# Automatic AI controller that can drive with confidence: steering vehicle with uncertainty knowledge

arXiv: https://arxiv.org/abs/2404.16893 · HTML: https://mlsanity.com/paper/2404.16893/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [Rare event failure test case generation in Learning-Enabled-Controllers](https://mlsanity.com/paper/2206.05533/) (2022)
- [Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression](https://mlsanity.com/paper/2211.10360/) (2022)
- [Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design](https://mlsanity.com/paper/2302.14732/) (2023)
- [Search for universal minimum drag resistance underwater vehicle hull using CFD](https://mlsanity.com/paper/2302.09441/) (2023)
- [Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey](https://mlsanity.com/paper/2004.05289/) (2020)
- [Reduced Robust Random Cut Forest for Out-Of-Distribution detection in machine learning models](https://mlsanity.com/paper/2206.09247/) (2022)
- [Modeling and Optimization of a Longitudinally-Distributed Global Solar Grid](https://mlsanity.com/paper/2206.05584/) (2022)
- [Fusion of ML with numerical simulation for optimized propeller design](https://mlsanity.com/paper/2302.14740/) (2023)
- [Web-Based Platform for Evaluation of Resilient and Transactive Smart-Grids](https://mlsanity.com/paper/2206.05550/) (2022)
- [Malaria detection using Deep Convolution Neural Network](https://mlsanity.com/paper/2303.03397/) (2023)
- [Vehicle lateral control using Machine Learning for automated vehicle guidance](https://mlsanity.com/paper/2303.08187/) (2023)

## Built on by (most cited first, 1 of 1)

- [Generative AI for CAD Automation: Leveraging Large Language Models for 3D Modelling](https://mlsanity.com/paper/2508.00843/) (2025)

## Abstract (arXiv)

In safety-critical systems that interface with the real world, the role of uncertainty in decision-making is pivotal, particularly in the context of machine learning models. For the secure functioning of Cyber-Physical Systems (CPS), it is imperative to manage such uncertainty adeptly. In this research, we focus on the development of a vehicle's lateral control system using a machine learning framework. Specifically, we employ a Bayesian Neural Network (BNN), a probabilistic learning model, to address uncertainty quantification. This capability allows us to gauge the level of confidence or uncertainty in the model's predictions. The BNN based controller is trained using simulated data gathered from the vehicle traversing a single track and subsequently tested on various other tracks. We want to share two significant results: firstly, the trained model demonstrates the ability to adapt and effectively control the vehicle on multiple similar tracks. Secondly, the quantification of prediction confidence integrated into the controller serves as an early-warning system, signaling when the algorithm lacks confidence in its predictions and is therefore susceptible to failure. By establishing a confidence threshold, we can trigger manual intervention, ensuring that control is relinquished from the algorithm when it operates outside of safe parameters.