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Basically, three research methodologies were used to provide answers to the research questions: survey, driving simulator and traffic simulation. This section discusses some directions for further research related to the issues that came across during these research parts. These issues concern both content and methodology.

9.2.1 Survey and user needs

The user needs survey in this thesis reflected the needs of the driver with respect to driver assistance. Based on the results, one can expect that integrated driver support systems are sensible. However, more research into user needs for integrated systems is advisable. For example, in our survey the respondents had to formulate their ideal system by making a trade- off between the driving tasks and situations that the system should support. For future research, it is recommended to formulate the ideal system on a more detailed level in terms of driver support functions instead of driving tasks and situations.

Our survey was distributed via the Internet. Despite the many benefits, selection bias can be regarded as a drawback of Internet questionnaires. Up to now, the Internet is less accessible to elderly people. Therefore, the opinion of an important group of drivers was missing in this

user needs survey. Further research should concentrate on this group, preferably using other data collection methods (e.g. face-to-face interviews, paper questionnaires).

The user needs survey included questions about the needs for driver support functions and the design of an ideal driver support system. This approach provided more insight into when and how car drivers want to be assisted by their cars during driving. However, it did not enable to study why exactly the respondents indicated these needs. For example, are the needs related to perceived difficulties of certain driving tasks or perceived risks of technology failure? Other (follow-up) methodologies might be better suited to provide this insight, such as in-depth interviews and focus groups.

9.2.2 Driving simulator and impacts on the driver

The participants gained experience with the Stop & Go of the Congestion Assistant in the driving simulator. In the traffic jam, the system maintained a time headway of 1.0 s. Although the participants would maintain a larger time headway themselves, they expressed great appreciation of the Stop & Go. Hence they accept closely following other vehicles in a traffic jam. However, these findings do not tell whether drivers will accept being closely followed by others. Further research is needed to study this aspect, preferably in a field study for its high validity.

Next to driving behaviour and mental workload, acceptance of and willingness to buy the Congestion Assistant were studied in a driving simulator, since the system is not available on the market. This provided valuable information about the initial reactions of drivers to the system. However, actual purchase and usage behaviour could not be examined. Future studies should therefore focus on factors influencing the purchase and usage of in-vehicle systems in reality.

Relations between user needs for congestion assistance and acceptance of the Congestion Assistant were investigated. It was found that expressing user needs for driver assistance can be considered a condition for actually accepting this technology. It might be interesting to also study possible relations between user needs and driving behaviour or mental workload. For example, drivers with a negative attitude towards congestion assistance might react differently to the Congestion Assistant, for instance showing less obedience to the Active pedal or a higher mental workload during the Stop & Go. To elaborate on this, a larger sample of participants with emphasis on driver characteristics, such as gender and age, would enable to examine whether these characteristics also affect the behavioural reactions to the Congestion Assistant.

When approaching the traffic jam with the Congestion Assistant, the driver could simultaneously receive congestion warnings on the display by the Warning function and feel a counterforce of the gas pedal by the Active pedal. However, it was assumed that particularly the Active pedal affected the driving behaviour in this situation, since it was more compelling than the Warning function. To verify this assumption, more knowledge is desired of the influence of congestion warnings alone on driving behaviour when running into congestion. In the driving simulator experiment, but also in the traffic simulation study, the safety effects of the Congestion Assistant were assessed using indicators, such as time headway and Time- To-Collision (TTC). These indicators have generally accepted boundaries on what is safe or not. For example, time headways smaller than 1 s and TTCs smaller than 4 s indicate

potentially dangerous situations. However, it was noted that small time headways or TTCs during automatic driving can be considered less dangerous than small values during manual driving, since the automatic system (e.g. the Stop & Go) is able to eliminate undesirable human behaviour, such as large reaction times. Further research is needed to develop adequate safety indicators and to come to new accepted boundaries for safety to fairly compare manual and supported driving.

9.2.3 Traffic simulation and impacts on the traffic flow

The traffic flow effects of the Congestion Assistant were assessed using the ITS Modeller. The simulated data concerning the onset of congestion showed a satisfactory resemblance with empirical data. However, further model calibrations and adaptations are desired to improve the simulation outcomes in congested traffic conditions, for example by including more variance of individual driving behaviour in congestion. Moreover, the ITS Modeller could benefit from the development of more traffic flow indicators, such as shockwave information and statistics related to an in-vehicle system (e.g. information about the time and location that the system was active).

In Chapter 8, it was suggested to also study other versions of the Stop & Go and the Active pedal. To elaborate on this, the Congestion Assistant could be extended towards a full speed range support system. For example, the Active pedal could operate like an ACC: instead of giving haptic feedback, it might automatically regulate speed using throttle and brake. And besides taking into account the tail of a traffic jam, it would take into account the predecessor and regulate the headway accordingly. This way, the longitudinal control of the ACC / Stop & Go is based on the traffic situation ahead, so that the driver (or better: the vehicle) can anticipate a jam, before the driver is able to see it. Another challenge is to adapt the working of the Congestion Assistant in such a way that it behaves more as a proactive system trying to avoid the onset of congestion instead of the current reactive system that starts acting after a traffic jam has been formed. Of course, these suggestions for the Congestion Assistant differ significantly from the version that has been studied in this research. Therefore, the impacts of such new versions on the traffic flow, but also on the driver, should be thoroughly investigated.