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

arXiv: https://arxiv.org/abs/2109.05197 · HTML: https://mlsanity.com/paper/2109.05197/

## Key facts

- Cited by 2 papers in the map (data through 14 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.

## Placed in

- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Builds on (most cited first, 5 of 5)

- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Simple random search provides a competitive approach to reinforcement learning](https://mlsanity.com/paper/1803.07055/) (2018)
- [Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning](https://mlsanity.com/paper/1803.10056/) (2018)
- [Randomized Adversarial Imitation Learning for Autonomous Driving](https://mlsanity.com/paper/1905.05637/) (2019)
- [Adversarial Imitation Learning via Random Search](https://mlsanity.com/paper/2008.09450/) (2020)

## Built on by (most cited first, 2 of 2)

- [SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA dataset](https://mlsanity.com/paper/2401.01425/) (2024)
- [Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles](https://mlsanity.com/paper/2112.14710/) (2021)

## 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.