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.
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