Advancements in ADAS and Integration with Legacy systems

Abstract

Advanced Driver Assistance Systems (ADAS) have significantly evolved, leveraging artificial intelligence (AI), machine learning (ML), and sensor fusion technologies to enhance vehicle automation, safety, and performance. This paper critically analyses ADAS advancements, challenges in retrofitting legacy systems, and safety-performance outcomes of ADAS integration. The study identifies key technological improvements, including the integration of LiDAR, radar, and real-time V2X communication, which enhance situational awareness and decision-making. However, retrofitting new ADAS into legacy vehicles presents challenges such as compatibility constraints, cybersecurity risks, and high implementation costs. Furthermore, while ADAS integration has demonstrated substantial improvements in accident prevention, driver assistance, and traffic efficiency, usability concerns related to automation reliance and human-machine interaction remain. The findings underscore the need for standardised protocols, adaptive software solutions, and robust testing frameworks to ensure seamless integration. Future research should explore adaptive AI-driven ADAS frameworks, real-world validation of cloud-edge architectures, and policy frameworks for global ADAS standardisation. Addressing these gaps will accelerate the adoption of next-generation autonomous driving technologies while ensuring safety, efficiency, and regulatory compliance.

DOI: https://doi.org/10.24321/2456.1428.202601