Simulating robotic cars using time-delay neural networks

Abstract

In this paper, we propose a simulator for robotic cars based on two time-delay neural networks. These networks are intended to simulate the mechanisms that govern how a set of effort commands changes the car's velocity and the direction it is moving. The first neural network receives as input a temporal sequence of current and previous throttle and brake efforts, along with a temporal sequence of the previous car's velocities (estimated by the network), and outputs the velocity that the real car would reach in the next time interval given these inputs. The second neural network estimates the arctangent of curvature (a variable related to the steering wheel angle) that a real car would reach in the next time interval given a temporal sequence of current and previous steering efforts and previous arctangents of curvatures of the car estimated by the network. We evaluated the performance of our simulator using real-world datasets acquired using an autonomous robotic car. Experimental results showed that our simulator was able to simulate in real time how a set of efforts influences the car's velocity and arctangent of curvature. While navigating in a map of a real-world environment, our car simulator was able to emulate the velocity and arctangent of curvature of the real car with mean squared error of 2.2×10-3 (m/s)2 and 4.0×10-5 rad2, respectively.