This course was built as a gentle but rigorous doorway into target tracking for driver assistance and autonomous driving. Rather than dropping learners directly into formulas, each idea is introduced through visual representations from road situations: crossing cars, hidden pedestrians, lane changes, slippery turns, and emergency braking events. Abstract symbols are always tied to something that could actually happen in traffic.
The journey starts with probability from the level most people remember from school and carefully upgrades it for real sensor data. Uncertainty is explained as something that lives inside noisy camera, radar, ultrasonic sensors and lidar measurements. Random variables and distributions are presented as shapes that can be seen in the mind and on plots, not just in equations. Step by step, these shapes are combined to describe multiple sources of uncertainty acting at the same time.
A dedicated section on random processes gives special attention to what makes real systems feel “alive” and changing. Random vectors, Markov and stationary behaviour, ergodicity and the law of large numbers are linked to moving vehicles, time-varying road conditions, and driver reactions. This part is designed to fix the common gap between textbook theory and how randomness shows up on the road and in real world applications.
Signals and systems are then revisited with this probabilistic viewpoint. Intuition for impulse response, convolution, and state space models is tied to how sensors and ECUs actually treat incoming data. With this base, estimation techniques are introduced: minimum mean square error, least squares, finite impulse response filtering, adaptive methods such as gradient descent, and Bayesian ideas.
Only after this scaffold is in place do Kalman filters appear. The standard filter is built as a natural consequence of everything before it, then extended to nonlinear motion through the extended Kalman filter. Each scenario is implemented in code and explored using simulator dashboards for crossing, parallel, oncoming, overtaking, and manoeuvring vehicles.
By the end, learners see that many algorithms in modern automotive perception and control are simply estimation methods wrapped around random processes. The course is designed to serve as a solid base for later study of advanced Kalman techniques, reinforcement learning, and machine learning for autonomous systems.
