Joseph SmythThe University of Warwick · Warwick Manufacturing Group
Joseph Smyth
Doctor of Engineering
Ex-academic. Human Factors Lead, Automotive Industry.
About
11
Publications
3,227
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151
Citations
Introduction
Additional affiliations
October 2015 - September 2019
Publications
Publications (11)
To ensure transferability of driving simulator-based user trials (where motion sickness onset is likely) it is important to understand if motion sickness affects human performance and therefore user trial data validity. 51 participants had their task performance ability measured in six defined categories (including physical, cognitive, visual and t...
Everyone can be susceptible to motion sickness (except those with complete loss of labyrinth function) and around one in three are known to be servery susceptible. Motion sickness can be experienced in many domains, including car travel, on a boat, using virtual reality headsets and simulator use amongst others. It is expected that due to potential...
Motion sickness (MS) is known to be a potentially limiting factor for future self-driving vehicles – specifically in regards to occupant comfort and well-being. With this as a consideration comes the desire to accurately measure, track and even predict MS state in real-time. Previous research has considered physiological measurements to measure MS...
Driver state monitoring (DSM) systems aim to measure driver/occupant state, considering factors such as fatigue, workload, attentiveness, and wellbeing. They are influential for some vehicles on the road today, but as we move towards higher levels of automation their use is expected to become even more important. Uncertainty around public perceptio...
Traffic crashes remain a leading cause of accidental human death where aggressive driving is a significant contributing factor. To review the driver’s performance presented in aggressive driving, this systematic review screens 2412 pieces of relevant literature, selects and synthesizes 31 reports with 34 primary studies that investigated the driver...
While it is widely agreed that automated and autonomous vehicles may provide safety benefits over vehicles with lower level or no automation, due to other road users there will still likely be situations where a collision is unavoidable. What should a vehicle that is operating autonomously do when it has no choice but to have a collision? And who s...
Almost everyone can experience motion sickness and one third of the population are highly susceptible. With growing development and popularity of technologies such as self-driving cars, simulators and virtual reality (VR), motion sickness management will be more of a consideration in the future than ever before. People who are susceptible to motion...
The purpose of this document is to highlight and evidence the benefits of applying a human
factors approach to incident investigation and to share a complementary perspective on the
events surrounding the Mountain View crash
A proposed benefit of self-driving cars is that of increased comfort and productivity of the occupants. Self-driving vehicle concepts and published research show the desire for engagement in non-driving related tasks while traveling in such vehicles. Based on survey results and financial productivity estimations, it is likely that completing work a...
Using machine learning techniques, it is possible to learn and subsequently automate certain driver-focused features in consumer vehicles. A human factors approach is taken to review current machine learning systems. Subsequently, it is found that current methods used for machine learning involve long learning times and might not be sufficient to g...
Questions
Question (1)
Measuring driver workload subjectively is a useful way of further understanding the effects of (for example) changing environmental conditions throughout a journey. In a driving simulator study where a participants shall experience multiple 'interventions' within the same homogeneous driving scenario it would be useful to gain a quick understanding of perceived workload for each intervention. For example, drive for 5 minutes under condition X, measure workload for condition X, continue to drive into condition Y, measure workload for condition Y ... etc...
Methods such as a Driver Activity Load Index (DALI) are very useful when participants have time to stop a scenario and engage with this pen-and-paper method directly after each intervention. However, when running multiple interventions back-to-back are there any recommended metrics (non physiology-based) through which one can measure perceived workload through verbal questioning/answering in a time-efficient manner?
