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Authors: Varshit Dubey ; Ruhshad Kasad and Karan Agrawal

Affiliation: Department of Electronics and Telecommunication Engineering, College of Engineering Pune, Pune, India

Keyword(s): Autonomous Systems, Collision Avoidance System, Deep Reinforcement Learning, Naturalistic Driving, Simulation.

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 get s out of the emergency situation and abides by the speed regulations, i.e the system resembles human driving. (More)

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 173-180. DOI: 10.5220/0010157401730180

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Dubey, V.
AU - Kasad, R.
AU - Agrawal, K.
PY - 2021
SP - 173
EP - 180
DO - 10.5220/0010157401730180
PB - SciTePress