SSLA BASED TRAFFIC SIGN AND LANE DETECTION FOR AUTONOMOUSCARS

Main Article Content

Choul Praveen Kumar
B Athreya
G Pragna
Swaroopa Naga

Abstract

The Self-Driving Cars are also known as Autonomous Vehicles. This Car has the ability to sense around the environment. These sensed parameters are processed and according to it the different actuators in the car will work without any human involvement. An Autonomous car work like a normal car but without any human driver. Autonomous cars rely on sensors, actuators, machine learning algorithms and Software to perform all the Automated Functions. The Software part is very important for Autonomous vehicles. The Software architecture acts as a bridge between Hardware Components and Application. The Standardized Software for Automotive cars is AUTOSAR. The AUTOSAR is a Standardized Architecture between Application Software and Hardware. This Standardized Architecture provide all Communication Interfaces, Device Drivers, Basic Software and Run-Time Environment. There are two important modules in Self-Driving Cars. They are Lane Detection and Traffic Signal detection which works automatically without any Human Intervention. A Machine Learning Algorithm is proposed in this paper. This Algorithm is mainly used to train the shape models and helps to detect the shape for Traffic Sign detection and Lane Detection. These both tasks are programmed using python with Open cv2 library file, numpy library file and Hough Detection technique is used to detect the appropriate circles of the traffic signals.By using all these tools, all the shape models are trained using Supervised training Algorithm and the detection is performed in such a way to help Autonomous cars to detect the lane and traffic Sign.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
Kumar, C. P., Athreya, B. ., Pragna, G. ., & Naga, S. . (2018). SSLA BASED TRAFFIC SIGN AND LANE DETECTION FOR AUTONOMOUSCARS. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 9(3), 1323–1326. https://doi.org/10.61841/turcomat.v9i3.14478
Section
Research Articles

References

K. Bimbraw, "Autonomous cars: Past, present

and future a review of the developments in the

last century, the present scenario and the

expected future of autonomous vehicle

technology," 2015 12th International Conference

on Informatics in Control, Automation and

Robotics (ICINCO), Colmar, France, 2015, pp.

-198.

Maro S, Anjorin A, Wohlrab R,

Steghöfer JP. “Traceability maintenance: factors

and guidelines.” International Conference on

Automated Software Engineering (pp. 414-425),

J. Zabavnik, A. Riel, M. Marguč, M.

Rodič.” Knowledge and skills requirements for

the software design and testing of automotive

applications”2020

Pawan Deshpande, "Road Safety and

Accident Prevention in India: A review", Int J

Adv Engg Tech., vol. V, no. II, pp. 64-68, AprilJune 2014.

A. R. Fayjie, S. Hossain, D. Oualid and

D. Lee, "Driverless Car: Autonomous Driving

Using Deep Reinforcement Learning in Urban

Environment," 2018 15th International

Conference on Ubiquitous Robots (UR),

Honolulu, HI, USA, 2018, pp. 896-901, doi:

1109/URAI.2018.8441797.

A. Agafonov and A. Yumaganov, "3D

Objects Detection in an Autonomous Car

Driving Problem," 2020 International

Conference on Information Technology and

Nanotechnology (ITNT), Samara, Russia, 2020,

pp. 1-5,doi: 10.1109/ITNT49337.2020.9253253.

R. Hussain and S. Zeadally,

"Autonomous Cars: Research Results, Issues,

and Future Challenges," in IEEE

Communications Surveys & Tutorials, vol. 21,

no. 2, pp.1275-

,Secondquarter2019,doi:10.1109/COMST.2

2869360.

M. Ikhlayel, A. J. Iswara, A. Kurniawan,

A. Zaini and E. M. Yuniarno, "Traffic Sign

Detection for Navigation of Autonomous Car

Prototype using Convolutional Neural

Network," 2020 International Conference on

Computer Engineering, Network, and Intelligent

Multimedia (CENIM), Surabaya, Indonesia,

, pp. 205-210, doi:

1109/CENIM51130.2020.9297973.

A. Josef Mík and B. P. Bouchner,

"Safety of crews of autonomous cars," 2020

Smart City Symposium Prague (SCSP), Prague,

Czech Republic, 2020, pp. 1-5, doi:

1109/SCSP49987.2020.9133942.

Sandhya, D. R., Sivakumar, P., &

Balaji, R. (2019). AUTOSAR Architecture

Based Kernel Development for Automotive

Application. In International Conference on

Intelligent Data Communication Technologies

and Internet of Things (ICICI) 2018. ICICI 2018

(Vol. 26). Springer.

. Sivakumar, P., Devi, R. S.,

Buvanesswaran, A. D., Kumar, B. V., Raguram,

R., & Ranjithkumar, M. (2020, July). ModelBased Testing of Car Engine Start/Stop Button

Debouncer Model. In 2020 Second International

Conference on Inventive Research in Computing

Applications (ICIRCA) (pp. 1077-1082). IEEE.

Sivakumar, P., RS Sandhya Devi, A.

Neeraja Lakshmi, B. VinothKumar, and B.

Vinod. "Automotive Grade Linux Software Architecture for Automotive Infotainment

System." In 2020 International Conference on

Inventive Computation Technologies (ICICT),

pp. 391-395. IEEE, 2020.

Sivakumar, P., Devi, R. S.,

Buvanesswaran, A. D., Kumar, B. V., Raguram,

R., & Ranjithkumar, M. (2020, July). ModelBased Testing of Car Engine Start/Stop Button

Debouncer Model. In 2020 Second International

Conference on Inventive Research in Computing

Applications (ICIRCA) (pp. 1077-1082). IEEE

Yuen, Kum Fai, Yiik Diew Wong, Fei

Ma, and Xueqin Wang. "The determinants of

public acceptance of autonomous vehicles: An

innovation diffusion perspective." Journal of

Cleaner Production 270 (2020): 121904.

Saini, S., Nikhil, S., Konda, K. R.,

Bharadwaj, H. S., & Ganeshan, N. (2017, June).

An efficient vision-based traffic light detection

and state recognition for autonomous vehicles.

In 2017 IEEE Intelligent Vehicles Symposium

(IV) (pp. 606-611). IEEE.