Random Dynamical Systems (RDS) are a powerful method for utilising mathematics to characterise and comprehend systems influenced by uncertainty and randomness. RDS are very important for self-driving cars because they help make decisions easier, plan routes better, and make the whole process safer.  Self-driving cars need to learn how-to drive-in places that change all the time, like when the weather changes, the traffic patterns change, or the noise from sensors changes. RDS makes guidance more reliable and useful, which helps these cars get ready for and deal with these kinds of unknowns. In this field, RDS is used to make random control methods, real-time adaptive planning, fault-tolerant systems, and other things. These methods help self-driving cars get ready for problems, change their routes on the fly, and keep working well even when things go wrong. By combining experience and data, the RDS-based models make it easier to use machine learning methods to improve guidance strategies over time. RDS will be an important part of solving the problems that come up in the real world as technology for self-driving cars gets better.  In the long run, this will make transportation systems safer and more efficient.