Abstract
This work presents a driver assistance system to detect brake and parking signals. Vehicle present ahead of the host vehicle performs sudden action such as applying brakes. The host vehicle’s driver must respond in real time to avoid accidents or collisions. Detection of action performed by a leading vehicle using its taillights is implemented with computer vision and machine learning techniques. Features are extracted using scale invariant feature transform (SIFT) and accelerated-KAZE (AKAZE). The dimensions are reduced using K-means clustering and then by principal component analysis (PCA). Five classification models have been trained to evaluate the performance. Random forest classifier provided the highest accuracy of 82% among all classifiers. Voting classifier provides final prediction based on five classification model’s output. It provided 81% accuracy.
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
Similar content being viewed by others
References
M. Alsfasser, M. Meuter, A. Kummert, Combinatorial use of optical tracker, Gaussian mixture PHD and group tracking for vehicle light tracking, in IEEE Intelligent Vehicles Symposium (2019), pp. 410–416
P. Bogacki, R. Dlugosz, Selected methods for increasing the accuracy of vehicle lights detection, in 24th International Conference on Methods and Models in Automation and Robotics (2019), pp. 227–231
L. Borelli, M. Kempken, Drowsy driving statistics and facts (2022). [Online]. https://www.bankrate.com/insurance/car/drowsy-driving-statistics/. Accessed 5 May 2022
D.-Y. Chen, Y.-H. Lin, Y.-J. Peng, Nighttime brake-light detection by Nakagami imaging. IEEE Trans. Intell. Transp. Syst. 13(4), 1627–1637 (2012)
D. Chen, Y. Peng, L. Chen, J. Hsieh, Nighttime turn signal detection by scatter modeling and reflectance-based direction recognition. IEEE Sens. J. 14(7), 2317–2326 (2014)
H.-T. Chen, Y.-C. Wu, C.-C. Hsu, Daytime preceding vehicle brake light detection using monocular vision. IEEE Sens. J. 16(1), 120–131 (2015)
L. Chen, X. Hu, T. Xu, H. Kuang, Q. Li, Turn signal detection during nighttime by CNN detector and perceptual hashing tracking. IEEE Trans. Intell. Transp. Syst. 18(12), 3303–3314 (2017)
Z. Cui, S.-W. Yang, H.-M. Tsai, A vision-based hierarchical framework for autonomous front-vehicle taillights detection and signal recognition, in IEEE 18th International Conference on Intelligent Transportation Systems (IEEE, 2015), pp. 931–937
P. Dave, N.M. Gella, N. Saboo, A. Das, A novel algorithm for night time vehicle detection even with one non-functional taillight by CIOF (color inherited optical flow), in International Conference on Pattern Recognition Systems (2016), pp. 1–6
S. Gupta, Y.J. Singh, M. Kumar, Object detection using multiple shape-based features, in Fourth International Conference on Parallel, Distributed and Grid Computing (2016), pp. 433–437
A. Humnabadkar, O. Kulkarni, P.K. Rajani, Automation of vehicle identification at night using light source recognition, in 5th International Conference on Computing, Communication, Control and Automation (2019), pp. 1–4
J.-D. Lee, J.-T. Wu, C.-H. Hsieh, J.-C. Chien, Close range vehicle detection and tracking by vehicle lights, in 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (2014), pp. 381–386
Q. Li, S. Garg, J. Nie, X. Li, R.W. Liu, Z. Cao, M.S. Hossain, A highly efficient vehicle taillight detection approach based on deep learning. IEEE Trans. Intell. Transp. Syst. 22(7), 4716–4726 (2020)
R. O’Malley, M. Glavin, E. Jones, Vehicle detection at night based on tail-light detection, in 1st International Symposium on Vehicular Computing Systems, Trinity College Dublin (2008)
R. O’Malley, E. Jones, M. Glavin, Rear-lamp vehicle detection and tracking in low-exposure color video for night conditions. IEEE Trans. Intell. Transp. Syst. 2, 453–462 (2011)
S.S. Pillai, B. Radhakrishnan, L.P. Suresh, Detecting tail lights for analyzing traffic during night using image processing techniques, in International Conference on Emerging Technological Trends (2016), pp. 1–7
C.S. Pradeep, R. Ramanathan, An improved technique for night-time vehicle detection, in International Conference on Advances in Computing, Communications, and Informatics (2018), pp. 508–513
M. Qing, K.-H. Jo, A novel particle filter implementation for a multiple-vehicle detection and tracking system using tail light segmentation. Int. J. Control Autom. Syst. 11(3), 577–585 (2013)
Reliance General Insurance, Common causes of road accidents in India (2022). [Online]. https://www.reliancegeneral.co.in/Insurance/Knowledge-Center/Blogs/Common-Causes-of-Road-Accidents-in-India.aspx. Accessed 5 May 2022
M. Rezaei, M. Terauchi, R. Klette, Robust vehicle detection and distance estimation under challenging lighting conditions. IEEE Trans. Intell. Transp. Syst. 16(5), 2723–2743 (2015)
Y. Saito, M. Itoh, T. Inagaki, Driver assistance system with a dual control scheme: effectiveness of identifying driver drowsiness and preventing lane departure accidents. IEEE Trans. Hum.-Mach. Syst. 46(5), 660–671 (2016)
Synopsys, What is ADAS (Advanced Driver Assistance Systems) (2022). [Online]. https://www.synopsys.com/automotive/what-is-adas.html. Accessed 5 May 2022
J.-G. Wang, L. Zhou, Y. Pan, S. Lee, Z. Song, B.S. Han, V.B. Saputra, Appearance-based brake-lights recognition using deep learning and vehicle detection, in IEEE Intelligent Vehicles Symposium (2016), pp. 815–820
T. Weis, M. Mundt, P. Harding, V. Ramesh, Anomaly detection for automotive visual signal transition estimation, in IEEE 20th International Conference on Intelligent Transportation Systems (2017), pp. 1–8
J. Zhang, J. Oh, J. Kim, Night time vehicle detection by using color information based on tail-light, in 15th International Conference on Control, Automation and Systems (2015), pp. 1724–1727
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 Singapore Pte Ltd.
About this paper
Cite this paper
Bhatlawande, S., Mhamane, V., Pande, A., Parbalkar, A., Shilaskar, S. (2023). Driver Assistance System for Recognition of Brake and Parking Signal. In: Reddy, A.B., Nagini, S., Balas, V.E., Raju, K.S. (eds) Proceedings of Third International Conference on Advances in Computer Engineering and Communication Systems. Lecture Notes in Networks and Systems, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-19-9228-5_21
Download citation
DOI: https://doi.org/10.1007/978-981-19-9228-5_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9227-8
Online ISBN: 978-981-19-9228-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)