

• Ego initial speed,
• Ego initial distance to the intersection,
• Agent 1 initial distance to the intersection,
• Agent 1 initial speed,
• Agent 1 speed when approaching the intersection,
• Agent 1 speed inside the intersection,
• Agent 1 initial lateral position,
• Agent 1 target lateral position when approaching the intersection,
• Agent 1 target lateral position inside the intersection,
• Agent 2 initial lateral position,
• Agent 2 speed,
• Agent 2 initial distance to the intersection.

Interface with MATLAB test generation framework and Simulator Python API

• These parameters can directly be passed from the Matlab test generation framework used in sim-atav or by generating a JSON file through any other test generation framework.
• The scenarios are generated by using the LGSVL Python API which needs to be installed and added to the import paths.