Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving

Varshit Dubey, Ruhshad Kasad, Karan Agrawal

2021

Abstract

Autonomous Braking and Throttle control is key in developing safe driving systems for the future. There exists a need for autonomous vehicles to negotiate a multi-agent environment while ensuring safety and comfort. A Deep Reinforcement Learning based autonomous throttle and braking system is presented. For each time step, the proposed system makes a decision to apply the brake or throttle. The throttle and brake are modelled as continuous action space values. We demonstrate 2 scenarios where there is a need for a sophisticated braking and throttle system, i.e when there is a static obstacle in front of our agent like a car, stop sign. The second scenario consists of 2 vehicles approaching an intersection. The policies for brake and throttle control are learned through computer simulation using Deep deterministic policy gradients. The experiment shows that the system not only avoids a collision, but also it ensures that there is smooth change in the values of throttle/brake as it gets out of the emergency situation and abides by the speed regulations, i.e the system resembles human driving.

Paper Citation

in Harvard Style

Dubey V., Kasad R. and Agrawal K. (2021). Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-484-8, pages 173-180. DOI: 10.5220/0010157401730180

in Bibtex Style

@conference{icaart21,
author={Varshit Dubey and Ruhshad Kasad and Karan Agrawal},
title={Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2021},
pages={173-180},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010157401730180},
isbn={978-989-758-484-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving
SN - 978-989-758-484-8
AU - Dubey V.
AU - Kasad R.
AU - Agrawal K.
PY - 2021
SP - 173
EP - 180
DO - 10.5220/0010157401730180