Autonomous driving paper index
A Deep Learning Based Implementation for Self-Driving Car
One-line summary
This paper proposes the self-driving car's architecture and its software components that have been solved in FPT's contest.
Engineering notes
Key topics: autonomous driving, self-driving car, self-driving, lane detection. 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
Today, self-driving cars are a part of our life. It has received much attention in recent years. Many big companies and developers have invested a lot in this area and developed their own autonomous driving car platforms. The intriguing area of self-driving car motivates us to build a self-driving platform. This paper proposes the self-driving car's architecture and its software components that have been solved in FPT's contest. Lane detection in different environmental conditions, dodging obstacles, and detecting traffic signs. In this competition, the vehicle is equipped with limited hardware such as a single low-cost camera, an Nvidia Jetson TX2 board. We analyze the results obtained in the game in the simulator. We see that our method has overcome limited hardware but still achieved good results in complex problems. The final product has been used to compete in the Digital Race competition 2020 - a competition held annually by FPT Corporation.
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