What drives the acceptance of autonomous driving? An investigation of acceptance factors from an end-user's perspective

By Ilja Nastjuk, Bernd Herrenkind, Mauricio Marrone, A. Brendel, L. Kolbe. 2020

302 citations recorded by the source corpus.

Abstract Autonomous driving is believed to provide numerous benefits for individuals and society, including increased road safety, reduced traffic congestion, and an improved ecological footprint. However, many barriers still hinder the widespread acceptance of autonomous vehicles. Research has proposed governmental policy strategies to accelerate the diffusion of autonomous driving, but less is known about end-user perceptions of this innovative technology. First, we employ a qualitative research design to identify the elements attributed to individual acceptance of autonomous driving. Furthermore, we organize a research model based on the technology acceptance model, validated with an online survey of 316 participants. The findings reveal how social influence, system characteristics, and individual factors determine individual acceptance of autonomous driving. The research helps to strengthen the existing body of knowledge by highlighting individual perceptions, with implications for practitioners.

Subfields: Automotive Engineering, Information Systems and Management, Social Psychology

View Paper: https://www.semanticscholar.org/paper/7271bcf34352c46045753bb704a4c00f4718499d

Citations in the corpus

• A Survey on 3D Gaussian Splatting (2024, unknown): “3D GS based methods [7, 36, 41, 62, 78, 94, 102, 111, 124, 126, 131, 143, 197, 200, 229, 238, 265, 266, 273] for dynamic scene reconstruction can generally be divided into two main categories as discussed in Sec.”