Robust perception and decision making for autonomous vehicles

Development of autonomous vehicles is seeing a growth in many different applications. As we increase the levels of automation and move into Self-driving cars, it is expected that these systems will combine a variety of sensors to perceive their surroundings in a robust manner and avoid human errors. They also need to adapt quickly to changes in their environments and make decisions based on a number on inputs that might be contradictory. Moreover, reports have found that autonomous vehicles can be vulnerable to a wide range of attacks such as physical perturbations or back-end malicious activity. This project looks at AI robust perception methods that combine a range of sensory inputs including computer vision to identify changes and suspicious sensor responses in order to make robust decisions on motion and planning.