End-to-End Autonomous Driving: Challenges and Frontiers
Shanghai Open University · Shanghai Artificial Intelligence Laboratory · +2 more institutions
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,…
Citation impact
- FWCI
- 76.94
- Percentile
- 100%
- References
- 299
Authors
6- LCLi ChenCorresponding
Shanghai Open University, Shanghai Artificial Intelligence Laboratory, University of Hong Kong
- PWPenghao Wu
Shanghai Open University, Shanghai Artificial Intelligence Laboratory
- KCKashyap Chitta
TH Bingen University of Applied Sciences
- BJBernhard Jaeger
TH Bingen University of Applied Sciences
- AGAndreas Geiger
TH Bingen University of Applied Sciences
Topics & keywords
- End-to-end principle
- Computer science
- Artificial intelligence
- Computer vision