Abstract
Increasing application areas and depths of autonomous systems in logistics provide a new level of challenge for the analysis and design of human–machine interaction concepts. Due to scarce high-skilled personnel in several regions and the objectives of efficiency and sustainability improvement, logistics operators have to pursue technological progress like automation with all means. In order to distinguish between more or less performing human–artificial collaboration systems in logistics ex ante for investment decision purposes, a multi-dimensional conceptual framework is developed. A comprehensive case study regarding automated truck driving in logistics is provided in order to test the concept concerning practical implications. Results include the notion of four distinctive and increasing resistance levels before finally an efficient ‘trusted’ collaboration between human operators and artificial intelligence systems can be achieved. This is important for the design of many automated systems in logistics, among others for driving and piloting professions regarding autonomous driving supervision.
| Original language | English |
|---|---|
| Pages (from-to) | 224-242 |
| Number of pages | 19 |
| Journal | International Journal of Logistics Research and Applications |
| Volume | 21 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 4 May 2018 |
Keywords
- acceptance model
- Automation in logistics
- autonomous driving
- human–machine interaction
- trusted collaboration human–artificial performance analysis
- n/a OA procedure