8 citations · 29 across the 23 of their papers we have counts for
6 papers · 1 filter
Perceived risk evolution in automated driving inferred from large-scale discrete ratings
Xiaolin He, Zirui Li, Xinwei Wang +2
Perceived risk in automated driving is often measured as discrete scores that summarise riding experience but this obscures volatile peaks from sustained elevation. Here we treat d…
Modelling human seat contact interaction for vibration comfort
Raj Desai, Marko Cvetković, Georgios Papaioannou +1
The seat to head vibration transmissibility depends on various characteristics of the seat and the human body. One of these, is the contact interaction, which transmits vibrational…
Computationally efficient human body modelling for real time motion comfort assessment
Raj Desai, Marko Cvetković, Junda Wu +2
Due to the complexity of the human body and its neuromuscular stabilization, it has been challenging to efficiently and accurately predict human motion and capture posture while be…
A new computational perceived risk model for automated vehicles based on potential collision avoidance difficulty (PCAD)
Xiaolin He, Riender Happee, Meng Wang
Perceived risk is crucial in designing trustworthy and acceptable vehicle automation systems. However, our understanding of its dynamics is limited, and models for perceived risk d…
Simulating vibration transmission and comfort in automated driving integrating models of seat, body, postural stabilization and motion perception
Riender Happee, Raj Desai, Georgios Papaioannou
To enhance motion comfort in (automated) driving we present biomechanical models and demonstrate their ability to capture vibration transmission from seat to trunk and head. A comp…
Evaluation of motion comfort using advanced active human body models and efficient simplified models
Raj Desai, Marko Cvetković, Georgios Papaioannou +1
Active muscles are crucial for maintaining postural stability when seated in a moving vehicle. Advanced active 3D non-linear full body models have been developed for impact and com…