Relevance of Classical Algorithms in Modern Autonomous Driving Architectures
While modern autonomous driving systems increasingly rely on machine learning and deep neural networks, classical algorithms continue to play a foundational role in ensuring reliability, interpretability, and real-time performance. Techniques such as Kalman filtering, A* path planning, PID control, and SLAM remain integral to perception, localization, and decision-making modules. Their deterministic nature and lower computational overhead make them especially valuable in safety-critical scenarios and resource-constrained environments. This talk explores the enduring relevance of classical algorithms, their integration with learning-based methods, and their evolving scope in the context of next-generation autonomous vehicle architectures.
Kalman Filtering - https://en.wikipedia.org/wiki/Kalman_...
A* Path Planning - https://en.wikipedia.org/wiki/A*_sear...
PID Control - https://en.wikipedia.org/wiki/Proport...
SLAM - https://en.wikipedia.org/wiki/Simulta...
About the Speaker
Prajwal Chinthoju is an Autonomous Driving Feature Development Engineer with a strong foundation in systems engineering, optimization, and intelligent mobility. I specialize in integrating classical algorithms with modern AI techniques to enhance perception, planning, and control in autonomous vehicle platforms.