On Self Driving Cars: An LED Time of Flight (ToF) based Detection and Ranging using various Unipolar Optical CDMA Codes

The dramatic surge in the development of autonomous vehicles has generated a need for, among others, improving detection and ranging accuracy. Various technologies and techniques have been proposed and adopted for autonomous vehicles, such as computer vision, in conjunction with machine learning and deep learning - neural network. Despite many attempts to improve the accuracy of detection and ranging, the computational load in training the neural network due to the requirement of massive amount of data had increased to a significant extent, along with the cost of operation. Light Detection and Ranging (LiDAR) systems are a new approach to improve the detection and ranging accuracy. However, LED Detection and Ranging (LEDDAR) based systems have not been explored so far. Herein, we present an analysis of the method of detection and ranging using LED and determine its performance for different number of users using Unipolar Optical Code Division Multiple Access (OCDMA) Codes. This was done using probability of error vs. Signal- to - Noise Ratio (SNR) and Distance (in m) using misdetection and false alarm analysis for a given transmitted power of LED obeying the current standards. The method of LED based beamforming by deploying multiple LED's in an array is proposed to improve the detection and ranging accuracy. Finally, the results show that OCDMA based OOC codes show a low probability of error for a given SNR and Distance and outperformed other OCDMA techniques such as prime codes and hence indicate that OOC codes will be an optimal choice that can be coded in LED's for the use in self driving cars.