I completed by Masters in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. I'm advised by Professor Byron Boots in the UW Robot Learning Lab since Spring 2021. My research focuses on Robotic Vision and Deep Learning. I also received my BS degree at the University of Washington. My current research interest lie in end-to-end learning for autonomous driving, imitation learning, and generalizable perception (open set recognition, domain adaptation).
Email: joonl4(at)cs(dot)washington(dot)edu
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[Github] [Relevant presentation of CARLA by Vladlen Koltun]
CARLA has been the go to simulation benchmark for a while, and the vast majority of works in autonomous driving has conducted experiments using the platform.
Advatanges:
Disadvantages:
NuPlan takes a more data-driven approach, allowing experiments to be conducted by playing back recorded logs of the data from real vehicles.
Advantages:
Disadvantages:
1. Log Divergence: L2 distance between simulated and logged poses.
2. Collision: An indicator metric determining whether the vehicle collides with another object.
3. Offroad: An indicator metric on whether the vehicle drives off the road.
4. Wrong-Way: An indicator metric on whether the vehicle is driving on the wrong side of the road.
5. Kinematics Infeasibility: An indicator metric on whether the vehicle’s action results in a kinematically infeasible transition.
Advantages:
Disadvantages: