Commonsense Reasoning-Aided Autonomous Vehicle Systems

TL;DR

This paper explores integrating commonsense reasoning models with image data to enhance autonomous vehicle systems, aiming to improve their reasoning, explainability, and ethical decision-making capabilities.

Contribution

It introduces a novel approach combining commonsense reasoning with AV systems to address limitations of traditional machine learning methods.

Findings

Improved reasoning accuracy in AV systems

Enhanced explainability and ethical decision-making

Potential for more adaptable autonomous driving behaviors

Abstract

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.