LaksNet: an end-to-end deep learning model for self-driving cars in Udacity simulator
This work addresses the problem of improving self-driving car safety in simulators, but it is incremental as it focuses on a specific simulator and model architecture.
The paper tackled the problem of building an efficient deep learning model for self-driving cars by proposing LaksNet, a convolutional neural network, and reported that it outperformed existing models in terms of driving duration without going off-track in the Udacity simulator.
The majority of road accidents occur because of human errors, including distraction, recklessness, and drunken driving. One of the effective ways to overcome this dangerous situation is by implementing self-driving technologies in vehicles. In this paper, we focus on building an efficient deep-learning model for self-driving cars. We propose a new and effective convolutional neural network model called `LaksNet' consisting of four convolutional layers and two fully connected layers. We conduct extensive experiments using our LaksNet model with the training data generated from the Udacity simulator. Our model outperforms many existing pre-trained ImageNet and NVIDIA models in terms of the duration of the car for which it drives without going off the track on the simulator.