Design a Self-Driving Car (Tesla Model) Using Reinforcement Learning Running in Webots Simulator - IT Assignment Help

Assignment Task

Design a self-driving car (Tesla model) using reinforcement learning running in Webots simulator. The code should be in python.
1. Design a vehicle in webots: Tesla model with a sensor onboard, i.e camera
2. Implement a reinforcement learning algorithm for the designed vehicle with any reinforcement learning algorithm using python. The algorithm must run in few different cities/maps. The following rule must be taken into consideration:
City Navigation avoiding collision with static and dynamic obstacles in the city (e.g other vehicles, pedestrians and/or other objects), in case the obstacle is getting closer to the vehicle.
3. Compare and explain the performance of your algorithm in different cities (contexts). Present graphics to show the performance of the algorithm for learning, accuracy, reward/episode and time spent.
4. Once the reinforcement learning algorithm is ready and the self-driving car can navigate in a city, implement another algorithm (simplistic, based on sensor data or using machine learning) to detect a traffic light or another object that might influence, e.g making the vehicle stop or change the velocity or change the trajectory/route. Explain your selected approach and its performance.

Note: You can use RL to endow the vehicle to move forward/backward/left/right or to navigate following the road/lane, in this case you have to implement a lane detection algorithm. If you implement a lane detection to follow the road, you don’t need to implement any other machine learning in item (e).
Record a video (no longer than 10min) to present the results, i.e. the simulation of a self-driving car.
You are required to submit the video and the implementation code.

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