Selection of AI Architecture for Autonomous Vehicles Using Complex Intuitionistic Fuzzy Rough Decision Making
Abstract
:1. Introduction
1.1. Motivation and Contribution
- Data fusion, perception, and object detection: These refer to combining the data from multiple sensors (vision system, lidar system (light detection and ranging), radar system (detection of objects and their speed, even in poor visibility), and ultrasonic sensor for short-range object detection) to produce a more accurate and reliable representation of the vehicle’s surrounding. The AI architecture should be able to use these sensors’ data efficiently and develop a fusion algorithm to improve perception and DM. By combining the information from these multiple sensors, autonomous vehicles can create an exceptionally flexible and redundant cognitive framework, thereby reducing the potential risk associated with wrong decisions in critical DM scenarios. In real-time, the AI structures need to be able to detect and accurately identify different objects, such as pedestrians, road hurdles, speed breakers, other cars, lane markings and traffic signs, and other static elements in the environment. For this, sophisticated computer vision and deep learning algorithms are needed.
- Localization and mapping: The localization system in the vehicles uses a combination of a global positioning system (GPS) and inertial measurement units (IMUs) for precise positioning. Autonomous vehicles often use high-definition (HD) maps that provide detailed information about the road network, pedestrians, road hurdles, speed breakers, lane markings, and traffic signs in the environment. These maps are combined with real-time sensor data to build a comprehensive understanding of the vehicle’s surroundings. The AI architecture should be able to accurately localize the car within its environment and maintain a detailed map of the environment, including static and dynamic elements, to support efficient navigation and DM.
- Decision-making and motion-planning framework: The DM system is the brain of autonomous vehicles. It uses the information from the perception, localization, and mapping systems to plan the vehicle’s trajectory and make decisions about when to accelerate, brake, or change lanes. This system relies on advanced algorithms to navigate the vehicle safely and efficiently.
- Control system and machine learning approach: The AI DM system interconnects with the vehicle control system, which translates the high-level decisions into precise commands that control the vehicle’s steering, acceleration, and brakes. Machine learning is the backbone of autonomous vehicles; the perception and DM modules, including deep neural networks, rely on advanced machine learning techniques to improve their performance and adapt to new situations.
- Safety, reliability, and scalability: The AI architecture must be designed with safety and ethical principles in mind, ensuring that autonomous vehicle actions and decisions prioritize the safety of pedestrians and other road users. AI systems develop rigorous testing and validation processes, including simulation and real-world trials, and they ensure that robust fail-safe mechanisms are in place to handle unexpected situations.
1.2. Aims and Objectives
- To define and develop the theory of CIFRSs.
- To define and develop different operators, such as the average AOs and geometric AOs, in the framework of CIFRSs.
- To apply a newly defined framework to assess multiple AI architectures based on a wide range of performance indicators, and utilize the AOs to combine diverse evaluation criteria into a single, meaningful value.
- To select a suitable MADM method based on the problem characteristics and data availability.
- To develop an MADM algorithm for aggregation and evaluation to handle large datasets and complex AI architectures.
- To examine the comparative study of the proposed methods with the present notions to show the supremacy and efficiency of the established work.
1.3. Study Framework
2. Background of the Study
Preliminaries
- ;
- ;
- , where represents the complement of ;
- .
- ;
- ;
- , where represents the complement of ;
- .
- ;
- ;
- , where represent the complement of the IF rough approximation operators ;
3. Complex Intuitionistic Fuzzy Rough Set
Basic Operations and Properties of Complex Intuitionistic Fuzzy Rough Set
- Complement
- Union
- Intersection:
- Algebraic sum:
- Algebraic product:
- 1.
- 2.
4. Aggregation Operators Based on CIFRSs
5. A Method of MADM in the Setting of CIFR Information
5.1. Algorithm
5.2. Case Study
6. Comparison Analysis
- ❖
- ❖
- IFR AOs for MCDM by Chinram et al. [37].
- ❖
- IFR Frank AOs for MCDM by Yahya et al. [36].
- ❖
- Aczel–Alsina average AOs based on IFRSs by Ahmmad et al. [38].
- ❖
- Confidence level AOs based on IFRSs by Mahmood et al. [39].
- ❖
- Yager AOs based on IFRSs by Mahmood et al. [40].
Theoretical and Practical Implications
7. Conclusions
7.1. Limitations
7.2. Advantages
7.3. Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Methods | Score Values of Alternatives | Rankings |
---|---|---|
Xu [18] | ||
Xu and Yager [19] | ||
Chinram et al. [37] | ||
Yahya et al. [36] | ||
Ahmmad et al. [38] | ||
Mahmood et al. [39] | ||
Mahmood et al. [40] | ||
Interpreted theory | 0.571, 0.664, 0.621, 0.596 | |
Interpreted theory | 0.491, 0.586, 0.565, 0.505 |
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© 2024 by the authors. Published by MDPI on behalf of the World Electric Vehicle Association. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Mahmood, T.; Idrees, A.; Hayat, K.; Ashiq, M.; Rehman, U.u. Selection of AI Architecture for Autonomous Vehicles Using Complex Intuitionistic Fuzzy Rough Decision Making. World Electr. Veh. J. 2024, 15, 402. https://doi.org/10.3390/wevj15090402
Mahmood T, Idrees A, Hayat K, Ashiq M, Rehman Uu. Selection of AI Architecture for Autonomous Vehicles Using Complex Intuitionistic Fuzzy Rough Decision Making. World Electric Vehicle Journal. 2024; 15(9):402. https://doi.org/10.3390/wevj15090402
Chicago/Turabian StyleMahmood, Tahir, Ahmad Idrees, Khizar Hayat, Muhammad Ashiq, and Ubaid ur Rehman. 2024. "Selection of AI Architecture for Autonomous Vehicles Using Complex Intuitionistic Fuzzy Rough Decision Making" World Electric Vehicle Journal 15, no. 9: 402. https://doi.org/10.3390/wevj15090402