Fuzzy Car Control

Intelligent vehicle control systems are a popular inclusion in many new cars. The features of such systems include radar$-$based vehicle separation, collision avoidance $($auto$-$braking$),$ lane following or lane$-$departure warnings, and of course the most common feature, cruise control $($i$.$e$.,$ constant speed control$).$

Constant speed control in vehicles and other machines can be achieved using error feedback control $$ a simple mathematical control theory technique $$ however the performance of such systems varies considerably when the road is not flat or even straight. A deficiency of many cruise control systems is the control lag that occurs when the road slope changes, causing the car to lose speed initially when driving uphill, or to overspeed initially when driving downhill.

Similarly, vehicle separation systems based on a threshold distance tend to cause the car to oscillate around this set distance, alternately speeding up to close the large gap, or slowing down to increase it$.$ While advanced filtering techniques could be applied to the control signal $($the commanded engine speed$),$ an alternative method is to implement a rule$-$based fuzzy logic controller.

Task in Python: Create an intelligent control system using Fuzzy Logic

In this task you are to create an intelligent control system for a vehicle, using the model of fuzzy systems we have been learned in week $3.$ You should select at least one feature mentioned above and create a controller for this. You can, of course, demonstrate a controller capable of handling two or more features.

You will be required to demonstrate the feasibility of your controller by implementing it in computer