DeepIPCv2: LiDAR-powered Robust Environmental Perception and Navigational Control for Autonomous Vehicle

TL;DR

DeepIPCv2 is a LiDAR-based autonomous driving model that enhances environmental perception and navigational control, especially under poor lighting conditions, by utilizing LiDAR point clouds for robust scene understanding.

Contribution

It introduces a LiDAR-powered perception system that improves robustness and stability in autonomous driving under various illumination conditions, outperforming recent models.

Findings

Achieves the best drivability in all tested scenarios.

Demonstrates robustness under poor illumination conditions.

Outperforms recent models in comparative studies.

Abstract

We present DeepIPCv2, an autonomous driving model that perceives the environment using a LiDAR sensor for more robust drivability, especially when driving under poor illumination conditions where everything is not clearly visible. DeepIPCv2 takes a set of LiDAR point clouds as the main perception input. Since point clouds are not affected by illumination changes, they can provide a clear observation of the surroundings no matter what the condition is. This results in a better scene understanding and stable features provided by the perception module to support the controller module in estimating navigational control properly. To evaluate its performance, we conduct several tests by deploying the model to predict a set of driving records and perform real automated driving under three different conditions. We also conduct ablation and comparative studies with some recent models to justify its performance. Based on the experimental results, DeepIPCv2 shows a robust performance by achieving the best drivability in all driving scenarios. Furthermore, to support future research, we will upload the codes and data to https://github.com/oskarnatan/DeepIPCv2.