Finding better learning algorithms for self-driving cars: An overview of the LAOP platform

• Published in: 2019 International Symposium on Networks, Computers and Communications (ISNCC) pp. 1 - 6
• Main Authors: Rezgui, Jihene, Bisaillon, Clement, O'Leary, Leonard Oest
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2019
• Subjects: Automobiles; Autonomous automobiles; evolution; Laser radar; natural selection; Neural networks; Optimization; platform; Reliability; Roads; self-driving cars; Testing; Topology; Tuning
• DOI: 10.1109/ISNCC.2019.8909159

Cars are becoming more and more intelligent, embedded with a range of sensors to give them local perception of their environment (LIDARs, cameras, etc.). Trendy companies like Google and Tesla are actively testing cars on American roads that can drive without any human interaction [1]. Neural networks are the modern approach for autonomous cars. However, an inefficient neural network algorithm will make the learning process slower and will result in a less reliable autonomous vehicle. In this paper, we will introduce a platform built in JAVA named LAOP (Learning Algorithm Optimization Platform) [2] while explaining the solutions we found to make it easy for researchers to test and compare their own algorithms. Then, we will show how we have integrated a natural selection algorithm with a neural network in order to improve them. Moreover, we will demonstrate how the Fully Connected Neural Network and the NeuroEvolution of Augmenting Topologies (NEAT) [3] algorithms are implemented in the context of vehicular learning on LAOP. Finally, we will display the different results extracted from LAOP by tuning several various parameters such as the weight mutation chance and the car density in the simulation.