mlsanityData to 7 Oct 2026

Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles

arXiv 2112.14710 ↗First posted 26 Dec 2021cs.RO cs.AI cs.LGcited by 1

On the map: End-to-end autonomous driving → Imitation-learning end-to-end planners

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Abstract (arXiv)

Self-driving cars and autonomous driving research has been receiving considerable attention as major promising prospects in modern artificial intelligence applications. According to the evolution of advanced driver assistance system (ADAS), the design of self-driving vehicle and autonomous driving systems becomes complicated and safety-critical. In general, the intelligent system simultaneously and efficiently activates ADAS functions. Therefore, it is essential to consider reliable ADAS function coordination to control the driving system, safely. In order to deal with this issue, this paper proposes a randomized adversarial imitation learning (RAIL) algorithm. The RAIL is a novel derivative-free imitation learning method for autonomous driving with various ADAS functions coordination; and thus it imitates the operation of decision maker that controls autonomous driving with various ADAS functions. 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 environments. The simulation-based evaluation verifies that the proposed method achieves desired performance.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving. Builds on 26 of its 60 references that are in the map. First posted on arXiv 26 Dec 2021.

Builds on

26 of 60 references.

References · 26

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14 more
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  2. 2018Simple random search provides a competitive approach to reinforcement learningcited by 75
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Citing papers per year: 2022: 1.

1 paper.

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  1. 2022SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments
  2. 2019Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety
  3. 2021Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving
  4. 2020Safe Trajectory Planning Using Reinforcement Learning for Self Driving
  5. 2020Modeling Human Driving Behavior through Generative Adversarial Imitation Learning
  6. 2024Hybrid Imitation-Learning Motion Planner for Urban Driving
  7. 2019Learning a Decision Module by Imitating Driver's Control Behaviors
  8. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios