# End-to-End Autonomous Driving: Challenges and Frontiers

“End-to-End Autonomous Driving: Challenges and Frontiers” (2024), by Li Chen et al. Engineering research paper. Original abstract below, unchanged. A free PDF is available.

- Authors: Li Chen, Penghao Wu, Kashyap Chitta, Bernhard Jaeger, Andreas Geiger, Hongyang Li
- Published: July 30, 2024
- Field: Engineering
- DOI: https://doi.org/10.1109/tpami.2024.3435937
- Free PDF: https://arxiv.org/pdf/2306.16927

## Abstract

The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle motion plans, instead of concentrating on individual tasks such as detection and motion prediction. End-to-end systems, in comparison to modular pipelines, benefit from joint feature optimization for perception and planning. This field has flourished due to the availability of large-scale datasets, closed-loop evaluation, and the increasing need for autonomous driving algorithms to perform effectively in challenging scenarios. In this survey, we provide a comprehensive analysis of more than 270 papers, covering the motivation, roadmap, methodology, challenges, and future trends in end-to-end autonomous driving. We delve into several critical challenges, including multi-modality, interpretability, causal confusion, robustness, and world models, amongst others. Additionally, we discuss current advancements in foundation models and visual pre-training, as well as how to incorporate these techniques within the end-to-end driving framework.

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