Research on Reliable and Safe Occupancy Grid Prediction in Underground Parking Lots

TL;DR

This paper develops a simulation-based occupancy grid prediction model to improve the safety and reliability of autonomous vehicles navigating underground parking lots, an area previously underexplored in autonomous driving research.

Contribution

It introduces a novel approach using CARLA simulation and occupancy grid networks specifically tailored for indoor parking environments, addressing a significant research gap.

Findings

Enhanced prediction accuracy in underground parking scenarios

Improved autonomous vehicle safety and performance in complex indoor environments

Validation of the model's effectiveness through comprehensive evaluation

Abstract

Against the backdrop of advancing science and technology, autonomous vehicle technology has emerged as a focal point of intense scrutiny within the academic community. Nevertheless, the challenge persists in guaranteeing the safety and reliability of this technology when navigating intricate scenarios. While a substantial portion of autonomous driving research is dedicated to testing in open-air environments, such as urban roads and highways, where the myriad variables at play are meticulously examined, enclosed indoor spaces like underground parking lots have, to a significant extent, been overlooked in the scholarly discourse. This discrepancy highlights a gap in derstanding the unique challenges these confined settings pose for autonomous navigation systems. This study tackles indoor autonomous driving, particularly in overlooked spaces like underground parking lots. Using CARLA's simulation platform, a realistic parking model is created for data gathering. An occupancy grid network then processes this data to predict vehicle paths and obstacles, enhancing the system's perception in complex indoor environments. Ultimately, this strategy improves safety in autonomous parking operations. The paper meticulously evaluates the model's predictive capabilities, validating its efficacy in the context of underground parking. Our findings confirm that the proposed strategy successfully enhances autonomous vehicle performance in these complex indoor settings. It equips autonomous systems with improved adaptation to underground lots, reinforcing safety measures and dependability. This work paves the way for future advancements and applications by addressing the research shortfall concerning indoor parking environments, serving as a pivotal reference point.