Convolutional Neural Network for a Self-Driving Car in a Virtual Environment

Engineering notes

Key topics: autonomous driving, self-driving car, self-driving, autonomous vehicle, end-to-end. 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

Convolutional neural networks (CNNs) are machine learning models accomplishing state of the art results in a variety of computer vision tasks, decision making and visual recognition. For a long time, traditional computer vision based algorithms has been the primary method for analyzing camera footage, used for assisting safety functions, where decision making have been a product of manually constructed behaviors. During the last few years deep learning has showed its extraordinary capabilities for both visual recognition and decision making in end-to-end systems. this paper proposes a solution of introducing redundancy by combining deep learning methods with traditional computer vision based techniques for minimizing unsafe behavior in autonomous vehicles. A CNN has been trained to map raw pixels from a single front-facing camera directly to steering commands. The objective was to build a simple and reliable algorithm for a self-driving car and to implement a system that allow autonomous driving.

Links and sources

• Official / arXiv page

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