In the context of robotics in general, "autonomous" refers to the ability of a robot to operate without the intervention of a human operator. And the same definition applies to autonomous driving i.e. the ability of a vehicle to drive itself without the intervention of a human.
This is an introductory course to autonomous driving. It covers different techniques and sub-systems needed to endow vehicles with autonomy. This includes the ability of a vehicle to perceive its environment, map its environment and localize itself within a map, plan its motion in its environment, execute that motion smoothly, as well as detect, model and predict behavior of other agents present in its environment.
Upon completion of the course, a student will be able to: I. Implement map-based localization using particle filtering II. Implement SLAM on simulated exteroceptive and proprioceptive data III. Explain different planning strategies for robot motion planning IV. Implement a control strategy for lateral position control V. Describe challenges in behavior modelling in autonomous driving VI. Understand the pros and cons of different exteroceptive sensing systems used in autonomous driving VII. Understand the steps involved in employing a sensing system
Topics covered in the course
- Simultaneous localization and mapping
- Motion planning
- Sensing and perception
- Sensor fusion
- Interaction and behavior modelling
- End-to-end approaches
Course evaluation/grading is based on weekly lab assignments (delivered in the form of reports and code) and three oral exams.
Naveed Muhammad (email@example.com)