Comparison of Deep Learning Models in Pothole Avoidance for Self-Driving Car

Engineering notes

Key topics: self-driving car, self-driving, end-to-end, 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

Self-driving car is one of the automotive innovation technologies that uses a computerized system to control a car without human assistance. Big manufactures have been developing this innovation into the fifth level autonomous technology. This study contributes to create a new system as innovation for this self-driving car to avoid road hazards and potholes. This paper reports the result of the conducted experiment on how the self-driving model is able to avoid potholes using end-to-end approach with Convolutional Neural Network (CNN) as a driving simulator called AirSim. Three different CNN models were tested to compare their performance. The result indicates that all the models were able to evade the road hazards.

Links and sources

• Official / arXiv page

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