Deep Vision Net: An AI-Based System for Dynamic Traffic Scene Reconstruction and Safety Prediction with Explainable AI
Keywords:
Dynamic traffic scene reconstruction, Multi-modal data fusion, XAI-based system, Safety prediction Deep learning techniquesAbstract
The research paper explores innovative, dynamic traffic scene reconstruction methodologies and multi-modal data fusion in safety-critical applications. Leveraging deep learning techniques, we propose an AI-based system capable of processing traffic images and LiDAR data to predict safety measures for connected vehicles. Our system utilizes the popular ResNet50 model and LSTM layers to create a DeepVisionNet model, enabling efficient multi-modal data fusion. To ensure comprehensive model training and address the limitations, we employ synthetic data generation techniques, which facilitate the analysis of various traffic scenarios. Through extensive experiments carried out by executing validation using real-world traffic data and connected vehicle simulations, we evaluate the performance and effectiveness of our AI-based system. Our results demonstrate superior accuracy, reliability, and interpretability compared to existing approaches in the literature. By providing interpretable safety recommendations by adopting Explainable-AI (XAI) approach to drivers and traffic management authorities, our system contributes significantly to road safety and traffic optimization. The AI-based system proves to be an invaluable asset for dynamic traffic scene reconstruction and multi-modal data fusion. It offers the potential to revolutionize the field of traffic analysis and safety prediction, providing a safer and more efficient transportation ecosystem.
Downloads
References
G. Xiao, J. Lee, Q. Jiang, H. Huang, M. Abdel-Aty, and L. Wang, “Safety improvements by intelligent connected vehicle technologies: A meta-analysis considering market penetration rates,” Accid. Anal. Prev., vol. 159, no. 106234, p. 106234, 2021.
M Bhavsingh, B.Pannalal, & K Samunnisa. (2022). Review: Pedestrian Behavior Analysis and Trajectory Prediction with Deep Learning. International Journal of Computer Engineering in Research Trends, 9(12), 263–268.
S. Jin, J. Yang, and Z. Liu, “Modeling and analysis of car-following for intelligent connected vehicles considering expected speed in helical ramps,” Sustainability, vol. 14, no. 24, p. 16732, 2022.
Nimma Mukesh Raghavendra, & Avinash Poojari. (2016). Weighing Parameters and Analysis of Congestion using Markov Chain Model. International Journal of Computer Engineering in Research Trends, 3(3), 143–148
Q. Xu, K. Li, J. Wang, Q. Yuan, Y. Yang, and W. Chu, “The status, challenges, and trends: an interpretation of technology roadmap of intelligent and connected vehicles in China (2020),” J. Intell. Connect. Veh., vol. 5, no. 1, pp. 1–7, 2022.
P. Zhi, R. Zhao, H. Zhou, Y. Zhou, N. Ling, and Q. Zhou, “Analysis on the development status of intelligent and connected vehicle test site,” Intell. and Converged Netw., vol. 2, no. 4, pp. 320–333, 2021.
H. Abbas and M. Q. Ismael, “Automated pavement distress detection using image processing techniques,” Eng. Technol. Appl. Sci. Res., vol. 11, no. 5, pp. 7702–7708, 2021.
X. Zhang and S. Xu, “Research on image processing technology of computer vision algorithm,” in 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL), 2020.
S.D, V. S., Ali, W., Gaurav, L., Sakinam , S., & M, B. (2023). A Systematic Analysis of Text Classification Overfitting Recommendation Methods. International Journal of Computer Engineering in Research Trends, 10(4), 188–198.
F. Dufaux, “Grand Challenges in Image Processing,” Front. Signal Process., vol. 1, 2021.
B. Shivayogi, N. C. Matasagara Dharmendra, A. M. Ramakrishna, and K. N. Subramanya, “Real-time traffic Sign Recognition using deep learning,” Pertanika J. Sci. Technol., vol. 31, no. 1, pp. 137–148, 2022.
J. E. Siegel, D. C. Erb, and S. E. Sarma, “A survey of the connected vehicle landscape—architectures, enabling technologies, applications, and development areas,” IEEE Trans. Intell. Transp. Syst., vol. 19, no. 8, pp. 2391–2406, 2018.
Giri, S. Dutta, S. Neogy, K. Dahal, and Z. Pervez, “Internet of Things (IoT) a survey on architecture, enabling technologies, applications and challenges,” in Proceedings of the 1st international conference on internet of things and machine learning, 2017, pp. 1–12.
Pandya Garvit Kalpesh. (2017). Distant Mission UAV capability with on-path charging to Increase Endurance, On-board Obstacle Avoidance and Route Re-planning facility. International Journal of Computer Engineering in Research Trends, 4(1), 10–14.
J. Guerrero-Ibáñez, S. Zeadally, and J. Contreras-Castillo, “Sensor technologies for intelligent transportation systems,” Sensors (Basel), vol. 18, no. 4, p. 1212, 2018.
G. P. Hancke, B. de C. E. Silva, and G. P. Hancke Jr, “The role of advanced sensing in smart cities,” Sensors (Basel), vol. 13, no. 1, pp. 393–425, 2012.
P. Xie, T. Li, J. Liu, S. Du, X. Yang, and J. Zhang, “Urban flow prediction from spatiotemporal data using machine learning: A survey,” Inf. Fusion, vol. 59, pp. 1–12, 2020.
Y. Lv, Y. Duan, W. Kang, Z. Li, and F.-Y. Wang, “Traffic flow prediction with big data: A deep learning approach,” IEEE Trans. Intell. Transp. Syst., pp. 1–9, 2014.
K. Ansari, “Joint use of DSRC and CV2X for V2X communications in the 5.9 GHz ITS band,” IET Intell. Transp. Syst., vol. 15, no. 2, pp. 213–224, 2021.
J. Khattak, A. Harris, M. Sartipi, I. Mahdinia, N. Moradloo, and M. SafariTaherkhani, “Connected and automated vehicles investment and smart infrastructure in Tennessee part 3: Infrastructure and vehicular communications: From Dedicated Short-Range communications to Cellular Vehicle-to-Everything,” arXiv [cs.CY], 2023.
P. Chawla et al., “Real-time traffic congestion prediction using big data and machine learning techniques,” World J. Eng., 2022.
K. Tian, G. Markkula, C. Wei, and R. Romano, “Decision model for pedestrian interacting with traffic at uncontrolled intersections,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022.
Y. Zhang, Y. Chen, R. Sun, N. Huynh, and G. Comert, “A surrogate approach for estimating vehicle-related emissions under heterogenous traffic conditions,” J. Air Waste Manag. Assoc., vol. 71, no. 6, pp. 778–789, 2021.
Khem, D. ., Panchal, S. ., & Bhatt, C. . (2023). An Overview of Context Capturing Techniques in NLP. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 193–198. https://doi.org/10.17762/ijritcc.v11i4s.6440
Kshirsagar, D. R. . (2021). Malicious Node Detection in Adhoc Wireless Sensor Networks Using Secure Trust Protocol. Research Journal of Computer Systems and Engineering, 2(2), 12:16. Retrieved from
https://technicaljournals.org/RJCSE/index.php/journal/article/view/26
Molla, J. P., Dhabliya, D., Jondhale, S. R., Arumugam, S. S., Rajawat, A. S., Goyal, S. B., Suciu, G. (2023). Energy efficient received signal strength-based target localization and tracking using support vector regression. Energies, 16(1) doi:10.3390/en16010555
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
