The main objective of the vision module was giving a waypoint to the planning module. The lane features were extracted using combination of various image channels. A lane model estimate was determined(linear or parabolic). The lane model parameters were determined using RANSAC, an iterative algorithm, which is robust to outliers.

A variety of sensors were mounted on the robot, including a lidar and a front facing camera. Using these sensors, a costmap of the surrounding area was build. Path planning was carried out using the waypoint generated by the vision module. A global path estimate was first made over which a local path was planned considering the vehicle dynamics and state.

The main goal of this module was to traverse the vehicle over the desired trajectory. A Low level controller used a simple PID controller to actuate the motors, while a high level controls algorithm called MPC(Model Predictive Control) was used to generate the velocity profile over the inputted trajectory.