CUED Publications database

Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning

McAllister, RT and Gal, Y and Kendall, A and van der Wilk, M and Shah, A and Cipolla, R and Weller, A (2017) Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning. In: IJCAI-17, 2017-8-19 to 2017-8-25, Melbourne, Australia pp. 4745-4753..

Full text not available from this repository.


Autonomous vehicle (AV) software is typically composed of a pipeline of individual components, linking sensor inputs to motor outputs. Erroneous component outputs propagate downstream, hence safe AV software must consider the ultimate effect of each component’s errors. Further, improving safety alone is not sufficient. Passengers must also feel safe to trust and use AV systems. To address such concerns, we investigate three under-explored themes for AV research: safety, interpretability, and compliance. Safety can be improved by quantifying the uncertainties of component outputs and propagating them forward through the pipeline. Interpretability is concerned with explaining what the AV observes and why it makes the decisions it does, building reassurance with the passenger. Compliance refers to maintaining some control for the passenger. We discuss open challenges for research within these themes. We highlight the need for concrete evaluation metrics, propose example problems, and highlight possible solutions.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: technical: techniques technical: models social: challenges social: human-machine interaction
Divisions: Div F > Computational and Biological Learning
Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 21 Aug 2017 20:15
Last Modified: 02 Sep 2021 04:12
DOI: doi:10.24963/ijcai.2017/661