Experimental Setup Enabling Self-Confrontation Interviews for Modelling Naturalistic Driving Behavior

Abstract

Behavioral models of traffic actors have a potential of unlocking sophisticated safety features and mitigating several challenges of urban automated driving. Intuitively, volunteers driving on routes of daily commuting in their private vehicles are the preferred source of information to be captured by data collection system. Such dataset can then serve as a basis for identifying efficient methods of context representation and parameterization of behavioral models. This paper describes the experimental setup supporting the development of driver behavioral models within the SIMUSAFE project. In particular, the paper presents an IoT data acquisition and analysis infrastructure supporting self-confrontation interviews with drivers. The proposed retrofit system was installed in private vehicles of volunteers in two European cities. Wherever possible, the setup used open source software and electronic components available on consumer market. Collected data about traffic context and driver behavior were automatically uploaded to cloud storage for immediate analysis by traffic psychologists and support of self-confrontation interviews. The timely availability of data for analysis and a very limited impact of the system on driver behavior are the key contributions of the proposed solution.