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“Ensuring safety on the road is not just a duty; it’s a commitment to protecting lives and fostering trust in automotive technology,” says Durgadevi Yenuganti, a functional safety specialist for hardware. With a Master’s degree in Computer Science and a Bachelor’s in Electronics and Communication, Durgadevi Yenuganti has used her extensive knowledge to drive advancements in autonomous driving assistance systems(ADAS).
“One of my key contributions has been developing failure mode effective analysis or FMEA to identify the failures of components of electronic systems or subsystems,” Durgadevi Yenuganti explains. This approach has enhanced the reliability of automotive systems and set new benchmarks for safety standards in the industry.
Durgadevi’s role as the HW TRA Moderator of the Radar project earned her the “Star of the Month” award, a reflection of her dedication and impact on the industry.
From design to deployment: Ensuring safety at every stage
Ensuring safety in automotive engineering is a comprehensive process that spans from the initial design phase to the final deployment of vehicles. Yenuganti’s responsibilities include deriving hardware safety requirements, where she performs Safety FMEA, FMEDA (Failure Modes, Effects, and Diagnostic Analysis), and FTA (Fault Tree Analysis). These processes are critical in identifying and mitigating potential safety risks.
“Functional safety requires conducting ‘safety analyses’ to classify hardware faults in relation to violations of specific safety goals,” Yenuganti notes. This involves a detailed examination of hardware components to ensure they meet the requirements of ISO 26262, the international standard for functional safety in road vehicles.
Implementing these safety requirements ensures the systems are both functional and safe.
Functional safety starts from the concept phase, where safety goals are defined and continues through system design, implementation, verification, validation, and maintenance. It involves identifying potential hazards, assessing risks, and ensuring that safety measures are integrated into the entire lifecycle of the system to meet safety requirements.
Real-world applications: Case studies in automotive safety
Yenuganti’s work in safety engineering is not just theoretical; it has real-world applications that have improved automotive safety. One notable project involved the development of autonomous driving assistance systems, where Yenuganti’s expertise in FMEA played a crucial role in identifying and addressing potential failures.
“We were able to implement robust safety mechanisms that reduced the risk of accidents by analyzing the potential failure modes and their effects,” Yenuganti shares.
Another successful case study is Yenuganti’s work on the short-range radar project, where she developed the template for FMEDA(Failure mode effective diagnostic analysis). This innovation improved the accuracy and efficiency of safety assessments, leading to better-informed decisions and enhanced safety outcomes.
The role of AI and machine learning in automotive safety
Emerging technologies like artificial intelligence (AI) and machine learning (ML) are set to provide advanced capabilities in hazard prediction, detection, and reaction in the automotive industry. Yenuganti is optimistic about its potential to further enhance vehicle safety and reliability.
“AI and machine learning can provide new insights and capabilities in safety engineering, allowing us to predict and prevent potential failures more effectively,” Yenuganti says. “These technologies can analyse vast amounts of data to identify patterns and trends that may not be apparent through traditional methods. It will lead to more accurate and proactive safety measures.”
Durgadevi Yenuganti demonstrates her expertise and dedication to safety engineering in the automotive industry. Utilising advanced technologies, implementing thorough safety processes, and working collaboratively, she improves vehicle reliability and safety, contributing to a safer future.
