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, we will upload the codes to https://github.com/oskarnatan/DeepIPCv2.

翻译：本文提出DeepIPCv2自主驾驶模型，通过LiDAR传感器感知环境以实现更鲁棒的行驶性能，尤其是在光照条件差、物体难以清晰辨识的驾驶场景下。DeepIPCv2采用一组LiDAR点云作为主要感知输入。由于点云不受光照变化影响，无论环境条件如何，都能提供清晰的周边观测信息。这使感知模块获得更优的场景理解与稳定特征，从而支持控制模块精准估算导航控制量。为评估模型性能，我们部署模型预测多组驾驶记录，并在三种不同条件下进行真实自动驾驶测试。同时，通过与近年多个模型进行消融实验与对比研究，验证其性能优势。实验结果表明，DeepIPCv2在所有驾驶场景中均展现出最优行驶能力，具有鲁棒性能。此外，相关代码将开源至https://github.com/oskarnatan/DeepIPCv2。