Abstract
Self-driving cars or vehicles don’t require humans to take control to safely operate the vehicle. Also, popular as autonomous or driverless cars, they combine various sensors and software like radar, lidar, sonar, GPS, odometry, etc. to control, navigate, and drive the vehicle. This chapter presents the working model of the Self-driving Car: Lane detection and Collision prevention system (LDCP System) using machine learning and computer vision. In the LDCP System two processes working simultaneously based on object detection and recognition and human facial expression recognition to hold the responsibility of the car’s outer and inner environment to prevent any collision which occurs due to human errors such as lack of concentration or focus. The extraction of information about objects and face(s) is performed by capturing the live video feed through multiple cameras. The implemented algorithm is advanced enough to works in real-time which is for sure can prevent any collisions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Krok, A.: Apple increases self-driving test fleet from 3 to 27. Roadshow (2018)
Hall, Z.: Apple ramping self-driving car testing, more CA permits than Tesla and Uber. Electrek. Retrieved 21 March 2018 (2018)
https://www.vedomosti.ru/technology/news/2018/02/16/751267-yandeks-bespilotnoe
"China’s first Level 4 self-driving shuttle enters volume production”. newatlas.com
LLC, Baidu USA (4 July 2018). Baidu Joins Forces with Softbank’s SB Drive, King Long to Bring Apollo-Powered Autonomous Buses to Japan. GlobeNewswire News Room
Nawal, A., et al.: Driver’s fatigue detection based on yawning extraction. International Journal of Vehicular Technology (2014)
Soni, A., Dharmacharya, D., Pal, A., Srivastava, V.K., Shaw, R.N., Ghosh, A.: Design of a machine learning-based self-driving car. In: Bianchini, M., Simic, M., Ghosh, A., Shaw, R.N. (eds.) Machine Learning for Robotics Applications. SCI, vol. 960, pp. 139–151. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-0598-7_11
Jabbar, R., et al.: Real-time driver drowsiness detection for android application using deep neural networks techniques. The 9th International Conference on Ambient Systems, Networks, and Technologies, 2018., Procedia Computer Science, Volume 130, pp. 400–407 (2018). https://doi.org/10.1016/j.procs.2018.04.060
Biswas, S., Bianchini, M., Shaw, R.N., Ghosh, A.: Prediction of traffic movement for autonomous vehicles. In: Bianchini, M., Simic, M., Ghosh, A., Shaw, R.N. (eds.) Machine Learning for Robotics Applications. SCI, vol. 960, pp. 153–168. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-0598-7_12
Nair, V., Charniya, N.: Drunk driving and drowsiness detection alert system. Int. Conference on ISMAC in Computational Vision and Bio-Eng. 3, 1191–1207 (2019)
Jain, A.K.: Working model of self-driving car using convolutional neural network, Raspberry Pi and Arduino. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, Coimbatore, India (2018)
Dai, J.-M., Wu, L.-T., Liny, H.-Y., Tai, W.-L.: A driving assistance system with vision-based vehicle detection techniques. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, Jeju, South Korea (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Singh, N., Srivastava, M., Mohan, S., Ali, A., Singh, V.K., Singh, P. (2023). Self-driving Car: Lane Detection and Collision Prevention System. In: Shaw, R.N., Paprzycki, M., Ghosh, A. (eds) Advanced Communication and Intelligent Systems. ICACIS 2022. Communications in Computer and Information Science, vol 1749. Springer, Cham. https://doi.org/10.1007/978-3-031-25088-0_49
Download citation
DOI: https://doi.org/10.1007/978-3-031-25088-0_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25087-3
Online ISBN: 978-3-031-25088-0
eBook Packages: Computer ScienceComputer Science (R0)