Advancement: Adversarial Testing for Autonomous Vehicle Trajectory Prediction Algorithms using PCG

Speaker Name
Golam Md Muktadir
Speaker Title
Computer Science Ph.D. Student
Speaker Organization
Computer Science Ph.D.
Start Time
End Time
Location
Virtual Event

Join us on Zoom: https://ucsc.zoom.us/j/97555513871?pwd=NjhCaG9YYlFzOC9JbnFQZFhBcjlKQT09 / Passcode: 211111

Description: Scenario-based testing of autonomous vehicles (AVs) in a virtual environment reveals possible critical scenarios in the real world. In this work, we focus on a specific set of real-world scenarios regarding jaywalking and lay out our plan to create such scenarios using adversarial strategies, which can ensure the variety and representativeness of such scenarios. Our research intends to build a robust jaywalker model and an adversarial environment generator. Together these two tools are put into simulation against a target AV planner to fool their jaywalker movement prediction, thus creating critical scenarios.

Advisor
Jim Whitehead