Towards Fully Autonomous Driving with Automated Commonsense Reasoning

Keegan Kimbrell, Tianhao Wang, Gopal Gupta, Feng Chen

Keywords: Commonsense Reasoning, Autonomous Vehicles, Uncertainty

TL;DR: This paper proposes the use of commonsense reasoning to handle various object misclassifications in an autonomous vehicle's perception model.

Abstract:

Autonomous Vehicle (AV) technology has been heavily researched and sought after, yet there are no SAE Level 5 AVs available today in the marketplace. We contend that over-reliance on machine learning technology is the main reason. Use of automated commonsense reasoning technology, we believe, can help achieve SAE Level 5 autonomy. In this paper, we show how automated commonsense reasoning technology can be deployed in situations where not enough data is available to train a machine learning model for autonomous driving. Specifically, we consider two situations where (i) a traffic signal is malfunctioning at an intersection and (ii) all the cars ahead are slowing down and steering away due to an unexpected obstruction (e.g., animals on the road). We show that in such situations, our commonsense reasoning based solution performs correctly. We also provide a pathway for efficiently invoking commonsense reasoning by measuring uncertainty in the computer vision model and using commonsense reasoning to handle uncertain scenarios. We describe our experiments conducted using the CARLA simulator and the results obtained. The main contribution of our research is to show that automated commonsense reasoning provides an effective pathway to reach SAE level 5 automation.

Primary Area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)

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Submission Number: 11492