Mahindra E2O Driverless Car
Our team, Autonomous Ground Vehicle Research Group, was one of the top 13 teams to get selected for the final round of the Mahindra Rise Prize challenge out of more than 400 organisations. We were entrusted with the task to convert the Mahindra e2o electric vehicle into a fully autonomous vehicle. AGV lab is one of the few labs in the country having state of the art sensors used for autonomous driving like the Velodyne 32E 3D Lidar, Bumblebee Stereo cam, Vector-Nav Inertial measurement units, Hokuyo 2D Lidars, etc. The entire group was working on this project. I will briefly describe the modules that I have worked on and give an overview of the work being done on the other modules.Â
Lateral and Longitudinal Controls
The module for lateral and longitudinal control was a very challenging and and time-consuming to develop. Various controllers were tested for the longitudinal velocity control like the normal PID controller and the adaptive PID controller. The tests were done on a simulator before testing them on a car. Various methods were tried for the lateral controls of the car by considering a kinematic model as well as a dynamic model of the vehicle.
Path Planning and trajectory generation
The problem of planning can be divided into two parts- a global plan and a local plan. The global plan is a permanent path from the initial point to the end point and it remains constant whereas the local plan is limited to a certain distance and it keeps adapting based on the changing environment. We tested and tuned the Dijkstra based global planner and the Time elastic band local planner. The ROS move-base package was used for achieving the goals set by the planner.