Autonomous driving paper index
Deep Learning Model for Simulating Self Driving Car
One-line summary
Self-driving cars have become a trending subject with a significant improvement in the technologies in the last decade.
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
Self-driving cars have become a trending subject with a significant improvement in the technologies in the last decade. The project purpose is to train a convolutional neural network to drive an autonomous car agent on the tracks of Udacity’s Car Simulator environment. Udacity has released the simulator as an open source software. Driving a car in an autonomous manner requires learning to control steering angle, throttle and brakes. Behavioral cloning technique is used to mimic human driving behavior in the training mode on the track. That means a dataset is generated in the simulator by a user driven car in training mode, and the NVIDIA’s convolutional neural network model then drives the car in autonomous mode. Augmentation and image pre-processing are used to increase the accuracy of CNN model.
Links and sources
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