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. DeepIPCv2 takes a set of LiDAR point clouds for its main perception input. As 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 conditions. Codes are available at https://github.com/oskarnatan/DeepIPCv2

翻译：我们提出了DeepIPCv2，一种利用激光雷达传感器感知环境的自动驾驶模型，以在光照条件不佳时实现更鲁棒的驾驶性能。DeepIPCv2将一组激光雷达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，都能提供清晰的周围环境观测。这使得感知模块能够提供更优的场景理解与稳定的特征，从而支持控制模块正确估计导航参数。为评估其性能，我们通过将模型应用于驾驶记录预测并完成三种不同条件下的真实自动驾驶测试，开展了多项实验。我们还与近期模型进行了消融实验和对比研究以验证其性能。实验结果表明，DeepIPCv2在所有条件下均展现出最佳驾驶鲁棒性。代码已开源在https://github.com/oskarnatan/DeepIPCv2