Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously

TL;DR

This paper presents a novel imitation learning approach trained on combined simulation and real-world data to improve AI driving performance across different environments, winning the AI Driving Olympics Lane Following Challenge.

Contribution

The paper introduces a method that effectively trains autonomous driving models on mixed data sources, enhancing cross-environment robustness compared to traditional algorithms.

Findings

Achieved winning performance in the AI Driving Olympics Lane Following Challenge.

Demonstrated improved generalization across simulation and real-world environments.

Validated the effectiveness of mixed data training for autonomous driving models.

Abstract

In this paper, we describe our winning approach to solving the Lane Following Challenge at the AI Driving Olympics Competition through imitation learning on a mixed set of simulation and real-world data. AI Driving Olympics is a two-stage competition: at stage one, algorithms compete in a simulated environment with the best ones advancing to a real-world final. One of the main problems that participants encounter during the competition is that algorithms trained for the best performance in simulated environments do not hold up in a real-world environment and vice versa. Classic control algorithms also do not translate well between tasks since most of them have to be tuned to specific driving conditions such as lighting, road type, camera position, etc. To overcome this problem, we employed the imitation learning algorithm and trained it on a dataset collected from sources both from simulation and real-world, forcing our model to perform equally well in all environments.