Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system

Smart Battery Management Technology in Electric Vehicle Applications: Analytical and Technical Assessment toward Emerging Future Directions

Electric vehicles (EVs) have received widespread attention in the automotive industry as the most promising solution for lowering CO2 emissions and mitigating worldwide environmental concerns. However, the effectiveness of EVs can be affected due to battery health degradation and performance deterioration with lifespan. Therefore, an advanced and smart battery management technology is essential for accurate state estimation, charge balancing, thermal management, and fault diagnosis in enhancing safety and reliability as well as optimizing an EV’s performance effectively. This paper presents an analytical and technical evaluation of the smart battery management system (BMS) in EVs. The analytical study is based on 110 highly influential articles using the Scopus database from the year 2010 to 2020. The analytical analysis evaluates vital indicators, including current research trends, keyword assessment, publishers, research categorization, country analysis, authorship, and collaboration. The technical assessment examines the key components and functions of BMS technology as well as state-of-the-art methods, algorithms, optimization, and control surgeries used in EVs. Furthermore, various key issues and challenges along with several essential guidelines and suggestions are delivered for future improvement. The analytical analysis can guide future researchers in enhancing the technologies of battery energy storage and management for EV applications toward achieving sustainable development goals