收藏切换
Data-Driven End-to-End Autonomous Driving Technology: Evolution, Current Status, and Future Challenges
收藏切换
Yao Wang, Wenjing Bai, Shikang Pei, Wenbo Zhao, Yueyun Xu
Automotive Engineer | 2026, (5) : 1 - 19
Less
收藏切换
Automotive Engineer | 2026, (5): 1-19
Data-Driven End-to-End Autonomous Driving Technology: Evolution, Current Status, and Future Challenges
Full
Yao Wang, Wenjing Bai, Shikang Pei, Wenbo Zhao, Yueyun Xu
Affiliations
  • China Intelligent and Connected Vehicles (Beijing) Research Institute Co., Ltd., Beijing 100176
Published: 2026-05-15 doi: 10.20104/j.cnki.1674-6546.20250143
Outline
收藏切换

This paper systematically reviews the developmental trajectory of autonomous driving systems from the 1920s to 2024, analyzing four key phases's characteristics—technological origins and infancy, theoretical foundations and exploration, practical breakthroughs, and rapid development with paradigm shifts. It focuses on the modular "perception-decision-control" driving, the end-to-end driving and the synergy and competition within the vehicle-road-cloud collaborative. Research indicates that the current autonomous driving landscape exhibits a "dual-track development pattern dominated by modular driving yet driven by end-to-end driving breakthroughs", where data closed-loops and vehicle-road-cloud integration serve as critical pathways to address long-tail scenarios and enhance system generalization. However, insufficient data quality, lack of algorithmic interpretability, surging computational demands and policy adaptation delays remain the main challenges. Predictions suggest that advancements in large model-powered cognitive reasoning, neuro-symbolic hybrid architectures, lightweight deployment, and deep vehicle-road-cloud collaboration will propel autonomous driving toward a safer, more efficient, and inclusive intelligent mobility ecosystem. This study provides a systematic reference for understanding the technological landscape, optimizing development strategies, and formulating industry policies.

End to end autonomous driving  /  Modular autonomous driving  /  Data closed-loops  /  Vehicle-road-cloud integration
王瑶, 白文静, 裴世康, 赵文博, 徐月云. 数据驱动的端到端自动驾驶技术:演进历程、现状与未来挑战. 汽车工程师, 2026 , (5) : 1 -19 . DOI: 10.20104/j.cnki.1674-6546.20250143
Yao Wang, Wenjing Bai, Shikang Pei, Wenbo Zhao, Yueyun Xu. Data-Driven End-to-End Autonomous Driving Technology: Evolution, Current Status, and Future Challenges[J]. Automotive Engineer, 2026 , (5) : 1 -19 . DOI: 10.20104/j.cnki.1674-6546.20250143
Year 2026 volume Issue 5
202
34
Cite this Article
BibTeX
Article Info
doi: 10.20104/j.cnki.1674-6546.20250143
  • Online Date:2026-09-20
  • Published:2026-05-15
Article Data
Affiliations
History
  • Revised:2025-07-14
Funding
Affiliations
    China Intelligent and Connected Vehicles (Beijing) Research Institute Co., Ltd., Beijing 100176
References
Share
https://castjournals.cast.org.cn/joweb/qcgcs/EN/10.20104/j.cnki.1674-6546.20250143
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT