Commonsense Reasoning-Aided Autonomous Vehicle Systems
This research addresses the problem of limited reasoning capabilities in autonomous vehicle systems for the automotive industry and society as a whole, providing a potentially incremental solution.
This research tackles the problem of improving autonomous vehicle systems by incorporating commonsense reasoning models, resulting in more accurate, adjustable, explainable, and ethical systems. The outcome is expected to enhance the overall performance of autonomous vehicles.
Autonomous Vehicle (AV) systems have been developed with a strong reliance on machine learning techniques. While machine learning approaches, such as deep learning, are extremely effective at tasks that involve observation and classification, they struggle when it comes to performing higher level reasoning about situations on the road. This research involves incorporating commonsense reasoning models that use image data to improve AV systems. This will allow AV systems to perform more accurate reasoning while also making them more adjustable, explainable, and ethical. This paper will discuss the findings so far and motivate its direction going forward.