Test scenario for road sign recognition systems with special attention on traffic sign anomalies

被引:0
|
作者
Lengyel, Henrietta [1 ]
Szalay, Zsolt [1 ]
机构
[1] Budapest Univ Technol & Econ, Dept Automot Technol, Budapest, Hungary
关键词
road sign recognition test validation recognition system testing;
D O I
10.1109/cinti-macro49179.2019.9105238
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The accurate identification and recognition of horizontal and vertical traffic signs can cause problems for autonomous vehicles. That is why have checked anomalies of traffic signs in the topic. Because for the safely navigate an autonomous vehicle, it is necessary to have a very accurate environment recognition. However, there are many problems with the sensors recognitions ability. The current article presents a test using a previously created anomaly classification table. The table divides the signals into five anomalies groups that are real and can cause problems with the recognition systems. This article with the tests highlights the limit values and circumstances that make it challenging to identify the traffic sign. The environment, weather, and visibility conditions, as well as different traffic situations, are typical problems. The test was done on the ZalaZONE, SMART city track in Hungary. The study can further develop a critical test environment for traffic signs to help develop future autonomous vehicle systems. It can use for environment recognition systems tests and validation procedures.
引用
收藏
页码:193 / 198
页数:6
相关论文
共 50 条
  • [31] Traffic Sign Recognition Based on Semantic Scene Understanding and Structural Traffic Sign Location
    Min, Weidong
    Liu, Ruikang
    He, Daojing
    Han, Qing
    Wei, Qingting
    Wang, Qi
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (09) : 15794 - 15807
  • [32] Road Sign Detection and Recognition of Thai Traffic Based on YOLOv3
    Thipsanthia, Paitoon
    Chamchong, Rapeeporn
    Songram, Panida
    MULTI-DISCIPLINARY TRENDS IN ARTIFICIAL INTELLIGENCE, 2019, 11909 : 271 - 279
  • [33] A road traffic sign recognition method based on improved YOLOv5
    Shi, Lu
    Zhang, Haifei
    INTERNATIONAL JOURNAL OF SENSOR NETWORKS, 2025, 47 (01)
  • [34] Boosted road sign detection and recognition
    Chen, Sin-Yu
    Hsieh, Jun-wei
    PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2008, : 3823 - 3826
  • [35] An Attention Based YOLOv5 Network for Small Traffic Sign Recognition
    Chen, Yi
    Wang, Junfan
    Dong, Zhekang
    Yang, Yuxiang
    Luo, Qiang
    Gao, Mingyu
    2022 IEEE 31ST INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE), 2022, : 1158 - 1164
  • [36] Anchor-Free Traffic Sign Recognition Algorithm Based on Attention Model
    Chu Jinghui
    Huang Hao
    Lu Wei
    LASER & OPTOELECTRONICS PROGRESS, 2021, 58 (16)
  • [37] Towards Reliable Traffic Sign Recognition
    Hoeferlin, Benjamin
    Zimmermann, Klaus
    2009 IEEE INTELLIGENT VEHICLES SYMPOSIUM, VOLS 1 AND 2, 2009, : 324 - 329
  • [38] Traffic Sign Recognition by Fuzzy Sets
    Fleyeh, Hasan
    2008 IEEE INTELLIGENT VEHICLES SYMPOSIUM, VOLS 1-3, 2008, : 283 - 288
  • [39] Indian Traffic Sign Detection and Recognition
    Alam, Altaf
    Jaffery, Zainul Abdin
    INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH, 2020, 18 (01) : 98 - 112
  • [40] Indian Traffic Sign Detection and Recognition
    Altaf Alam
    Zainul Abdin Jaffery
    International Journal of Intelligent Transportation Systems Research, 2020, 18 : 98 - 112