مروری بر فناوری های پیشرفته و چالش های سیستم های دستیار راننده- مقاله مروری
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمندمهدی سیفی پور 1 , محدثه پرویزی 2 , سیامک محمدی 3
1 - دانشجوی دکتری مهندسی کامپیوتر، دانشکده برق و کامپیوتر، دانشگاه تهران، تهران، ایران
2 - دانشجوی کارشناسی ارشد هوش مصنوعی، دانشگاه الزهرا، تهران، ایران
3 - دانشیار دانشکده برق و کامپیوتر، دانشگاه تهران، تهران، ایران
کلید واژه: فناوری‌های نوظهور, سیستم دستیار راننده, چالش‌ها, راهکارها,
چکیده مقاله :
سیستمهای دستیار رانندۀ پیشرفته (ADAS) به منظور افزایش ایمنی و راحتی در رانندگی توسعه یافته‌اند. این سیستم‌ها با استفاده از ترکیب داده‌ای سنسورهای مختلف از جمله رادار، لیدار، دوربین و سنسورهای آلتراسونیک، اطلاعات محیطی را جمع‌آوری و پردازش می‌کنند تا از وقوع تصادفات جلوگیری کرده و مسیر بهتر و امن‌تری را پیدا کنند. سیستمهای مرتبط با ADAS در دو نوع فعال و غیرفعال بررسی می‌شوند که در نوع غیرفعال، وظیفه سیستم ایجاد هشدارهای لازم برای راننده است درحالی که در انواع فعال، سیستم علاوه بر تصمیم‌گیری‌های صحیح به طور خودکار در شرایط خاص واکنش نشان می‌دهند. یک سیستم دستیار راننده به شش سطح مختلف تقسیم‌بندی می‌شود که با افزایش سطح انتزاع، میزان خودکار بودن سیستم افزایش یافته و در سطوح بالاتر سیستم به سمت رانندگی کاملاً خودکار پیش می‌رود. با وجود پیشرفت‌های چشمگیر در این زمینه، چالش‌هایی از جمله محدودیت سنسورها در شرایط جوی نامساعد، ترافیک‌های سنگین، مدیریت داده‌های سنگین، ابعاد تراشه‌های مورد استفاده، پاسخ سریع در مورد مسائل زمان واقعی و امنیت سایبری همچنان وجود دارد. الگوریتم‌های هوش مصنوعی و یادگیری عمیق نقش کلیدی در بهبود سیستمهای یادشده ایفامی‌کنند. این الگوریتم‌ها می‌توانند مسائلی چون شناسایی دقیق‌تر موانع، تشخیص اشیا، پیش‌بینی تغییرات محیطی - جاده‌ای و مدیریت داده‌های سنگین را حل کنند. با وجود این الگوریتم‌ها، سیستمهای دستیار رانندۀ پیشرفته در طول زمان با فرآیند یادگیری بهبودخواهدیافت. همچنین با تعامل بین خودرو و محیط و تعامل بین خودروها، هر وسیله نقلیه تجربه‌های خود را از طریق شبکه در اختیار سایر وسایل نقلیه قرار خواهد داد که این امر سبب یادگیری سریع‌تر و بهتر خواهدشد. ارتباط با سایر وسایل نقلیه و محیط، باعث روز بودن داده‌ها و وضعیت ترافیک جاده‌ای می‌شود که به پیشگیری از خطرات ناگهانی کمک می‌کند. این امر پاسخ در زمان واقعی را بهبود بخشیده و باعث کاهش تصادفات و بهبود جریان ترافیک خواهد شد. پژوهش حاضر یک مقاله مروری بوده که به بررسی تکنولوژی‌های نوظهور در سیستمهای دستیار رانندۀ پیشرفته می‌پردازد. همچنین چالش‌هایی مثل شرایط آب‌و‌هوایی نامساعد، حفظ امنیت سایبری، درک و پردازش داده‌های سنگین و استفاده از الگوریتم‌های هوش مصنوعی و یادگیری عمیق را بررسی می‌کند. پژوهش حاضر علاوه بر شناسایی این مشکلات راهکارهایی برای حل یا بهبود مسئله را معرفی می‌کند.
Abstract
Advanced Driver Assistance Systems (ADAS) have been developed to enhance driving safety and comfort. These systems collect and process environmental information using the data combination of various sensors, including radar, lidar, camera, and ultrasonic sensors, to prevent accidents and find a better and safer route. ADAS-related systems are investigated in two types: active and passive, in the passive type, the system's task is to create the necessary warnings for the driver, while in the active type, the system reacts to specific situations in addition to making correct decisions. A driver assistance system is divided into six different levels, which change as the level of abstraction increases, the degree of automation of the system changes, and at higher levels the system moves towards fully automated driving. Despite significant advancements in this field, challenges remain, including limitations of sensors in adverse weather conditions, heavy traffic, heavy data management, the size of the chips used, the issue of real-time rapid response, and cybersecurity. This review explores how using Deep learning and AI algorithms for enhance ADAS capabilities in data processing, obstacle detection and predictive analysis for better results.
Introduction: ADAS aims for better and safer driving by gathering sufficient data from the environment. Using sensor fusion technologies and enhancing the algorithms improved these systems over time. Regardless of these improvements, several challenges remain to be addressed. AI and deep learning algorithms play a key role in improving the aforementioned systems. These algorithms can solve problems such as more accurate identification of obstacles, object detection, prediction of other vehicles, pedestrians, or environmental changes, and heavy data management. By using these algorithms, advanced driver assistance systems will be improved over time using the learning process, and with the interaction between the car and the environment as well as between the cars, each vehicle will share its experiences with the other vehicles through the network, which will lead to faster and better learning. Communication with other vehicles and the environment causes up-to-date data and traffic conditions and prevents sudden hazards. This will improve real-time response and reduce accidents and improve traffic flow. The present study is a review article that examines emerging technologies in advanced driver assistance systems. It also examines challenges such as adverse weather conditions, maintaining cybersecurity, understanding and processing heavy data, and using artificial intelligence and deep learning algorithms.
Method: This study leverages a comprehensive review on existing ADAS technologies, challenges and potential solution. Additionally, this research highlights emerging technologies in ADAS development by comparing existing features with anticipated advancement.
Results: This review demonstrates the use of AI, deep learning algorithms and big data enhances ADAS tasks. This approach increases accuracy in obstacle detection, object recognition and predictive capabilities. This enhancement enables vehicles to predict the behaviors of other vehicles, road traffic, dynamic environment and pedestrians. Improved data processing techniques promise to manage large amount of data more effectively. Furthermore, real-time data sharing between vehicles and surrounding environment, improves real-time responsiveness and safety.
[1] Haas, R. E., Bhattacharjee, S., & Möller, D. P. (2020). Advanced driver assistance systems. Smart Technologies: Scope and Applications, 345-371.
[2] Galvani, M. (2019). History and future of driver assistance. IEEE Instrumentation & Measurement Magazine, 22(1), 11-16.
[3] Srinivasan, C., Sridhar, P., Raj, M., & Raj, S. (2023, June). Advanced Driver Assistance System (ADAS) in Autonomous Vehicles: A Complete Analysis. In 2023 8th International Conference on Communication and Electronics Systems (ICCES) (pp. 1501-1505). IEEE.
[4] Srinivasan, S., Rajegowda, R., & Udhayakumar, E. (2023). Latest Advancements in Perception Algorithms for ADAS and AV Systems Using Infrared Images and Deep Learning.
[5] Sharkawy, A. E., Yacoub, M., & Asfoor, M. (2022). Comprehensive evaluation of emerging technologies of advanced driver assistance systems: An overview. Journal of Engineering Science and Military Technologies, 6(1), 41-51.
[6] Li, X., Lin, K. Y., Meng, M., Li, X., Li, L., Hong, Y., & Chen, J. (2022). A survey of adas perceptions with development in china. IEEE Transactions on Intelligent Transportation Systems, 23(9), 14188-14203.
[7] Kankoriya, S., & Khatal, M. (2023). Advance Vehicle Advanced Driver Assistance Systems: Working & Features ADAS A Path towards Intelligent Vehicles. Travel With Virtual Reality.
[8] Kankoriya, S., & Khatal, M. (2024). Advanced Driver-Assistance Systems: Features Journey for Tomorrow. System, 4, 5.
[9] Jabbari, F., & Fallah Tafti, M. (2022). Evaluating the performance of intelligent traffic signals based on Firefly algorithm in an adaptive control system. Traffic and Transportation Engineering Journal, 3(3), 11-17.
[10] Chen, K. S., Yu, C. M., Lin, J. S., Huang, T. H., & Zhong, Y. S. (2024). Fuzzy Radar Evaluation Chart for Improving Machining Quality of Components. Mathematics, 12(5), 732.
[11] Seyfipour, M., Hajihaydari, E., Navidpour, R., & Mohammadi, S. (2024). Challenges and solutions in implementing object detection systems in advanced driver assistance systems (ADAS). Paper presented at the 22nd National Conference on Electrical, Computer, and Mechanical Engineering.
[12] Kocić, O., Simić, A., Bjelica, M. Z., & Maruna, T. (2016, November). Optimization of driver monitoring ADAS algorithm for heterogeneous platform. In 2016 24th Telecommunications Forum (TELFOR) (pp. 1-4). IEEE.
[13] Liu, Z., Cai, Y., Wang, H., & Chen, L. (2021). Surrounding objects detection and tracking for autonomous driving using LiDAR and radar fusion. Chinese Journal of Mechanical Engineering, 34, 1-12.
[14] Dimassi, S., Jemai, M., Ouni, B., & Mtibaa, A. (2017, May). Reducing power consumption for system on programmable chip by scheduling tasks. In 2017 International Conference on Engineering & MIS (ICEMIS) (pp. 1-4). IEEE.
[15] Tahir, N. U. A., Zhang, Z., Asim, M., Chen, J., & ELAffendi, M. (2024). Object detection in autonomous vehicles under adverse weather: a review of traditional and deep learning approaches. Algorithms, 17(3), 103.
[16] Yeong, D. J., Velasco-Hernandez, G., Barry, J., & Walsh, J. (2021). Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors, 21(6), 2140.
[17] Borrego-Carazo, J., Castells-Rufas, D., Biempica, E., & Carrabina, J. (2020). Resource-constrained machine learning for ADAS: A systematic review. IEEE Access, 8, 40573-40598.
[18] Li, B. (2022). High-speed 3D optical sensing for manufacturing research and industrial sensing applications. Transactions on Energy Systems and Engineering Applications, 3(2), 1-12.
[19] Makris, A., Kontopoulos, I., Psomakelis, E., Xyalis, S. N., Theodoropoulos, T., & Tserpes, K. (2022). Performance analysis of storage systems in edge computing infrastructures. Applied Sciences, 12(17), 8923.
[20] Zheng, Y. (2024). Unlocking The Potential of Lidar: Principles, Applications, And Future Prospects. Highlights in Science, Engineering and Technology, 81, 258-264.
[21] Wang, M. (2024). Virtual Reality Simulated Augmented Reality Display on Windshields: Improving the Spatial Awareness of Autonomous Car Drivers.
[22] Dayananda, B. N., Srivastava, N., Achala, G., Nandagiri, A., Srihari, P., Pardhasaradhi, B., & Cenkeramaddi, L. R. (2024, April). Depth Information Fusion Using Radar-LiDAR-Camera Experimental Setup for ADAS Applications. In 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 59-65). IEEE.
[23] Ahmad, U., Han, M., Jolfaei, A., Jabbar, S., Ibrar, M., Erbad, A., ... & Alkhrijah, Y. (2024). A Comprehensive Survey and Tutorial on Smart Vehicles: Emerging Technologies, Security Issues, and Solutions Using Machine Learning. IEEE Transactions on Intelligent Transportation Systems.
[24] Murugan, S., Sampathkumar, A., Kanaga Suba Raja, S., Ramesh, S., Manikandan, R., & Gupta, D. (2022). Autonomous vehicle assisted by heads up display (HUD) with augmented reality based on machine learning techniques. In Virtual and augmented reality for automobile industry: Innovation vision and applications (pp. 45-64). Cham: Springer International Publishing.
[25] Bisogni, C., Cascone, L., Nappi, M., & Pero, C. (2024). IoT-enabled biometric security: enhancing smart car safety with depth-based head pose estimation. ACM Transactions on Multimedia Computing, Communications and Applications, 20(6), 1-24.
[26] Hariri, M., & Heydarzadeh, N. (2023). Face recognition in images using Viola-Jones method and image texture analysis. Journal of Intelligent Processing and Multimedia Communication Systems, 4(2), 1-10
[27] Wang, H., Feng, Y., Tian, Y., Wang, Z., Hu, J., & Tomizuka, M. (2024). Towards the next level of vehicle automation through cooperative driving: A roadmap from planning and control perspective. IEEE Transactions on Intelligent Vehicles.
[28] Li, J., Fotouhi, A., Liu, Y., Zhang, Y., & Chen, Z. (2024). Review on eco-driving control for connected and automated vehicles. Renewable and Sustainable Energy Reviews, 189, 114025.
[29] Rana, M. M., & Hossain, K. (2023). Connected and autonomous vehicles and infrastructures: A literature review. International Journal of Pavement Research and Technology, 16(2), 264-284.
[30] De-Las-Heras, G., Sánchez-Soriano, J., & Puertas, E. (2021). Advanced Driver Assistance Systems (ADAS) Based on Machine Learning Techniques for the Detection and Transcription of Variable Message Signs on Roads. Sensors, 21, 5866.
[31] Kim, Y., Oh, C., Hwang, J., Kim, W., Oh, S., Lee, Y., Sharma, H., Yazdanbakhsh, A., & Park, J. (2024). DaCapo: Accelerating Continuous Learning in Autonomous Systems for Video Analytics.
[32] Tatar, G., & Bayar, S. (2024). Real-Time Multi-Learning Deep Neural Network on an MPSoC-FPGA for Intelligent Vehicles: Harnessing Hardware Acceleration With Pipeline. IEEE Transactions on Intelligent Vehicles.
[33] Ekatpure, R. (2023). Machine Learning for Enhancing Vehicle Safety and Collision Avoidance Systems in Automotive Development: Techniques, Models, and Real-World Applications. Journal of Computational Intelligence and Robotics, 3(2), 1-43.
[34] Tian, B., Wang, J., Xie, Y., Li, Y., Zhu, Y., & Chen, L. (2024). High-Definition Mapping for Autonomous Driving in Surface Mines. IEEE Transactions on Intelligent Vehicles.
[35] Elghazaly, G., Frank, R., Harvey, S., & Safko, S. (2023). High-definition maps: Comprehensive survey, challenges and future perspectives. IEEE Open Journal of Intelligent Transportation Systems.
[36] Liu, L., Chen, C., Pei, Q., et al. (2021). Vehicular Edge Computing and Networking: A Survey. Mobile Networks and Applications, 26(1145-1168).
[37] Jihong, X. I. E., Xiang, Z. H. O. U., & CHENG, L. (2024). Edge Computing for Real-Time Decision Making in Autonomous Driving: Review of Challenges, Solutions, and Future Trends. International Journal of Advanced Computer Science & Applications, 15(7).
[38] Attaran, M. (2023). The impact of 5G on the evolution of intelligent automation and industry digitization. Journal of ambient intelligence and humanized computing, 14(5), 5977-5993.
[39] Hakak, S., Gadekallu, T. R., Ramu, S. P., Parimala, M., Maddikunta, P. K. R., de Alwis, C., & Liyanage, M. (2022). Autonomous Vehicles in 5G and Beyond: A Survey.
[40] Hemani, Singh, D., & Dwivedi, R. K. (2024). Designing blockchain based secure autonomous vehicular internet of things (IoT) architecture with efficient smart contracts. International Journal of Information Technology.
[41] Wang, Y., Wang, Y., Qin, H., et al. (2021). A Systematic Risk Assessment Framework of Automotive Cybersecurity. Automotive Innovation, 4(253-261).
[42] Qudus, A. (2024). Database Management Systems in Autonomous Vehicles: Ensuring Data Integrity and Security in ADAS.
[43] Guan, T., Han, Y., Kang, N., Tang, N., Chen, X., & Wang, S. (2022). An overview of vehicular cybersecurity for intelligent connected vehicles. Sustainability, 14(9), 5211.
[44] Dönmez, Ö., Vaculín, O., & de Borba, T. (2024). A Cost Effective Solution to an Automated Valet Parking System. International Journal of Automotive Technology, 25(2), 369-380.
[45] Wang, X., Li, K., & Chehri, A. (2023). Multi-sensor fusion technology for 3D object detection in autonomous driving: A review. IEEE Transactions on Intelligent Transportation Systems.
[46] Liu, S., Chen, R., Hu, X., Zhou, B., Chen, C., Liu, Z., & Li, X. (2023, August). Development of cloud automation evaluation system based on typical ADAS functions. In Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023) (Vol. 12784, pp. 782-794). SPIE.
[47] Kothari, A., Talty, T., Huxtable, S., & Zeng, H. (2024). Energy-Efficient and Context-Aware Computing in Software-Defined Vehicles for Advanced Driver Assistance Systems (ADAS) (No. 2024-01-2051). SAE Technical Paper.
[48] Jain, A., Goyal, V., & Sharma, K. (2024). A Comprehensive Analysis of AI/MLenabled Predictive Maintenance Modelling for Advanced Driver-Assistance Systems. Journal of Electrical Systems, 20(4s), 486-507.
[49] Wani, A., Kumari, D., & Singh, J. (2024). Ethics in the Driver's Seat: Unravelling the Ethical Dilemmas of AI in Autonomous Driving (No. 2024-01-2023). SAE Technical Paper.
[50] Pandolfi, A., Adinolfi, E. A., Polverino, P., & Pianese, C. (2023). Real-Time Prediction of Fuel Consumption via Recurrent Neural Network (RNN) for Monitoring, Route Planning Optimization and CO2 Reduction of Heavy-Duty Vehicles (No. 2023-24-0175). SAE Technical Paper.
[51] Selvaraj, S., & Banerjee, J. (2023, December). Harnessing the Power of Right Data Over Big Data for Autonomous Driving using Data Analytics. In 2023 IEEE International Transportation Electrification Conference (ITEC-India) (pp. 1-4). IEEE.
[52] Deo, A., & Palade, V. (2022). Switching trackers for effective sensor fusion in advanced driver assistance systems. Electronics, 11(21), 3586.
[53] Moujahid, A., Dulva Hina, M., El Araki Tantaoui, M., & El Khadimi, A. (2018). Machine learning techniques in ADAS: A review. Proceedings of the IEEE International Conference on Electronics, Circuits and Systems, 1-5Mounier, E., Elhabiby, M., Korenberg, M., & Noureldin, A. (2024). LiDAR-Based Multi-Sensor Fusion with 3D Digital Maps for High-Precision Positioning. IEEE Internet of Things Journal.
[54] Garikapati, D., & Shetiya, S. S. (2024). Autonomous vehicles: Evolution of artificial intelligence and the current industry landscape. Big Data Cognition and Computation, 8(4), 42.
[55] Singh, J., Sharma, A., & Kaur, S. (2024). Autonomous driving and ADAS embedded with AI: Comparing the AI norms. In Proceedings of the 2024 International Conference on Intelligent Systems for Cybersecurity (ISCS) (pp. 979-8-3503-7523-7). IEEE.
[56] Tang, Z., He, J., Yang, K., & Chen, H.-H. (2024). MCSV: A vehicle matching approach for 5G-connected vehicles in cooperative driving applications. IEEE Network, 38(3), 227-234.nr.
