Traffic congestion is a major societal challenge. By advising drivers on the optimal lane to drive, traffic flow can be improved, and congestion reduced. In this paper we describe the development of a lane-specific advice Human Machine Interface (HMI). Persuading drivers to follow an advice that is beneficial to the traffic situation, but may not be immediately beneficial to the drivers themselves, is challenging. In this paper we define persuasive elements to encourage drivers to follow the lane-specific advices. We then describe the interface design process, followed by its evaluation using a driving simulator study. In the simulator study, the effect of two types of persuasion are evaluated: a competitive variant where drivers could earn points and compete with others, and a cooperative variant where real-time information on the number of compliant drivers was available. Participants drove in the simulator on two days. Between days, the treatment groups viewed a Web-portal showing their performance and encouragement from an avatar. Those in the competitive and cooperative groups followed significantly more advices (117 and 111) than those in the control group (89). No significant differences were visible between competitive and cooperative groups. The differences between groups only emerged on the second day.@en

Persuasive in-vehicle systems aim to intuitively influence the attitudes and/or behaviour of a driver (i.e. without forcing them). However, the challenge in using these systems in a driving setting, is to maximise the persuasive effect without infringing upon the driver's safety. This paper proposes a conceptual model for driver persuasion at the tactical level (i.e., driver manoeuvring level, such as lane-changing and car-following). The main focus of the conceptual model is to describe how to safely persuade a driver to change his or her behaviour, and how persuasive systems may affect driver behaviour. First, existing conceptual and theoretical models that describe behaviour are discussed, along with their applicability to the driving task. Next, we investigate the persuasive methods used with a focus on the traffic domain. Based on this we develop a conceptual model that incorporates behavioural theories and persuasive methods, and which describes how effective and safe driver persuasion functions. Finally, we apply the model to a case study of a lane-specific advice system that aims to reduce travel time delay and traffic congestion by advising some drivers to change lanes in order to achieve a better distribution of traffic over the motorway lanes.
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Heart rate data are often collected in human factors studies, including those into vehicle automation. Advances in open hardware platforms and off-the-shelf photoplethysmogram (PPG) sensors allow the non-intrusive collection of heart rate data at very low cost. However, the signal is not trivial to analyse, since the morphology of PPG waveforms differs from electrocardiogram (ECG) waveforms and shows different noise patterns. Few validated open source available algorithms exist that handle PPG data well, as most of these algorithms are specifically designed for ECG data.

In this paper we present the validation of a novel algorithm named HeartPy, useful for the analysis of heart rate data collected in noisy settings, such as when driving a car or when in a simulator. We benchmark the performance on two types of datasets and show that the developed algorithm performs well. Further research steps are discussed.@en

The present study aims to add to the literature on driver workload prediction using machine learning methods. The main aim is to develop workload prediction on a multi-level basis, rather than a binary high/low distinction as often found in literature. The presented approach relies on measures that can be obtained unobtrusively in the driving environment with off-the-shelf sensors, and on machine learning methods that can be implemented in low-power embedded systems. Two simulator studies were performed, one inducing workload using realistic driving conditions, and one inducing workload with a relatively demanding lane-keeping task. Individual and group-based machine learning models were trained on both datasets and evaluated. For the group-based models the generalizing capability, that is the performance when predicting data from previously unseen individuals, was also assessed. Results show that multi-level workload prediction on the individual and group level works well, achieving high correct rates and accuracy scores. Generalizing between individuals proved difficult using realistic driving conditions but worked well in the highly demanding lane-keeping task. Reasons for this discrepancy are discussed as well as future research directions.

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The present study aims to add to the literature on driver workload prediction using machine learning methods. The main aim is to develop workload prediction on a multi-class basis, rather than a binary high/low distinction as often found in litearature. The presented approach relies on measures that can be obtained unobtrusively in the driving environment with off-the-shelf sensors, and on machine learning methods that can be implemented on low-power embedded systems. Two simulator studies were performed, one inducing workload using realistic driving conditions, and one inducing workload with a relatively demanding lane-keeping task. Individual and group-based machine learning models were trained on both datasets and evaluated. For the group-based models the generalising capability, that is the performance when predicting data from previously unseen individuals, was also assessed. Results show that multi-class workload prediction on the individual and group level works well, achieving high correct rates and accuracy scores. Generalising between individuals proved difficult using realistic driving conditions, but worked very well in the high demanding lane-keeping task. Reasons for this discrepancy are discussed as well as future research directions.@en

The use of in-car technology has become more prevalent, both as driver assistance systems as well as connectivity or entertainment systems. Driver assistance systems can be built-in, after-market or run on a smartphone. The challenge however, is to increase drivers’ compliance with these systems. Stimulating the driver to adopt certain behaviours over others is a growing area within driver assistance systems. These approaches have the potential to be very effective, but only when they attract or persuade road users to use it. This study has two main aims. The first, is to provide a review of the different approaches in the literature to influence driver behaviour using in-car technology. The second aim is to develop a conceptual model to guide the research efforts on influencing driver behaviour at the tactical level. A structured review was conducted exploring the literature available in the automotive, behavioural and traffic safety domains. First, we explored the available psychological models that describe behaviour and their applicability to the driving task. Following this, we investigated the methodological ways used to influence behaviour with a focus on the traffic domain. Finally, a conceptual model is presented which encompasses the behavioural basis, methods and techniques for influencing the behaviour using in-car technology and the strategic planning of behavioural change technology. The role of driver workload within the model is discussed. Possible applications using the composite model are discussed.@en

The aim of this research is to work towards building an open-source, platform-independent algorithm capable of predicting driver workload in real-time and in a non-intrusive way. To work towards a system that can also be implemented in on-road settings, we aimed at using off-the-shelf, non-intrusive sensors that could be implemented into the steering wheel and dashboard of current and future generations of cars, making them non-intrusive. In order to build the initial predictive model, a driving simulator experiment was performed. Nineteen participants were required to drive a virtual replication of the Dutch A67 C-ITS corridor between Eindhoven and Venlo. We attempted to induce driver workload by varying weather, traffic composition, traffic density and by asking participants to perform various manoeuvres such as lane changing, merging and exiting. We measured heart rate, skin response, blink and performance measures. Results show that within individuals and within the experimental group, workload was predictable with a high correct rate in both individual models as well as group models. We also evaluated how well the models would generalise when used outside of the experimental setting. Preliminary results for this generalisation are poor. We discuss possible reasons for this and next steps we are planning to take to increase this performance.@en

Objective: In this study, we assessed the use of portable navigation systems in everyday driving by applying in-vehicle naturalistic driving. Method: Experienced users of navigation systems, 7 females and 14 males, were provided with a specially equipped vehicle for approximately 1 month. Their trips were recorded using 4 cameras, Global Positioning System (GPS) data, and other sensor data. The drivers’ navigation system use data were coded from the video recordings, which showed how often and for how long the system was activated and how often and for how long a driver operated the system. Results: The system was activated for 23% of trips, predominantly on longer and unique trips. Analyses of the percentage of time for which the speed limit was exceeded showed no evidence of differences between trips for which the navigation system was used or not used. On trips for which the navigation system was activated, participants spent about 5% of trip time interacting with the device. About 40% of interacting behavior took place in the first 10% of the trip time, and about 35% took place while the car was standing still or moving at a very low speed; that is, 0–10 km/h. Conclusion: These results shed light on how and when drivers use navigation systems. They suggest that although drivers regulate their use of such systems to some extent, they often perform risky tasks while driving.

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