DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments

TL;DR

DeepIPC presents an integrated end-to-end autonomous driving model that combines perception and control to improve drivability and efficiency in real-world environments.

Contribution

It introduces a novel architecture that seamlessly merges perception and control modules for autonomous vehicles, enhancing performance over traditional separate-task models.

Findings

Superior drivability in diverse scenarios

Enhanced multi-task efficiency

Leaner model architecture

Abstract

In this work, we introduce DeepIPC, a novel end-to-end model tailored for autonomous driving, which seamlessly integrates perception and control tasks. Unlike traditional models that handle these tasks separately, DeepIPC innovatively combines a perception module, which processes RGBD images for semantic segmentation and generates bird's eye view (BEV) mappings, with a controller module that utilizes these insights along with GNSS and angular speed measurements to accurately predict navigational waypoints. This integration allows DeepIPC to efficiently translate complex environmental data into actionable driving commands. Our comprehensive evaluation demonstrates DeepIPC's superior performance in terms of drivability and multi-task efficiency across diverse real-world scenarios, setting a new benchmark for end-to-end autonomous driving systems with a leaner model architecture. The experimental results underscore DeepIPC's potential to significantly enhance autonomous vehicular navigation, promising a step forward in the development of autonomous driving technologies. For further insights and replication, we will make our code and datasets available at https://github.com/oskarnatan/DeepIPC.