# Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

arXiv: https://arxiv.org/abs/2205.04712 · HTML: https://mlsanity.com/paper/2205.04712/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2022.

## Built on by (most cited first, 11 of 11)

- [Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art](https://mlsanity.com/paper/2503.08695/) (2025)
- [BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving](https://mlsanity.com/paper/2401.01065/) (2024)
- [OpenREAD: Reinforced Open-Ended Reasoning for End-to-End Autonomous Driving with LLM-as-Critic](https://mlsanity.com/paper/2512.01830/) (2025)
- [Plausibility Verification For 3D Object Detectors Using Energy-Based Optimization](https://mlsanity.com/paper/2211.05233/) (2022)
- [Generative AI for Autonomous Driving: A Review](https://mlsanity.com/paper/2505.15863/) (2025)
- [Informed Priors for Knowledge Integration in Trajectory Prediction](https://mlsanity.com/paper/2211.00348/) (2022)
- [Informed Spectral Normalized Gaussian Processes for Trajectory Prediction](https://mlsanity.com/paper/2403.11966/) (2024)
- [Hybrid Convolutional Neural Networks with Reliability Guarantee](https://mlsanity.com/paper/2405.05146/) (2024)
- [Knowledge Graphs: The Future of Data Integration and Insightful Discovery](https://mlsanity.com/paper/2502.15689/) (2024)
- [Offline Reinforcement Learning using Human-Aligned Reward Labeling for Autonomous Emergency Braking in Occluded Pedestrian Crossing](https://mlsanity.com/paper/2504.08704/) (2025)
- [Beyond One Model Fits All: Ensemble Deep Learning for Autonomous Vehicles](https://mlsanity.com/paper/2312.05759/) (2023)

## Abstract (arXiv)

The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models. However, in real life applications these models often encounter scenarios that are inadequately represented in the data used for training. There are various reasons for the absence of sufficient data, ranging from time and cost constraints to ethical considerations. As a consequence, the reliable usage of these models, especially in safety-critical applications, is still a tremendous challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches. Knowledge augmented machine learning approaches offer the possibility of compensating for deficiencies, errors, or ambiguities in the data, thus increasing the generalization capability of the applied models. Even more, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-driven models with existing knowledge. The identified approaches are structured according to the categories knowledge integration, extraction and conformity. In particular, we address the application of the presented methods in the field of autonomous driving.