Review on self-driving cars using neural network architectures

Engineering notes

Key topics: self-driving car, self-driving, path planning, waymo, tesla, planning. 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

A self-driving automobile is one that can sense its environment and navigate obstacles like traffic and other vehicles on its own with little to no human intervention. Although it has been debated and worked on for a very long time, Tesla was able to produce this cutting-edge technology, which is currently being used in the automotive sector. These cars started to appear in other markets in recent years as both private and public transportation (taxis etc.). In this product development, numerous businesses are involved, including Waymo, UBER, Nissan, and Nvidia. With this kind of vehicle, the efficiency, safety, and ability to reduce human error in all aspects of automobile transportation are all improved, and driving is made as safe as possible. This kind of system can revolutionize transportation for those with disabilities and support independent travel for the blind. In this article, the development of self-driving automobiles is briefly discussed. The deep learning techniques used for self-driving automobiles are thoroughly discussed in this review article. It focuses on current techniques for lane recognition, path planning, and traffic sign detection. Additionally, it covers the experimental findings related to each of the aforementioned techniques.

Links and sources

• Official / arXiv page
• PDF from original source

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