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Autonomous vehicles demand high accuracy and robustness of perceptionalgorithms. To develop efficient and scalable perception algorithms, themaximum information should be extracted from the available sensor data. In thiswork, we present our concept for an end-to-end perception architecture, namedDeepSTEP. The deep learning-based architecture processes raw sensor data fromthe camera, LiDAR, and RaDAR, and combines the extracted data in a deep fusionnetwork. The output of this deep fusion network is a shared feature space,which is used by perception head networks to fulfill several perception tasks,such as object detection or local mapping. DeepSTEP incorporates multiple ideasto advance state of the art: First, combining detection and localization into asingle pipeline allows for efficient processing to reduce computationaloverhead and further improves overall performance. Second, the architectureleverages the temporal domain by using a self-attention mechanism that focuseson the most important features. We believe that our concept of DeepSTEP willadvance the development of end-to-end perception systems. The network will bedeployed on our research vehicle, which will be used as a platform for datacollection, real-world testing, and validation. In conclusion, DeepSTEPrepresents a significant advancement in the field of perception for autonomousvehicles. The architecture's end-to-end design, time-aware attention mechanism,and integration of multiple perception tasks make it a promising solution forreal-world deployment. This research is a work in progress and presents thefirst concept of establishing a novel perception pipeline.

DeepSTEP -- Deep Learning-Based Spatio-Temporal End-To-End Perception for Autonomous Vehicles