A comparative study of machine learning algorithms in self-driving cars

This teaching case examines the role of machine learning, deep learning, and artificial intelligence algorithms in the development and operation of self-driving cars. Using examples such as Waymo and Tesla, it explores how autonomous vehicles collect, process, and respond to real-time data through sensor systems, computer vision, object detection, and neural networks. The teaching case compares the functions of machine learning and deep learning in autonomous driving, emphasizing their importance in decision-making, pedestrian detection, traffic navigation, and safety improvement. It also discusses the potential benefits of autonomous vehicles, including increased convenience, reduced traffic accidents, improved fuel efficiency, enhanced accessibility, and more efficient transportation systems. At the same time, the teaching case addresses major concerns involving public trust, legal liability, cybersecurity, data privacy, infrastructure readiness, job displacement, and performance limitations in unusual driving conditions. By examining current applications and future possibilities, the teaching case argues that self-driving cars represent both a major technological advancement and a complex social challenge. Continued progress will depend on reliable algorithms, strong data security, regulatory guidance, public confidence, and responsible implementation.