Some statistical challenges in automated driving systems

Author:

Caballero William N.1,Rios Insua David2,Naveiro Roi23ORCID

Affiliation:

1. U.S. Air Force Academy Colorado Colorado Springs USA

2. ICMAT‐CSIC C. Nicolás Cabrera Madrid Madrid Spain

3. CUNEF Universidad C. de Leonardo Prieto Castro Madrid Madrid Spain

Abstract

AbstractAutomated driving systems are rapidly developing. However, numerous open problems remain to be resolved to ensure this technology progresses before its widespread adoption. A large subset of these problems are, or can be framed as, statistical decision problems. Therefore, we present herein several important statistical challenges that emerge when designing and operating automated driving systems. In particular, we focus on those that relate to request‐to‐intervene decisions, ethical decision support, operations in heterogeneous traffic, and algorithmic robustification. For each of these problems, earlier solution approaches are reviewed and alternative solutions are provided with accompanying empirical testing. We also highlight open avenues of inquiry for which applied statistical investigation can help ensure the maturation of automated driving systems. In so doing, we showcase the relevance of statistical research and practice within the context of this revolutionary technology.

Funder

Air Force Office of Scientific Research

AXA Research Fund

European Office of Aerospace Research and Development

Fundación BBVA

Horizon 2020 Framework Programme

Ministerio de Ciencia y Tecnología

National Science Foundation

Publisher

Wiley

Subject

Management Science and Operations Research,General Business, Management and Accounting,Modeling and Simulation

Reference66 articles.

1. AMSTAT.Statistical science improving transportation; 2012.https://www.amstat.org/asa/files/pdfs/StatSig/StatSigTransportation.pdf

2. HuqN VosselerR SwimmerM.Cyberattacks against intelligent transportation systems. Technical report. Trend Micro; 2017.

3. JeffreyHH amdE VijayB KendallA.Reimagining an autonomous vehicle. arXiv preprint arXiv:2018.05805 2021.

4. BMW.The path to autonomous driving; 2023.https://www.bmw.com/en/automotive‐life/autonomous‐driving.html

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