Self-Driving Car Navigation With Single-Beam LiDAR and Neural Networks Using JavaScript

Abstract

This paper presents the development of a system for simulating and visualizing autonomous vehicle navigation using a single-beam LiDAR sensor integrated with an artificial neural network, implemented entirely in JavaScript. The single-beam LiDAR sensor in the simulation was configured to closely resemble real-world counterparts, such as the YDLIDAR TG30. The proposed neural network architecture, consisting of four output neurons, processes data from ten distinct angular measurements of the LiDAR sensor to predict necessary steering and speed adjustments for autonomous navigation. Furthermore, we introduce an enhanced collision detection algorithm that improves upon traditional methods by providing more precise identification of line segment intersections. Implementing the entire system in JavaScript demonstrates the viability of utilizing web technologies for complex machine learning tasks, representing a significant advancement in autonomous vehicle research. Experimental results validate the effectiveness of this approach across various scenarios, emphasizing its potential for broader applications in cost-sensitive autonomous systems. This work may also serve as a valuable resource for researchers new to machine learning, contributing to greater accessibility in the development of autonomous vehicles.

Keywords

• Self-driving car
• single-beam LiDAR
• deep feedforward neural network
• JavaScript
• autonomous navigation
• obstacle detection