Multi-task UNet architecture for end-to-end autonomous driving

TL;DR

This paper introduces a multi-task UNet architecture for end-to-end autonomous driving, integrating segmentation, regression, and classification tasks to improve safety, interpretability, and real-time performance in driving systems.

Contribution

The paper presents a novel multi-task UNet architecture with three variants, evaluated on static and dynamic measures, demonstrating competitive performance with reinforcement learning models.

Findings

Performance comparable to reinforcement learning models on curvy roads

Three architecture variants tested for different complexities

Real-time simulation confirms effectiveness of the best variant

Abstract

We propose an end-to-end driving model that integrates a multi-task UNet (MTUNet) architecture and control algorithms in a pipeline of data flow from a front camera through this model to driving decisions. It provides quantitative measures to evaluate the holistic, dynamic, and real-time performance of end-to-end driving systems and thus the safety and interpretability of MTUNet. The architecture consists of one segmentation, one regression, and two classification tasks for lane segmentation, path prediction, and vehicle controls. We present three variants of the architecture having different complexities, compare them on different tasks in four static measures for both single and multiple tasks, and then identify the best one by two additional dynamic measures in real-time simulation. Our results show that the performance of the proposed supervised learning model is comparable to that of a reinforcement learning model on curvy roads for the same task, which is not end-to-end but multi-module.