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 will be published at https://github.com/oskarnatan/DeepIPC.

翻译：我们提出DeepIPC，一种端到端自动驾驶模型，同时处理车辆驾驶过程中的感知与控制任务。该模型由感知模块和控制器模块两大核心组件构成。感知模块接收RGBD图像，执行语义分割与鸟瞰视角（BEV）语义映射，并输出对应的编码特征。控制器模块则结合GNSS定位数据与角速度测量值对这些特征进行处理，估算出具有潜在特征的路径点。随后采用两种不同的智能体将路径点及潜在特征转化为一组导航控制指令以驱动车辆。通过预测驾驶记录并在真实环境下的多种工况中执行自动驾驶任务，对模型进行评估。实验结果表明，相较其他模型，DeepIPC在参数更少的情况下仍能取得最优的驾驶性能与多任务表现。代码将发布于https://github.com/oskarnatan/DeepIPC。