A dynamic assessment framework for the safe performance of Sidewalk Autonomous Delivery Robots on public sidewalks

A dynamic assessment framework for the safe performance of Sidewalk Autonomous Delivery Robots on public sidewalks: Focus area: the Netherlands

Beekes, Sebastiaan (TU Delft Civil Engineering & Geosciences)

Tavasszy, Lorant (graduation committee)
Annema, J.A. (graduation committee)
van Binsbergen, A.J. (graduation committee)
Klein, Tim (mentor)

Delft University of Technology

Transport, Infrastructure and Logistics

2022-08-26

Testing Sidewalk Autonomous Delivery Robot (SADR) performance in real world conditions is important to prove whether the innovation is ready for large-scale adoption. Currently, testing SADRs in public is not allowed in the Netherlands, because little is known about the actual risks associated with delivery robots, and the risks are currently difficult to assess because the robots are continuously evolving. Since the adoption of SADRs can reduce the negative effects of last mile logistics, in this article a theoretical dynamic assessment framework for the safe and sustainable performance of delivery robots is proposed. It is explored that there exist different degrees of relative difficulty within the Operational
Design Domain of SADRs and a set of adapted metrics to objectively and completely quantify and qualify factors that cause this relative difficulty is drawn up. On this basis, the dynamic assessment framework has been developed. Both the theory of relative Operational Design Domain Difficulty and the proposed dynamic assessment framework have been validated with experts. Delivery robots can be safely tested in public, because based on the dynamic assessment framework and a living lab approach, associated risks can be constantly minimised.

automated driving
dynamic Operational Design Domain
intelligent vehicles
living lab approach
performance assessment
safety
Sidewalk Autonomous Delivery Robot

http://resolver.tudelft.nl/uuid:521fec4e-1cf3-4b8e-a6c1-e3ca78ec9092

Student theses

master thesis

© 2022 Sebastiaan Beekes