This study investigates the impact of multiple in-vehicle information systems on the driver. It was undertaken using a high fidelity driving simulator. The participants experienced, paced and unpaced single tasks, multiple secondary tasks and an equal period of 'normal' driving. Results indicate that the interaction with secondary tasks led to significant compensatory speed reductions. Multiple secondary tasks were shown to have a detrimental affect on vehicle performance with significantly reduced headways and increased brake pressure being found. The drivers reported interaction with the multiple in-vehicle systems to significantly impose more subjective mental workload than either a single secondary task or 'normal driving'. The implications of these findings and the need to integrate and manage complex in-vehicle information systems are discussed.

Train driving is primarily a visual task; train drivers are required to monitor the dynamic scene visually both outside and inside the train cab. Poor performance on this visual task may lead to errors, such as signals passed at danger. It is therefore important to understand the visual strategies that train drivers employ when monitoring and searching the visual scene for key items, such as signals. Prior to this investigation, a pilot study had already been carried out using an eye tracking technique to investigate train drivers' visual behaviour and to collect data on driver monitoring of the visual environment, Groeger et al. (2003) Pilot study of train drivers' eye movements, University of Surrey. However, a larger set of data was needed in order to understand more fully train driver visual behaviour and strategies. In light of this need, the Transport Research Laboratory produced a methodology for the assessment of UK train driver visual strategies, on behalf of the Rail Safety and Standards Board and applied this methodology to conduct a large-scale trial. The study collected a wealth of data on train drivers' visual behaviour with the aim of providing a greater understanding of the strategies adopted. The corneal dark-eye tracking system chosen for these trials tracks human visual search and scanning patterns, and was fitted to 86 drivers whilst driving in-service trains. Data collected include the duration and frequency of glances made towards different elements of the visual scene. In addition, the train drivers were interviewed after driving the routes, to try and understand the thought processes behind the behaviour observed. Statistical analysis of over 600 signal approaches was conducted. This analysis revealed that signal aspect, preceding signal aspect, signal type and signal complexity are important factors, which affect the visual behaviour of train drivers. Train driver interview data revealed that driver expectation also plays a significant role in train driving. The findings of this study have implications for the rail industry in terms of infrastructure design, design of the driving task and driver training. However, train driving is extremely complex and the data from this study only begin to describe and explain train driver visual strategies in the specific context of signal approaches. This study has provided a wealth of data and further analysis of it is needed to investigate the role of other factors and the complex relationships between factors during signal approaches and other driving situations systematically. Finally, there are important aspects of visual behaviour that cannot be examined using these data or this method. Investigation of other aspects of visual behaviour, such as peripheral vision, will require other methods such as simulation.

Occlusion is a practical technique to measure the visual demand imposed by in-vehicle tasks and to assess whether a task can be resumed having been interrupted. This study describes a number of important factors and variables that need to be controlled to ensure reliability of results. Training of participants on in-vehicle tasks is found to help consistency and five training sessions are required for complex tasks. No significant differences in training with and without occlusion goggles are reported. The required sample size is dependent on the variability of the task; for those investigated an appropriate sample size is found to be 14. For in-vehicle systems that exhibit a delay in response to the user, consistency is improved when these delays are excluded from timing measurements. In terms of calculating the occlusion parameter R, the within-participant basis is most consistent by taking the ratio of the respective median total shutter open time and total task times across trial repetitions completed by one participant on each task under evaluation and, for the purposes of identifying interface designs that exhibit poor resumability, the 85th percentile value is identified as most suitable. Findings from the study are discussed in terms of future application of the occlusion technique to assess in-vehicle information systems (IVIS).

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