Autonomous driving paper index
A Combination of Feedback Control and Vision-Based Deep Learning Mechanism for Guiding Self-Driving Cars
One-line summary
In this paper, we implemented a self-driving car which can drive itself on the track of a simulator.
Engineering notes
Key topics: self-driving car, self-driving, control. 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
The purpose of this paper is to develop an agent that can imitate the behavior of humans driving a car. When human beings driving a car, he/she majorly uses vision system to recognize the states of the car, including the position, velocity, and the surrounding environments. In this paper, we implemented a self-driving car which can drive itself on the track of a simulator. The self-driving car uses deep neural network as a computational framework to "learn" what is the position of the car related to the road. While the car understands the position of itself related to the track, it can use the information as a basis for feedback control.
Links and sources
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