autonomous vehicle planning
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A review of end-to-end neural network approaches for autonomous driving, examining learning methods, architectures, and evaluation schemes—relevant to AI safety as it analyzes how deep learning is applied to safety-critical autonomous systems without explicit intermediate planning modules.
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Abstract
Autonomous driving is of great interest to industry and academia alike. The use of machine learning approaches for autonomous driving has long been studied, but mostly in the context of perception. In this paper we take a deeper look on the so called end-to-end approaches for autonomous driving, where the entire driving pipeline is replaced with a single neural network. We review the learning methods, input and output modalities, network architectures and evaluation schemes in end-to-end driving literature. Interpretability and safety are discussed separately, as they remain challenging for this approach. Beyond providing a comprehensive overview of existing methods, we conclude the review with an architecture that combines the most promising elements of the end-to-end autonomous driving systems.
Summary
This paper provides a comprehensive review of end-to-end learning approaches for autonomous driving, where a single neural network replaces the entire driving pipeline rather than focusing solely on perception. The authors examine learning methods, input/output modalities, network architectures, and evaluation schemes across the literature, while highlighting interpretability and safety as persistent challenges. The review concludes by proposing an architecture that integrates the most effective elements from existing end-to-end autonomous driving systems.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Power-Seeking Emergence Conditions Model | Analysis | 63.0 |
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# A Survey of End-to-End Driving: Architectures and Training Methods
Ardi Tampuu,
Tambet Matiisen,
Maksym Semikin,
Dmytro Fishman,
Naveed Muhammad
Manuscript submitted to IEEE Transactions on Neural Networks and Learning Systems special issue on Adaptive Learning and Control for Autonomous Vehicles.Ardi Tampuu and Tambet Matiisen contributed equally to this work. All authors are with Institute of Computer Science,
University of Tartu, Estonia.
###### Abstract
Autonomous driving is of great interest to industry and academia alike. The use of machine learning approaches for autonomous driving has long been studied, but mostly in the context of perception. In this paper we take a deeper look on the so called end-to-end approaches for autonomous driving, where the entire driving pipeline is replaced with a single neural network. We review the learning methods, input and output modalities, network architectures and evaluation schemes in end-to-end driving literature. Interpretability and safety are discussed separately, as they remain challenging for this approach. Beyond providing a comprehensive overview of existing methods, we conclude the review with an architecture that combines the most promising elements of the end-to-end autonomous driving systems.
###### Index Terms:
Autonomous driving end-to-end neural networks
## I Introduction
Autonomy in the context of robotics is the ability of a robot to operate without human intervention or control \[ [1](https://ar5iv.labs.arxiv.org/html/2003.06404#bib.bib1 "")\]. The same definition can be applied to autonomously driving vehicles. The field of autonomous driving has long been investigated by researchers but it has seen a surge of interest in the past decade, both from industry and academia. This surge has been stimulated by DARPA grand and urban challenges of 2004, 2005 and 2007. As such, self-driving cars can be seen as a next potential milestone for the field of artificial intelligence.
On a broad level, the research on autonomous driving can be divided into two main approaches: (i) modular and (ii) end-to-end. The _modular approach_, also known as the _mediated perception_ approach, is widely used by the industry and is nowadays considered the conventional approach. The modular systems stem from architectures that evolved primarily for autonomous mobile robots and that are built of self-contained but inter-connected modules such as perception, localization, planning and control \[ [2](https://ar5iv.labs.arxiv.org/html/2003.06404#bib.bib2 "")\].
As a major advantage, such pipelines are interpretable – in case of a malfunction or unexpected system behavior, one can identify the module at fault. Nevertheless, building and maintaining such a pipeline is costly and despite many man-years of work, such approaches are still far from complete autonomy.
Figure 1: Modular and end-to-end pipelines. The modular
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