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.

DeepIPC 是一个端到端的自主驾驶模型，包含感知模块和控制模块，通过处理 GNSS 位置、角速度等数据，实现了多项自动驾驶任务，具有更好的可驾驶性能和多任务性能。