# AUTO-DISCERN: Autonomous Driving Using Common Sense Reasoning

arXiv: https://arxiv.org/abs/2110.13606 · HTML: https://mlsanity.com/paper/2110.13606/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 17 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [DenseCap: Fully Convolutional Localization Networks for Dense Captioning](https://mlsanity.com/paper/1511.07571/) (2015)
- [Clingo = ASP + Control: Preliminary Report](https://mlsanity.com/paper/1405.3694/) (2014)
- [Constraint Answer Set Programming without Grounding](https://mlsanity.com/paper/1804.11162/) (2018)
- [The NVIDIA PilotNet Experiments](https://mlsanity.com/paper/2010.08776/) (2020)
- [Justifications for Goal-Directed Constraint Answer Set Programming](https://mlsanity.com/paper/2009.10238/) (2020)
- [A Physician Advisory System for Chronic Heart Failure Management Based on Knowledge Patterns](https://mlsanity.com/paper/1610.08115/) (2016)
- [Out of Sight But Not Out of Mind: An Answer Set Programming Based Online Abduction Framework for Visual Sensemaking in Autonomous Driving](https://mlsanity.com/paper/1906.00107/) (2019)
- [Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning about Moving Objects](https://mlsanity.com/paper/1712.00840/) (2017)

## Built on by (most cited first, 14 of 14)

- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Towards Knowledge-driven Autonomous Driving](https://mlsanity.com/paper/2312.04316/) (2023)
- [Reliable Natural Language Understanding with Large Language Models and Answer Set Programming](https://mlsanity.com/paper/2302.03780/) (2023)
- [Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2505.20223/) (2025)
- [DualAD: Dual-Layer Planning for Reasoning in Autonomous Driving](https://mlsanity.com/paper/2409.18053/) (2024)
- [EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning](https://mlsanity.com/paper/2403.10692/) (2024)
- [Assessing Drivers' Situation Awareness in Semi-Autonomous Vehicles: ASP based Characterisations of Driving Dynamics for Modelling Scene Interpretation and Projection](https://mlsanity.com/paper/2308.15895/) (2023)
- [Knowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Survey](https://mlsanity.com/paper/2502.10477/) (2025)
- [Preconditioned Visual Language Inference with Weak Supervision](https://mlsanity.com/paper/2306.01753/) (2023)
- [Mind the Gaps: Logical English, Prolog, and Multi-agent Systems for Autonomous Vehicles](https://mlsanity.com/paper/2502.09216/) (2025)
- [Commonsense Reasoning-Aided Autonomous Vehicle Systems](https://mlsanity.com/paper/2502.09233/) (2025)
- [Correcting Autonomous Driving Object Detection Misclassifications with Automated Commonsense Reasoning](https://mlsanity.com/paper/2601.04271/) (2026)
- [Reinforcement Learning Enhancement Using Vector Semantic Representation and Symbolic Reasoning for Human-Centered Autonomous Emergency Braking](https://mlsanity.com/paper/2602.05079/) (2026)
- [Multi-Agent Orchestration with the Common-Sense Reasoning Capabilities of LLMs for Autonomous Driving](https://mlsanity.com/paper/2608.20129/) (2026)

## Abstract (arXiv)

Driving an automobile involves the tasks of observing surroundings, then making a driving decision based on these observations (steer, brake, coast, etc.). In autonomous driving, all these tasks have to be automated. Autonomous driving technology thus far has relied primarily on machine learning techniques. We argue that appropriate technology should be used for the appropriate task. That is, while machine learning technology is good for observing and automatically understanding the surroundings of an automobile, driving decisions are better automated via commonsense reasoning rather than machine learning. In this paper, we discuss (i) how commonsense reasoning can be automated using answer set programming (ASP) and the goal-directed s(CASP) ASP system, and (ii) develop the AUTO-DISCERN system using this technology for automating decision-making in driving. The goal of our research, described in this paper, is to develop an autonomous driving system that works by simulating the mind of a human driver. Since driving decisions are based on human-style reasoning, they are explainable, their ethics can be ensured, and they will always be correct, provided the system modeling and system inputs are correct.