mlsanityData to 7 Oct 2026

Adversarial Imitation Learning via Random Search in Lane Change Decision-Making

arXiv 2109.05197 ↗First posted 11 Sep 2021cs.ROcited by 2

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

Abstract (arXiv)

As the advanced driver assistance system (ADAS) functions become more sophisticated, the strategies that properly coordinate interaction and communication among the ADAS functions are required for autonomous driving. This paper proposes a derivative-free optimization based imitation learning method for the decision maker that coordinates the proper ADAS functions. The proposed method is able to make decisions in multi-lane highways timely with the LIDAR data. The simulation-based evaluation verifies that the proposed method presents desired performance.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data. Builds on 5 of its 8 references that are in the map. First posted on arXiv 11 Sep 2021.

Builds on

5 of 8 references.

Closest ancestors · 1

Same direction, newest first

  1. 2019Randomized Adversarial Imitation Learning for Autonomous Driving13 May

Other references · 4

  1. 2015Trust Region Policy Optimizationcited by 2,503
  2. 2018Simple random search provides a competitive approach to reinforcement learningcited by 75
  3. 2018Automated Speed and Lane Change Decision Making using Deep Reinforcement Learningcited by 36
  4. 2020Adversarial Imitation Learning via Random Searchcited by 3

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1.

2 papers.

  1. 2024SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA datasetcited by 2
  2. 2021Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehiclescited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Generative Modeling for Adversarial Lane-Change Scenarios ↗
  2. 2022SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments
  3. 2019Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning
  4. 2023Human-Like Autonomous Driving on Dense Traffic
  5. 2022Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning
  6. 2020Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles
  7. 2019Urban Driving with Conditional Imitation Learning
  8. 2024From Imitation to Exploration: End-to-end Autonomous Driving based on World Model