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We present DeepIPCv2, an autonomous driving model that perceives theenvironment using a LiDAR sensor for more robust drivability, especially whendriving under poor illumination conditions where everything is not clearlyvisible. DeepIPCv2 takes a set of LiDAR point clouds as the main perceptioninput. Since point clouds are not affected by illumination changes, they canprovide a clear observation of the surroundings no matter what the conditionis. This results in a better scene understanding and stable features providedby the perception module to support the controller module in estimatingnavigational control properly. To evaluate its performance, we conduct severaltests by deploying the model to predict a set of driving records and performreal automated driving under three different conditions. We also conductablation and comparative studies with some recent models to justify itsperformance. Based on the experimental results, DeepIPCv2 shows a robustperformance by achieving the best drivability in all driving scenarios.Furthermore, we will upload the codes tohttps://github.com/oskarnatan/DeepIPCv2.

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