N 2 C : Neural Network Controller Design Using Behavioral Cloning

TL;DR

This paper introduces a neural network controller using behavioral cloning for autonomous vehicles, demonstrating improved accuracy and reliability over traditional methods through real-time and dataset evaluations.

Contribution

The study presents a novel neural network-based controller ($N^2C$) trained with manual driving data, and an end-to-end neural network for speed and steering prediction from images.

Findings

$N^2C$ outperforms PID controllers in accuracy.

The end-to-end network predicts speed and steering reliably.

Frameworks show better metrics and real-time performance.

Abstract

Modern vehicles communicate data to and from sensors, actuators, and electronic control units (ECUs) using Controller Area Network (CAN) bus, which operates on differential signaling. An autonomous ECU responsible for the execution of decision commands to an autonomous vehicle is developed by assimilating the information from the CAN bus. The conventional way of parsing the decision commands is motion planning, which uses a path tracking algorithm to evaluate the decision commands. This study focuses on designing a robust controller using behavioral cloning and motion planning of autonomous vehicle using a deep learning framework. In the first part of this study, we explore the pipeline of parsing decision commands from the path tracking algorithm to the controller and proposed a neural network-based controller ($N^2C$) using behavioral cloning. The proposed network predicts throttle, brake, and torque when trained with the manual driving data acquired from the CAN bus. The efficacy of the proposed method is demonstrated by comparing the accuracy with the Proportional-Derivative-Integral (PID) controller in conjunction with the path tracking algorithm (pure pursuit and model predictive control based path follower). The second part of this study complements $N^2C$, in which an end-to-end neural network for predicting the speed and steering angle is proposed with image data as an input. The performance of the proposed frameworks are evaluated in real-time and on the Udacity dataset, showing better metric scores in the former and reliable prediction in the later case when compared with the state-of-the-art methods.