Road Identification using Convolutional Neural Network on Autonomous Electric Vehicle

被引:0
作者
Hermawan, Markus [1 ]
Husin, Zaenal [1 ]
Hikmarika, Hera [1 ]
Dwijayanti, Suci [1 ]
Suprapto, Bhakti Yudho [1 ]
机构
[1] Sriwijaya Univ, Fac Engn, Dept Elect Engn, Ogan Ilir 30662, Sumatera Selata, Indonesia
来源
2021 8TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, COMPUTERSCIENCE AND INFORMATICS (EECSI) 2021 | 2021年
关键词
Autonomous Electric Vehicle; CNN; Road Identification;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Research in the field of autonomous electric vehicle has growth rapidly since they can overcome traffic accidents due to human error. Currently, the method used to identify the road for an autonomous electric vehicle is not in real-time. Thus, this study proposed a method for the autonomous electric vehicle to follow a predetermined route by identifying the road using the Convolutional Neural Network (CNN) as input of the steering control system. The optimal CNN model was obtained using an optimizer of Stochastic Gradient Descent with 150 epoch optimizer that was then used in simulation testing and real-time testing. In simulation testing, from 15 trials conducted, the percentage of success was 93.333%. The success rate to transmit the data from the system to the tool in a real-time manner is 100%. In real-time testing, the autonomous electric vehicle was successfully able to follow the predetermined route accurately. However, the autonomous electric vehicle has not succeeded in avoiding the object in front of it due to the lack of precise steering mechanics and the lack of variation in training data from various conditions that may be passed by the autonomous electric vehicle.
引用
收藏
页码:341 / 346
页数:6
相关论文
共 15 条
  • [1] Alhamdi M. R, 2020, DETEKSI JALAN SEKITA
  • [2] Breach D, CONVOLUTION NEURAL N
  • [3] Chop YG, 2015, INT CONF UBIQ ROBOT, P163, DOI 10.1109/URAI.2015.7358855
  • [4] Recognition of traffic signs by convolutional neural nets for self-driving vehicles
    Farag, Wael
    [J]. INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS, 2018, 22 (03) : 205 - 214
  • [5] Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
  • [6] Jia He, 2010, Proceedings 2010 International Conference on Optoelectronics and Image Processing (ICOIP 2010), P28, DOI 10.1109/ICOIP.2010.307
  • [7] Katole A. L., 2015, COMPUT SCI INF TECHN, P77, DOI [10.5121/csit.2015.51408, DOI 10.5121/CSIT.2015.51408]
  • [8] Robust lane detection based on convolutional neural network and random sample consensus
    Kim, Jihun
    Lee, Minho
    [J]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8834 : 454 - 461
  • [9] Convolutional Neural Network with Biologically Inspired Retinal Structure
    Kim, Jonghong
    O, Sangjun
    Kim, Yoonnyun
    Lee, Minho
    [J]. 7TH ANNUAL INTERNATIONAL CONFERENCE ON BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES, (BICA 2016), 2016, 88 : 145 - 154
  • [10] Liu B., 2019, P 3 INT C MECH ENG I, VVolume 87, P696, DOI 10.2991/icmeit-19.2019.111