# Ensemble Bayesian Decision Making with Redundant Deep Perceptual Control Policies

arXiv: https://arxiv.org/abs/1811.12555 · HTML: https://mlsanity.com/paper/1811.12555/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

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

- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review](https://mlsanity.com/paper/2107.12045/) (2021)
- [Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/2311.10747/) (2023)
- [A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks](https://mlsanity.com/paper/2205.09968/) (2022)
- [A Structured Review of Literature on Uncertainty in Machine Learning & Deep Learning](https://mlsanity.com/paper/2406.00332/) (2024)
- [Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning](https://mlsanity.com/paper/2004.08051/) (2020)
- [Giving Commands to a Self-Driving Car: How to Deal with Uncertain Situations?](https://mlsanity.com/paper/2106.04232/) (2021)
- [Autonomous Driving Small-Scale Cars: A Survey of Recent Development](https://mlsanity.com/paper/2404.06229/) (2024)
- [CONFINE: Conformal Prediction for Interpretable Neural Networks](https://mlsanity.com/paper/2406.00539/) (2024)
- [Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain](https://mlsanity.com/paper/2112.04684/) (2021)
- [Enhancing Robot Learning through Learned Human-Attention Feature Maps](https://mlsanity.com/paper/2308.15327/) (2023)
- [Ensemble based Closed-Loop Optimal Control using Physics-Informed Neural Networks](https://mlsanity.com/paper/2510.18195/) (2025)
- [Learning Causal States Under Partial Observability and Perturbation](https://mlsanity.com/paper/2512.00357/) (2025)

## Abstract (arXiv)

This work presents a novel ensemble of Bayesian Neural Networks (BNNs) for control of safety-critical systems. Decision making for safety-critical systems is challenging due to performance requirements with significant consequences in the event of failure. In practice, failure of such systems can be avoided by introducing redundancies of control. Neural Networks (NNs) are generally not used for safety-critical systems as they can behave in unexpected ways in response to novel inputs. In addition, there may not be any indication as to when they will fail. BNNs have been recognized for their ability to produce not only viable outputs but also provide a measure of uncertainty in these outputs. This work combines the knowledge of prediction uncertainty obtained from BNNs and ensemble control for a redundant control methodology. Our technique is applied to an agile autonomous driving task. Multiple BNNs are trained to control a vehicle in an end-to-end fashion on different sensor inputs provided by the system. We show that an individual network is successful in maneuvering around the track but crashes in the presence of unforeseen input noise. Our proposed ensemble of BNNs shows successful task performance even in the event of multiple sensor failures.