Analysis of the influence of non-driving-related activities on seat parameters and sitting posters

Abstract

Changing mobility scenarios are leading to innovative vehicle concepts. The absence of the driver has opened up a wide range of modified interiors and seating configurations for highly automated vehicles, which are the focus of research.With the ongoing automatization in the car industry, new questions arise about human factors. From SAE level 3, conditional driving automation allows the driver to disengage from the driving task without the need for supervision. With an increasing degree of automation, the active vehicle driver is transformed into a passive vehicle passenger. This gives the driver the possibility to deal with non-driving related activities and tasks (NDRA, NDRT) whenever the automation is active. The question of what people are likely to do during an automated ride has mostly been addressed via online surveys or by analyzing other means of transportation like train and bus. Various studies examining train or bus journeys using different methods such as (online) surveys or observation of passengers in different means of transport show a wide variety of activities such as listening to music, looking at the surroundings, relaxing, talking on the phone, reading or working, and the use of electronic devices such as laptops, tablets and smartphones [1][2]. Other studies additionally examined seating parameters such as seat and recline angle [3][4][5][6][7][8].However, knowing about desired activities allows researchers and developers to design future car interior including seat and seating position, internal HMI, air-conditioning and the automated driving functions according to user needs. Highly automated and autonomous vehicles enable different seating postures.Space in front of the seat allows the passenger more range for movement and postures [4]. Moreover, several studies contribute to the space managements of interior design in the future and show significant effects of NDRTs on driving postures concerning the seat positions and backrest angles [3,8].

A Study on Reducing Traffic Congestion in the Roadside Unit for Autonomous Vehicles Using BSM and PVD

“…These vehicles are based on advanced sensor technology [1][2][3], sophisticated artificial intelligence algorithms [3][4][5][6][7][8], and continuously evolving machine learning systems, enabling safe and efficient operation without driver intervention. These technologies offer a variety of benefits, including reducing traffic accidents, increasing the capacity of the road network, and improving overall traffic flow, which can improve the sustainable development of cities and quality of life [9]. Realizing this potential requires a deep understanding and systematic improvement of existing transportation systems and infrastructure.…”