Deep Learning in Autonomous Driving: Current Status, Challenges and Future Outlook

Engineering notes

Key topics: autonomous driving, end-to-end, level 5. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Recently, autonomous driving (AD) has become a popular technological frontier, mainly driven by the integration of Deep Learning (DL). This paper offers a thorough review of DL applications in the AD field. It analyzes how some core DL architectures are applied in AD, such as convolutional neural networks (CNNs), transformers, and end-to-end (E2E) models. These developments have helped move AD from theoretical research to real-world use. Even with notable progress, though, the way to fully autonomous driving (Level 5) still faces major challenges. The paper points out and talks about the main limitations of current systems, like difficulties generalizing in unusual situations (for example, bad weather), the "black-box" characteristics of neural networks that impact interpretability and safety certification, and computational limits that get in the way of real-time processing in embedded systems. The paper then brings these points together to provide a roadmap for future research, looking into trends such as Explainable AI (XAI) and neuromorphic computing to build more intelligent and reliable autonomous systems.

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