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Technical Paper

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

2019-04-02
2019-01-1082
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.
Research Report

Unsettled Topics Concerning Human and Autonomous Vehicle Interaction

2020-12-18
EPR2020025
This report examines the current interaction points between humans and autonomous systems, with a particular focus on advanced driver assistance systems (ADAS), the requirements for human-machine interfaces as imposed by human perception, and finally, the progress being made to close the gap. Autonomous technology has the potential to benefit personal transportation, last-mile delivery, logistics, and many other mobility applications enormously. In many of these applications, the mobility infrastructure is a shared resource in which all the players must cooperate. In fact, the driving task has been described as a “tango” where we—as humans—cooperate naturally to enable a robust transportation system. Can autonomous systems participate in this tango? Does that even make sense? And if so, how do we make it happen? Click here to access the full SAE EDGETM Research Report portfolio.
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