Manchester Stinger Motorsports AI

Our Objectives

  • Build a fully autonomous racing car compliant with Formula Student UK AI rules
  • Develop a simulation environment for testing
  • Create a future proof system for development over time

Autonomous Events

Sprint Track Drive
Driving the autonomous car around a closed loop circuit to test its endurance
Acceleration
Measuring the car's acceleration capabilities on a 75m long straight track
Autocross/Sprint
Skidpad
Testing the car's handling through a sprint lap around a closed loop track
Engineering Design Judging
Evalution of the engineering process and thinking behind the design and development of the AI system.
Real World
Focuses primarily on the team’s understanding of the real world challenge for autonomous vehicles and their integration as future transport solutions.
Business Plan Presentation
Creation and evaluation of a business case tendering an AI system for public transport
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Teams

  • Build an instance segmentation dataset to train the neural network on the 4 cones types, humans and cars.
  • Train an instance segmentation network on the aforementioned dataset.
  • Sample the depths of the detected objects to build an accurate representation of the cone.
  • Train a general adversarial network to augment the weather conditions in the dataset to increase the size of the dataset.

  • Take in sensor information into an Extended Kalman Filter to estimate the position of the car.
  • Build up a map of the detected cones.
  • Using K-Means clustering, reduce noise in the detected cones.

  • Estimate the track boundaries the mapped track.
  • Calculate an optimal path for the car drive.
  • Build a dataset of tracks and optimal lines.

  • Calculate steering angle given the position of the car and upcoming path.
  • Calculate the speed given the position of the car and upcoming path.
  • Eventually, move over to a reinforcement learning approach.

  • Integrate as a node in a ROS 2 environment.
  • Interact with the vehicle via CAN-bus to control the car.
  • Log all testing data.
  • Visualise all data in real time.
  • Build a working simulator in gazebo that allows us to test codebase.
  • Build a reasonably accurate vehicle physics model.
  • Simulate CAN-bus interface.
  • Live visualisation of the vehicle in the environment.
  • Simulate vehicle sensors – GPS, IMU, Wheel Speed.
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