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Uzzaman, Asif
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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A Comprehensive Review of Environmental and Economic Impacts of Autonomous Vehicles Uzzaman, Asif; Muhammad, Waseem
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.131

Abstract

The development of autonomous vehicles (AVs) holds great potential for revolutionary improvements in several fields, most notably the economic and environmental domains. This paper analyzes the two-pronged effects of AVs, showing both the advantages and disadvantages that may arise. With their enhanced fuel efficiency, integrated electric vehicle technology, and driving behaviors, autonomous vehicles (AVs) have the potential to drastically reduce emissions and have a positive environmental impact. Lower carbon footprints could also be achieved by improved urban design and the possibility of less traffic congestion. In terms of the economy, AVs present chances for new transportation service business models, lower accident-related costs, and cost reductions in logistics. Nevertheless, there are drawbacks to these developments as well, such as high upfront costs, the possibility of employment displacement in the driving industry, and the requirement for strong regulatory frameworks to guarantee security and safety. It is anticipated that autonomous vehicles will improve lane management, acceleration, and deceleration, which could result in less gasoline being used. In a place like California where traffic congestion is a well-known issue, this is especially pertinent. When paired with electric cars (EVs), autonomous vehicles (AVs) have the potential to significantly lower greenhouse gas (GHG) emissions, supporting California aggressive climate targets, which include becoming carbon neutral by 2045. To maximize the positive effects of AVs while minimizing their negative effects, this review summarizes the most recent studies, offering a fair assessment of their implications. It also identifies important topics for further research.
Review on the Safety and Sustainability of Autonomous Vehicles: Challenges and Future Directions Uzzaman, Asif; Adam, Md Ibrahim; Alam, Shahin; Basak, Pallab
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.185

Abstract

Autonomous vehicles (AVs) represent a major advancement in transportation technology, offering significant benefits such as enhanced road safety, reduced traffic congestion, and improved mobility. However, the widespread deployment of AVs faces key obstacles, including sensor limitations under adverse weather conditions, ethical decision-making in complex scenarios, regulatory challenges, and data privacy concerns. This paper examines these challenges and proposes potential solutions. Key challenges include improving sensor fusion and AI algorithms to enhance perception and decision-making, developing standardized ethical guidelines for autonomous systems, and establishing consistent legal and regulatory standards across regions. Additionally, ensuring cybersecurity and addressing data privacy issues are critical for maintaining the safety and trust of AV users. The future of AVs also depends on advancements in infrastructure, such as the development of smart roads and Vehicle-to-Everything (V2X) communication systems, as well as reducing production costs to increase accessibility. Furthermore, raising public awareness and fostering acceptance through education and transparent communication about AV benefits is vital. The paper concludes that with ongoing research, innovation, and collaboration, AVs have the potential to revolutionize transportation, offering a safer, more efficient, and sustainable future for mobility.
AI-Driven Microgrid Solutions for Enhancing Energy Access and Reliability in Rural and Remote Areas: A Comprehensive Review Ahmed, Faisal; Uzzaman, Asif; Adam, Md Ibrahim; Islam, Monirul; Rahman, Md Moklesur; Islam, Asm Mohaimenul
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.183

Abstract

As localized energy systems, microgrids provide a viable way to solve problems with energy dependability and access in rural and isolated locations. These regions often have inadequate and unstable grid infrastructure, which restricts their access to energy. Artificial Intelligence (AI) improves the overall performance, flexibility, and efficiency of microgrid systems. AI ensures a steady and dependable power supply by enabling predictive maintenance, optimal load forecasting, energy storage management, and renewable energy resource optimization. AI may help microgrids anticipate system faults, better control energy consumption, and prolong the life of vital parts. Additionally, AI ensures the sustainability of microgrids in resource-constrained places by optimizing the usage of renewable energy sources like solar and wind. Successful case studies from places like the US, India, and Africa have shown the promise of AI-enhanced microgrids in raising the standard of living for marginalized areas, despite obstacles like data infrastructure and upfront installation costs. Microgrids have a bright future thanks to developments in artificial intelligence (AI), which might increase electricity availability and promote economic growth in rural and isolated regions of the world.
Testing Autonomous Vehicles in Virtual Environments: A Review of Simulation Tools and Techniques Uzzaman, Asif; Islam, Monirul; Hossain, Md Shimul
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.196

Abstract

Autonomous vehicles (AVs) have the potential to transform the transportation industry by improving road safety, reducing traffic congestion, and enhancing fuel efficiency. Significant progress has been made in autonomous vehicle (AV) technologies, especially in sensor systems, machine learning, and artificial intelligence. These advancements enable vehicles to navigate complex environments and make real-time decisions. Despite these advancements, numerous challenges remain in ensuring the safety, reliability, and acceptance of AVs. Key issues include sensor fusion, the ability to handle unpredictable scenarios, the development of universally accepted regulatory frameworks, and public trust in autonomous systems. Furthermore, ethical dilemmas, such as decision-making in unavoidable accident situations, present additional concerns. The deployment of AVs also raises questions about the impact on employment in driving-dependent industries and the infrastructure needed to support these technologies. This paper reviews the current state of AV development, examining the progress made in simulation-based testing, sensor technology, and decision-making algorithms. Additionally, it discusses the challenges that still need to be addressed, including safety concerns, regulatory barriers, and societal implications. The paper concludes by outlining potential areas for future research, such as improving sensor reliability, enhancing machine learning algorithms, integrates an analysis of simulation-based testing, decision-making algorithms, and sensor technologies with a forward-looking discussion on legal frameworks, public trust, and employment impacts, offering a holistic perspective on the path toward AV integration.
Bio-Inspired Hybrid Control for Autonomous Vehicles: Improving Real-Time Navigation Through the Integration of ACO and PSO Uzzaman, Asif; Islam, Monirul; Ahmed, Shishir
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.204

Abstract

This research demonstrates nature-inspired control systems for the navigation of autonomous vehicles (AVs), utilizing algorithms derived from nature Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) to tackle challenges posed by dynamic environments. ACO is based on the pheromone trails of ants to facilitate adaptive route selection, PSO draws inspiration from bird flocking behavior for optimal pathfinding, and ABC imitates the division of labor seen in bee swarms for decentralized decision-making. A combined ACO-PSO model merges ACO's capability for local adaptability with PSO's ability for global convergence, allowing for real-time modifications to paths. Simulations conducted on the CARLA and SUMO platforms illustrate improvements in navigation stability and responsiveness, showcasing enhancements in trajectory smoothness by 15%, collision avoidance by 22%, and congestion reduction by 18% when faced with unexpected obstacles and variable traffic conditions. The findings support the notion that bio-inspired systems can serve as scalable and resilient alternatives to conventional algorithms, providing strong solutions for the emergence of next-generation AV technologies. This study connects biological concepts with artificial autonomy to develop intelligent transportation systems using hybrid algorithms and real-time adaptive learning. Biologically inspired models enhance decision-making in complex environments. However, limitations such as high computational complexity and challenges in scaling the system for real-world applications are acknowledged.
Co-Authors Adam, Md Ibrahim Ahmed, Faisal Ahmed, Shishir Alam, Shahin Basak, Pallab Hossain, Md Shimul Islam, Asm Mohaimenul Islam, Monirul Muhammad, Waseem Rahman, Md Moklesur