TY - GEN
T1 - Obstacle Avoidance System on Autonomous Car Using D3QN
AU - Sahal, Mochammad
AU - Hidayat, Zulkifli
AU - Saputra, Firdaus Dheo
AU - Rizqifadiilah, Muhammad Azriel
AU - Putra, Resqi Abdurrazzaaq
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - An autonomous vehicle's triumphant and safe navigation, which can circumvent obstacles, necessitates a skill set encompassing steering wheel control, sometimes called obstacle avoidance. One potential approach to address this issue is using a simulation framework wherein an automobile is subjected to various barriers. During this simulation, the sensory input of the car, as well as its corresponding actions, are recorded and analyzed. An alternative approach involves allowing the vehicle to autonomously acquire knowledge to optimize its performance towards the desired objective. The Dueling Deep Double QNetworks (D3QN) approach is a strategy that enables the model to autonomously learn and optimize its performance to attain the most favorable conclusion. The D3QN architecture is a computational framework incorporating Dueling and double-Q processes. The implementation of D3QN is anticipated to result in a reduction in the training time required for an autonomous vehicle. This study is expected to substitute for training an autonomous vehicle.
AB - An autonomous vehicle's triumphant and safe navigation, which can circumvent obstacles, necessitates a skill set encompassing steering wheel control, sometimes called obstacle avoidance. One potential approach to address this issue is using a simulation framework wherein an automobile is subjected to various barriers. During this simulation, the sensory input of the car, as well as its corresponding actions, are recorded and analyzed. An alternative approach involves allowing the vehicle to autonomously acquire knowledge to optimize its performance towards the desired objective. The Dueling Deep Double QNetworks (D3QN) approach is a strategy that enables the model to autonomously learn and optimize its performance to attain the most favorable conclusion. The D3QN architecture is a computational framework incorporating Dueling and double-Q processes. The implementation of D3QN is anticipated to result in a reduction in the training time required for an autonomous vehicle. This study is expected to substitute for training an autonomous vehicle.
KW - Autonomous Car
KW - D3QN
KW - Deep Learning
KW - Neural Network.
KW - Obstacle Avoidance
UR - http://www.scopus.com/inward/record.url?scp=85180371260&partnerID=8YFLogxK
U2 - 10.1109/ICTS58770.2023.10330873
DO - 10.1109/ICTS58770.2023.10330873
M3 - Conference contribution
AN - SCOPUS:85180371260
T3 - 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023
SP - 199
EP - 204
BT - 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Information and Communication Technology and System, ICTS 2023
Y2 - 4 October 2023 through 5 October 2023
ER -