Autonomous driving paper index
Self Driving Car using Deep Learning Technique
One-line summary
— The biggest challenge of a self-driving car is autonomous lateral motion so the main aim of this paper is to clone drives for better performance of the autonomous car for which we are using multilayer neural networks and deep learning techniques.
Engineering notes
Key topics: self-driving car, self-driving. 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 biggest challenge of a self-driving car is autonomous lateral motion so the main aim of this paper is to clone drives for better performance of the autonomous car for which we are using multilayer neural networks and deep learning techniques. We will focus to achieve autonomous cars driving in stimulator conditions. Within the simulator, preprocessing the image obtained from the camera placed in the car imitate the driver’s vision and then the reaction, which is the steering angle of the car. The neural network trains the deep learning technique on the basis of photos taken from a camera in manual mode which provides a condition for running the car in autonomous mode, utilizing the trained multilayered neural network. The driver imitation algorithm fabricated and characterized in the paper is all about the profound learning technique that is centered around the NVIDIA CNN model.
Links and sources
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