Exploring the Training Data for Online Learning of Autonomous Driving in a Simulated Environment

Detta är en Master-uppsats från Linköpings universitet/Datorseende

Sammanfattning: The field of autonomous driving is as active as it has ever been, but the reality where an autonomous vehicle can drive on all roads is currently decades away. Instead, using an on-the-fly learning method, such as qHebb learning, a system can,after some demonstration, learn the appearance of any road and take over the steering wheel. By training in a simulator, the amount and variation of training can increase substantially, however, an on-rails auto-pilot does not sufficiently populate the learning space of such a model. This study aims to explore concepts that can increase the variance in the training data whilst the vehicle trains online. Three computationally light concepts are proposed that each manages to result in a model that can navigate through a simple environment, thus performing better than a model trained solely on the auto-pilot. The most noteworthy approach uses multiple thresholds to detect when the vehicle deviates too much and replicates the action of a human correcting its trajectory. After training on less than 300 frames, a vehicle successfully completed the full test environment using this method.

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