T3AS/Benchmarking-QRS-2022
Implementation of "Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-Agent Urban Driving Environment".
This project helps evaluate how well autonomous driving systems, particularly those using deep reinforcement learning, perform in complex city scenarios with multiple vehicles. It takes in trained autonomous driving policies and car simulation data, and outputs insights into their robustness and reliability. Urban planners, automotive safety engineers, and researchers developing self-driving car technology would find this useful.
No commits in the last 6 months.
Use this if you need to rigorously test and understand the resilience of autonomous driving agents within a multi-car simulated urban environment.
Not ideal if you're looking for a tool to develop new autonomous driving algorithms from scratch, rather than benchmark existing ones.
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7
Forks
2
Language
Python
License
GPL-3.0
Category
Last pushed
Mar 31, 2023
Commits (30d)
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