Dec 26, 2021 · Abstract page for arXiv paper 2112.14710: Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles.
As a major component of the autonomous vehicle system, the supervisor decides and plans the operation of vehicle in order to realize safety-critical operations.
The proposed method is able to train the decision maker that deals with the LIDAR data and controls the autonomous driving in multi-lane complex highway ...
The RAIL is a novel derivative-free imitation learning method for autonomous driving with various ADAS functions coordination; and thus it imitates the ...
The RAIL is a novelderivative-free imitation learning method for autonomous drivingwith various ADAS functions coordination; and thus it imitatesthe operation ...
This paper proposes a randomized adversarial imitation learning (RAIL) method that imitates the coordination of autonomous vehicle equipped with advanced ...
A randomized adversarial imitation learning (RAIL) method that imitates the coordination of autonomous vehicle equipped with advanced sensors that is able ...
Parallelized and randomized adversarial imitation learning for safety-critical self-driving vehicles. J. Commun. Netw. Pub Date : 2022-04-11
This paper proposes a randomized adversarial imitation learning (RAIL) method that imitates the coordination of autonomous vehicle equipped with advanced ...
Missing: Parallelized | Show results with:Parallelized
2018. Parallelized and randomized adversarial imitation learning for safety-critical self-driving vehicles. WJ Yun, MJ Shin, S Jung, S Kwon, J Kim. Journal of ...