# An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice

arXiv: https://arxiv.org/abs/2003.00344 · HTML: https://mlsanity.com/paper/2003.00344/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2020.

## Built on by (most cited first, 13 of 13)

- [Neurosymbolic AI for Reasoning over Knowledge Graphs: A Survey](https://mlsanity.com/paper/2302.07200/) (2023)
- [RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2405.00449/) (2024)
- [A Semantic Web Technology Index](https://mlsanity.com/paper/2201.07034/) (2022)
- [A Study of Situational Reasoning for Traffic Understanding](https://mlsanity.com/paper/2306.02520/) (2023)
- [BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving](https://mlsanity.com/paper/2401.01065/) (2024)
- [A Survey on Knowledge Graph-based Methods for Automated Driving](https://mlsanity.com/paper/2210.08119/) (2022)
- [Spatial Retrieval Augmented Autonomous Driving](https://mlsanity.com/paper/2512.06865/) (2025)
- [Ontology-based n-ball Concept Embeddings Informing Few-shot Image Classification](https://mlsanity.com/paper/2109.09063/) (2021)
- [Rate-Informed Discovery via Bayesian Adaptive Multifidelity Sampling](https://mlsanity.com/paper/2411.17826/) (2024)
- [Knowledge-based Entity Prediction for Improved Machine Perception in Autonomous Systems](https://mlsanity.com/paper/2203.16616/) (2022)
- [Utilizing Background Knowledge for Robust Reasoning over Traffic Situations](https://mlsanity.com/paper/2212.07798/) (2022)
- [Knowledge Graphs: The Future of Data Integration and Insightful Discovery](https://mlsanity.com/paper/2502.15689/) (2024)
- [Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI](https://mlsanity.com/paper/2411.03225/) (2024)

## Abstract (arXiv)

The autonomous driving (AD) industry is exploring the use of knowledge graphs (KGs) to manage the vast amount of heterogeneous data generated from vehicular sensors. The various types of equipped sensors include video, LIDAR and RADAR. Scene understanding is an important topic in AD which requires consideration of various aspects of a scene, such as detected objects, events, time and location. Recent work on knowledge graph embeddings (KGEs) - an approach that facilitates neuro-symbolic fusion - has shown to improve the predictive performance of machine learning models. With the expectation that neuro-symbolic fusion through KGEs will improve scene understanding, this research explores the generation and evaluation of KGEs for autonomous driving data. We also present an investigation of the relationship between the level of informational detail in a KG and the quality of its derivative embeddings. By systematically evaluating KGEs along four dimensions -- i.e. quality metrics, KG informational detail, algorithms, and datasets -- we show that (1) higher levels of informational detail in KGs lead to higher quality embeddings, (2) type and relation semantics are better captured by the semantic transitional distance-based TransE algorithm, and (3) some metrics, such as coherence measure, may not be suitable for intrinsically evaluating KGEs in this domain. Additionally, we also present an (early) investigation of the usefulness of KGEs for two use-cases in the AD domain.