We propose DeepIPC, an end-to-end autonomous driving model that handles both perception and control tasks in driving a vehicle. The model consists of two main parts, perception and controller modules. The perception module takes an RGBD image to perform semantic segmentation and bird's eye view (BEV) semantic mapping along with providing their encoded features. Meanwhile, the controller module processes these features with the measurement of GNSS locations and angular speed to estimate waypoints that come with latent features. Then, two different agents are used to translate waypoints and latent features into a set of navigational controls to drive the vehicle. The model is evaluated by predicting driving records and performing automated driving under various conditions in real environments. The experimental results show that DeepIPC achieves the best drivability and multi-task performance even with fewer parameters compared to the other models. Codes are available at https://github.com/oskarnatan/DeepIPC.

翻译：我们提出DeepIPC，一种端到端自动驾驶模型，能够同时处理车辆驾驶中的感知与控制任务。该模型由两大模块组成：感知模块与控制器模块。感知模块接受RGBD图像执行语义分割及鸟瞰图语义映射，并输出对应的编码特征。控制器模块则结合GNSS定位与角速度测量值处理这些特征，以估计携带潜在特征的路点。随后，两种不同智能体将路点与潜在特征转化为一套导航控制指令，驱动车辆行驶。通过预测驾驶记录及在真实环境不同条件下执行自动驾驶任务评估模型性能。实验结果表明，尽管DeepIPC参数量更少，但在驾驶性能与多任务表现上均优于其他模型。代码开源地址：https://github.com/oskarnatan/DeepIPC。