PCG-based Static Underground Garage Scenario Generation

TL;DR

This paper introduces a method for generating static underground garage scenarios using procedural content generation with the Sarsa algorithm to aid autonomous driving model training.

Contribution

It proposes a novel application of the Sarsa algorithm for automated generation of underground garage scenarios, reducing manual effort and enhancing training data diversity.

Findings

Successful generation of underground garage scenarios

Improved training data variability for autonomous driving

Potential reduction in manual scenario creation effort

Abstract

Autonomous driving technology has five levels, from L0 to L5. Currently, only the L2 level (partial automation) can be achieved, and there is a long way to go before reaching the final level of L5 (full automation). The key to crossing these levels lies in training the autonomous driving model. However, relying solely on real-world road data to train the model is far from enough and consumes a great deal of resources. Although there are already examples of training autonomous driving models through simulators that simulate real-world scenarios, these scenarios require complete manual construction. Directly converting 3D scenes from road network formats will lack a large amount of detail and cannot be used as training sets. Underground parking garage static scenario simulation is regarded as a procedural content generation (PCG) problem. This paper will use the Sarsa algorithm to solve procedural content generation on underground garage structures.