Test Scenario for Road Sign Recognition Systems with Special Attention on Traffic Sign Anomalies

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.

[1]  Zsolt Szalay,et al.  Technical Specification Methodology for an Automotive Proving Ground Dedicated to Connected and Automated Vehicles , 2017 .

[2]  Kuei-Shu Hsu,et al.  Simulation and application of cooperative driving sense systems using prescan software , 2018, Microsystem Technologies.

[3]  Máté Zöldy Legal Barriers of Utilization of Autonomous Vehicles as Part of Green Mobility , 2018, Proceedings of the 4th International Congress of Automotive and Transport Engineering (AMMA 2018).

[4]  Domen Mongus,et al.  Detection of Ground in Point-clouds Generated from Stereo-pair Images , 2015, Informatica.

[5]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[6]  Dennis Saleh Zs , 2001 .

[7]  Jacopo Guanetti,et al.  Hardware-In-the-Loop for Connected Automated Vehicles Testing in Real Traffic , 2019, ArXiv.

[8]  Árpád Barsi,et al.  An offline path planning method for autonomous vehicles , 2018 .

[9]  Feng Gao,et al.  Test Scenario Design for Intelligent Driving System Ensuring Coverage and Effectiveness , 2018, International Journal of Automotive Technology.

[10]  Viktor Tihanyi,et al.  Offline path planning of automated vehicles for slow speed maneuvering , 2018, 2018 IEEE 18th International Symposium on Computational Intelligence and Informatics (CINTI).

[11]  Peter Drgona,et al.  Utilization of modern sensors in autonomous vehicles , 2018, 2018 ELEKTRO.

[12]  István Varga,et al.  Critical features of autonomous road transport from the perspective of technological regulation and law , 2017 .

[13]  Biagio Ciuffo,et al.  From connected vehicles to a connected, coordinated and automated road transport (C2ART) system , 2017, 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

[14]  Samah Mansour,et al.  DSRC Based Sensor-Pooling Protocol for Connected Vehicles in Future Smart Cities , 2018 .

[15]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[16]  Wolfgang Hirschberg,et al.  Potentials of Active Safety and Driver Assistance Systems , 2011 .

[17]  Viktor Tihanyi,et al.  Comparison of path following controllers for autonomous vehicles , 2019, 2019 IEEE 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI).