An Advance Driving Assistant System Simulator using Unity-3D
Keywords:
ADAS, simulations, forward collision detection, backward collision detection, signal detectionAbstract
Simulations are 3D environments that are based on real-world scenarios. As technology is evolving in automobiles, the need for advancement in Artificial Intelligence (AI) of automobiles and to train AI systems of the automobiles are getting expansive. This study investigates the problem of collecting reference data for the testing and evaluation algorithms for autonomous vehicles using unity simulation. Using computer simulations is one way to solve this issue. A genuine system modeled using computer simulations, including all of its static and dynamic properties. There are 4 modules: forward & backward collision, speed warning, traffic light & sign detection, and lane departure system. All the modules are implemented in C# language and the unity-3d platform. The composite collider module is used for the lane departure in which the line render algorithm is applied for forwarding/backward collision. This method reduces the testing period while ensuring accuracy and efficiency in data collection. The construction of a simulation environment based on Unity with the ability to test sensors and algorithms for autonomous cars and display deviations from reference data is the goal of this research work. Roads, sidewalks, buildings, traffic signs, and automobiles are among the common city players, and items in the simulation model are presented in the simulation. The simulation is tested by the methods like black box testing, use case testing, performance testing, stress testing, module testing, Software-In-the-loop, Driver-in-the loop testing.
References
P. M. Greenwood, J. K. Lenneman, and C. L. Baldwin, “Advanced driver assistance systems (ADAS): Demographics, preferred sources of information, and accuracy of ADAS knowledge,” Transportation research part F: traffic psychology and behaviour, vol. 86, pp. 131-150, 2022.
B. Ponton, M. Ferri, L. Koenig, and M. Bartels, “Efficient Extrinsic Calibration of Multi-Sensor 3D LiDAR Systems for Autonomous Vehicles using Static Objects Information,” arXiv preprint arXiv:2211.02614, 2022.
C. Ounoughi, and S. B. Yahia, “Data fusion for ITS: A systematic literature review,” Information Fusion, 2022.
X. Liu, and W. Q. Yan, “Vehicle-Related Distance Estimation Using Customized YOLOv7.”
W. Goldstone, Unity 3. x game development essentials: Packt Publishing Ltd, 2011.
S. Checkoway, D. McCoy, B. Kantor, D. Anderson, H. Shacham, S. Savage, K. Koscher, A. Czeskis, F. Roesner, and T. Kohno, "Comprehensive experimental analyses of automotive attack surfaces."
J. Amin, M. Sharif, G. A. Mallah, and S. L. Fernandes, “An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification,” Frontiers in Public Health, vol. 10, 2022.
N. Shaukat, J. Amin, M. Sharif, F. Azam, S. Kadry, and S. Krishnamoorthy, “Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning,” Journal of Personalized Medicine, vol. 12, no. 9, pp. 1454, 2022.
S. Saleem, J. Amin, M. Sharif, G. A. Mallah, S. Kadry, and A. H. Gandomi, “Leukemia segmentation and classification: A comprehensive survey,” Computers in Biology and Medicine, pp. 106028, 2022.
J. Amin, “Segmentation and Classification of Diabetic Retinopathy,” University of Wah Journal of Computer Science, vol. 2, no. 1, 2019.
J. Amin, M. Sharif, and M. Almas Anjum, "Skin lesion detection using recent machine learning approaches," Prognostic Models in Healthcare: AI and Statistical Approaches, pp. 193-211: Springer, 2022.
U. Yunus, J. Amin, M. Sharif, M. Yasmin, S. Kadry, and S. Krishnamoorthy, “Recognition of knee osteoarthritis (KOA) using YOLOv2 and classification based on convolutional neural network,” Life, vol. 12, no. 8, pp. 1126, 2022.
S. Malik, J. Amin, M. Sharif, M. Yasmin, S. Kadry, and S. Anjum, “Fractured Elbow Classification Using Hand-Crafted and Deep Feature Fusion and Selection Based on Whale Optimization Approach,” Mathematics, vol. 10, no. 18, pp. 3291, 2022.
J. Amin, M. A. Anjum, and M. Malik, “Fused information of DeepLabv3+ and transfer learning model for semantic segmentation and rich features selection using equilibrium optimizer (EO) for classification of NPDR lesions,” Knowledge-Based Systems, vol. 249, pp. 108881, 2022.
J. Amin, M. A. Anjum, M. Sharif, S. Jabeen, S. Kadry, and P. Moreno Ger, “A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier,” Computational Intelligence and Neuroscience, vol. 2022, 2022.
D. Sadaf, J. Amin, M. Sharif, and M. Yasmin, “Detection of Diabetic Foot Ulcer Using Machine/Deep Learning,” Advances in Deep Learning for Medical Image Analysis, pp. 101-123, 2000.
J. Amin, M. A. Anjum, A. Sharif, and M. I. Sharif, “A modified classical-quantum model for diabetic foot ulcer classification,” Intelligent Decision Technologies, no. Preprint, pp. 1-6.
K. Abdelgawad, M. Abdelkarim, B. Hassan, M. Grafe, and I. Gräßler, "A scalable framework for advanced driver assistance systems simulation." pp. 12-16.
L. Artal-Villa, and C. Olaverri-Monreal, "Vehicle-pedestrian interaction in SUMO and unity3D." pp. 198-207.
J. Amin, M. Sharif, M. A. Anjum, A. Siddiqa, S. Kadry, Y. Nam, and M. Raza, “3d semantic deep learning networks for leukemia detection,” 2021.
J. Amin, M. Sharif, M. A. Anjum, Y. Nam, S. Kadry, and D. Taniar, “Diagnosis of COVID-19 infection using three-dimensional semantic segmentation and classification of computed tomography images,” Computers, Materials and Continua, vol. 68, no. 2, pp. 2451-2467, 2021.
N. B. Chetan, J. Gong, H. Zhou, D. Bi, J. Lan, and L. Qie, "An overview of recent progress of lane detection for autonomous driving." pp. 341-346.
J. Cho, Y. Jung, D.-S. Kim, S. Lee, and Y. Jung, “Moving object detection based on optical flow estimation and a Gaussian mixture model for advanced driver assistance systems,” Sensors, vol. 19, no. 14, pp. 3217, 2019.
R. H. Creighton, Unity 3D game development by example: A Seat-of-your-pants manual for building fun, groovy little games quickly: Packt Publishing Ltd, 2010.
J. S. Gonçalves, J. Jacob, R. J. Rossetti, A. Coelho, and R. Rodrigues, "An integrated framework for mobile-based ADAS simulation," Modeling Mobility with Open Data, pp. 171-186: Springer, 2015.
S. Hossain, A. R. Fayjie, O. Doukhi, and D.-j. Lee, "CAIAS simulator: self-driving vehicle simulator for AI research." pp. 187-195.
J. M. Johnson, G. R. Joy, Y. Sindhu, and E. Joy, "Virtual 3D Game-on simulation: An immersive learning framework for assisted driving." pp. 1-5.
S. Sun, A. P. Petropulu, and H. V. Poor, “MIMO radar for advanced driver-assistance systems and autonomous driving: Advantages and challenges,” IEEE Signal Processing Magazine, vol. 37, no. 4, pp. 98-117, 2020.
X. Feng, and Y. Zhu, "Trace AI Simulation of Feedforward Neural Network Visualization Optimized by Genetic Algorithm Based on Unity3D." pp. 4934-4938.
L. Artal-Villa, A. Hussein, and C. Olaverri-Monreal, "Extension of the 3DCoAutoSim to Simulate Vehicle and Pedestrian Interaction based on SUMO and Unity 3D." pp. 885-890.
S. Eisele, M. Yamaura, N. Arechiga, S. Shiraishi, J. Hite, J. Scott, S. Neema, and T. Bapty, “ADAS virtual prototyping with the OpenMETA toolchain,” SAE International Journal of Passenger Cars-Electronic and Electrical Systems, vol. 9, no. 1, pp. 22-30, 2016.
R. Ayachi, M. Afif, Y. Said, and M. Atri, “Traffic signs detection for real-world application of an advanced driving assisting system using deep learning,” Neural Processing Letters, vol. 51, no. 1, pp. 837-851, 2020.
S. S. BV, and A. Karthikeyan, "Computer vision based advanced driver assistance system algorithms with optimization techniques-a review." pp. 821-829.
C. M. Martinez, M. Heucke, F.-Y. Wang, B. Gao, and D. Cao, “Driving style recognition for intelligent vehicle control and advanced driver assistance: A survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 3, pp. 666-676, 2017.