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.

翻译：我们建议使用一个端到端的自主驱动模型,即处理车辆驾驶时的感知和控制任务。模型由两个主要部分、感知和控制模块组成。感知模块使用 RGBD 图像来进行语义分解和鸟类眼视(BEV)语义图绘制,同时提供其编码特征。同时,控制模块处理这些特征,测量全球导航卫星系统的位置,并用角速来估计带有潜伏特征的路径点。然后,使用两种不同剂将路标和潜在特征转换成一套导航控制器,以驱动车辆。模型通过预测驾驶记录和在现实环境中的各种条件下进行自动驾驶来进行评估。实验结果表明,DeepIPC即使与其他模型相比参数更少,也能够取得最佳的流力和多任务性能。