# LaksNet: an end-to-end deep learning model for self-driving cars in Udacity simulator

Source: [LaksNet: an end-to-end deep learning model for self-driving cars in Udacity simulator](<https://www.sota2.com/research/paper/68fa70f21e209131cea4e3bc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:54:43.381Z

Authors: Lakshmikar R. Polamreddy, Youshan Zhang

Published: 2023-10-24T18:11:25.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2310.16103>)

[PDF](<https://arxiv.org/pdf/2310.16103.pdf>)