Integrity Monitoring of Motion Estimation and Hazard Detection Algorithms in Environmentally-Impacted Scenarios

Imagine some of the difficult driving conditions experienced by vehicle operators. In these conditions, the sun might be blindingly bright, or the snow might obfuscate what is going on around the vehicle. Surprisingly, the sensors used by autonomous vehicles to understand the environment they are in suffer from similar effects. As a field, robotics has yet to tackle integrity monitoring of the sensors used in autonomous applications. In order to transport safety critical loads, such as people or train freight, it is imperative to know when the data the autonomous vehicle receives is corrupted by environmental effects and adjust the behaviour of the autonomous vehicle accordingly. In this project, we aim to develop an algorithm that uses a sensor’s datastream and the outputs of the algorithms that rely on that sensor’s datastream to monitor the integrity of the data collected by the sensor.

Intern: 
Emmett Wise
Faculty Supervisor: 
Jonathan Kelly
Province: 
Ontario
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