Definition and improvements of real-time interventions to implement the Safety Tolerance Zone concept of H2020 i-DREAMS project

The H2020 i-DREAMS project aims to establish a framework for the definition, development, testing, and validation of a context-aware safety envelope for driving called the "Safety Tolerance Zone" (STZ). Main goal of i-DREAMS project is to create a platform that, considering driver background factors and real-time risk indicators related to driving performance, continuously monitors and assess in real-time the driver state and driving task complexity, to determine whether the driver is within the acceptable boundaries of safe operation. In addition, safety-oriented interventions will be developed to inform or warn the driver in real-time and at an aggregated level after the trip via an app- and web-based gamification coaching platform (post-trip intervention).The main purpose of this paper is to present the efforts and improvements made during the i-DREAMS project in relation to real-time intervention: it highlights the progress made in the selection of indicators, the definition of real-time interventions and the thresholds of the indicators used. All these elements have been updated and finalised in the last months in view of the real needs for the implementation of the i-DREAMS Safety Tolerance Zone (STZ).</div

Effectiveness of real-time and post-trip interventions from the H2020 i-DREAMS naturalistic driving project: a sneak preview

This paper addresses the effectiveness of real-time and post-trip interventions from the H2020 i-DREAMS naturalistic driving project. The project aims to setup a framework for the definition, development and validation of a context-aware ‘safety tolerance zone (STZ)’ for driving. A range of sensors are used to collect a large variety of data, which enables assessment of the STZ, and help designing interventions. Effectiveness evaluation is based on the outcome and process evaluation using the COM-B and REAIM frameworks, respectively. Preliminary results for a sample of 27 car drivers from Belgium and 26 car drivers from UK are presented. Overall, the interventions were found effective in improving driving behaviour. UK drivers have performed significantly better, while Belgian car drivers showed mixed results, mainly due to the changing mobility patterns during COVID19 pandemic. </p

Effectiveness evaluation of the interventions. Deliverable 7.2 of the EC H2020 project i-DREAMS

The overall objective of the i-DREAMS project is to set up a framework for the definition, development, testing and validation of a context-aware safety envelope for driving (‘Safety Tolerance Zone’), within a smart Driver, Vehicle & Environment Assessment and Monitoring System (i-DREAMS). Taking into account driver background factors and real-time risk indicators associated with the driving performance as well as the driver state and driving task complexity indicators, a continuous real-time assessment was made to monitor and determine if a driver is within acceptable boundaries of safe operation (i.e., Safety Tolerance Zone). Moreover, the i-DREAMS platform offers a series of in-vehicle interventions, meant to prevent drivers from getting too close to the boundaries of unsafe operation and to bring them back into the safety tolerance zone while driving. This deliverable focusses on evaluating the effectiveness of the i-DREAMS interventions in improving drivers’ safety outcomes. The work here will evaluate the impact of the real-time driver interventions, post-trip driver feedback, and gamification interventions, in order to assess their impact on driving behaviour and driver state. Comparisons will be made between the different countries for which data are available, between the different interventions, and between the different outcome variables. The data collected in on-road field trials are analysed for private drivers (passenger cars) and professional drivers (trucks and busses). The analysis of the interventions is formed of two main areas: outcome evaluation and process evaluation. Outcome evaluation, also known as effect evaluation, measures the effectiveness of the intervention. More specifically, it assesses whether the targeted factors of the on-road trials changed as a result of the intervention or not. The outcome evaluation of the on-road trials will examine whether the i-DREAMS interventions influenced the following four areas: safety outcomes, safety promoting goals, performance objectives, and change objectives. These four areas are part of the logic model of change behind the i-DREAMS interventions. Process evaluation assesses which parts of the intervention were implemented as intended, and which were not. For private drivers, the results show that there was a statistically significant decrease in events from Phase 1 to Phase 4. This suggests that the i-DREAMS system had a positive impact on the measured safety outcomes and succeeded in keeping drivers in the first level of the STZ for more of their journey. When individual phase changes are considered, the most significant results were seen from Phase 3 to Phase 4. This suggest that the addition of the gamification elements had a significant impact on safety outcomes, and further supports the conclusion that the full system provides the most effective results. However, differences were found when each country was analysed individually, which were statistically significant, though there is not a clear reason why this would be so. Furthermore, differences were also found between drivers within countries. In each country, between two thirds to three quarters of drivers showed improved outcomes (i.e., a reduction in events), but the remainder had worse outcomes (an increase in events). It’s not clear from the data why some individuals responded positively to the technology and others did not, and further work is needed to understand why the system has such varied effects on different drivers. For all countries, drivers engaged more with the app in Phase 4 of the trial compared with Phase 3, after the introduction of the gamification features. Although the ‘trips’ and ‘scores’ menu were the functions most used by drivers (functions that were available in both phases), the data suggests that the gamification functions were more engaging and held attention more consistently. The generic information in the app (hints, tips etc.) was less appealing to users. They found more interest in personalised feedback such as their trip information, goals, and position on the leader board. The data also suggests a link between app usage and performance outcome; nearly all the drivers who used the app heavily showed improved outcomes. It would be interesting to investigate this further to determine whether there is a causal effect between these results. Generally, the i-DREAMS system showed less positive impact with professional drivers compared to private drivers. Specifically, a lower proportion of the professional drivers showed improved outcomes, and little significant change was seen in terms of safety outcomes. Where there were significant results, these were most often increases in events, i.e., worse outcome. Again, it is not obvious why this result is observed. The only statistically significant improved outcome was for truck drivers, which was for ‘total’ high severity events specifically between Phase 1 and Phase 2. Therefore, it can tentatively be concluded that the system had a positive impact on the most severe events. Process evaluation results were only available for Truck drivers, but showed similar results to private drivers, with more app engagement in Phase 4 compared to Phase 3, after the introduction of gamification features. This further supports the value of gamification features. The intention was to use the results to inform the ranking of interventions and provide an assessment of which intervention schemes are most effective. However, given the varied results between countries and transport modes, it is difficult to conclude a definitive ranking of the different interventions. The results indicate that the full system (real-time warnings plus app feedback plus gamification features in the app) provides the most significant positive impact on driver outcome. For private drivers, the analysis showed that most significant positive change was seen in Phase 4 of the trial, i.e., the gamification features, however it cannot be said that those alone were the most effective, as they were tested in combination with the other interventions. However, the data does suggest that app feedback on its own is less effective than when the app also includes gamification features. For truck drivers, we can tentatively conclude that the real-time interventions had the most impact, however more data is needed to support this. Lastly, the rail mode was included in i-DREAMS to broaden the application of the i-DREAMS platform which was originally designed for use in road vehicles. Trams operate within a mixed-traffic environment, driving on both segregated track, and shared, multi-user road. Therefore, aspects of the i-DREAMS platform can be applied to trams and may be beneficial to tram driving safety and risk mitigation. Two main studies were carried out to assess the use of the i-DREAMS platform in trams. The first was a simulator study to test the real-time element of the platform and the second was a focus group study to assess the potential use of the post-trip feedback app in the tram context. The tram simulator study suggests that the i-DREAMS system and associated warnings offer several benefits for tram driving operations. Firstly, as instances of speeding are rare, the speed alert would be more helpful as a warning before the occurrence of speeding, alerting the drivers they are approaching the limit, or more effective as a constant in-cab reminder of the current speed limit. The concept of a vulnerable road user (VRU) warning could be beneficial to tram drivers operating in mixed traffic environments, however, it was clear that the VRU warning needs to be developed to take into account specific aspects of tram driving and there is a concern about it being triggered too often. The fatigue warning could also potentially be beneficial as a warning before the existing fatigue monitoring device alerts, as a prompt to drivers to consider their alertness or take a break. Tram drivers suggested that the app would be most useful in identifying issues that were common to drivers and as a self-evaluation tool. They were more sceptical about the gamification elements, in particular the leader board, and expressed views that competition could have a negative impact on safety and is therefore not desired.</p

D2.1 State of the art on monitoring driver state and task demand

The work presented in this deliverable addresses the monitoring of the task demand and complexity based on contextual factors of the driver environment and the monitoring of the driver (car, truck, bus and train) in real-time; both with the aim of eventually determining whether an individual operates within or without a safe zone. In recent years, considerable research has been conducted in relation to these topics and the development of associated technologies is progressing fast – especially in view of the autonomous driving boom – which has resulted in an enormous variety of approaches, data collection methods, monitoring and warning equipment etc. This variety and ever-changing technology maturity, however, also reflects the circumstance that there is not a standard approach but quite the contrary, a multitude of tools, measurement methods, indicators, algorithms etc. with specific advantages and drawbacks, delivering their full potential in specific contexts. Therefore, capturing the state of the art of monitoring techniques and subsequently assessing the applicability for i-DREAMS’ endeavour is indispensable.</div

D3.2 Toolbox of recommended data collection tools and monitoring methods and a conceptual definition of the Safety Tolerance Zone

The STZ is the core concept of the i-DREAMS project. This report aims to explicitly describe the practical conceptualisation of the STZ to develop the theoretical framework for operational design, presented in Deliverable 3.1, towards a fully functional methodology to be implemented in the forthcoming experimental setups (i.e. in WP4). In order to fulfil this purpose a trilateral correspondence is needed between the list of available technologies, the factors and indicators that need to be monitored (as described in Deliverable 2.1) and the translation of themeasurements into meaningful STZ levels and the triggering of interventions (Deliverable 2.2). As a result, the ultimate outcomes of this deliverable will be the provision of a toolbox, a list of viable options of the most useful data collection and monitoring tools as well as the suggestion of a mathematical framework to realize the STZ in real-world driving situations. With regards to the state-of-the-art measuring tools, several physiological and behavioral indicators, such as distraction/inattention, fatigue, emotions or forward collision warning are proposed for realtime, while performance measurements such as speeding, harsh acceleration, braking or risky hours driving are also mentioned for post-trip processing.Furthermore, as different aspects related to the actual driving context (e.g. driver stress, time schedules, workload, frustration) can explain why drivers deviate from their “normal” way of driving, by accepting higher risks and engaging in increased risky driving behaviors (e.g. speeding, harsh accelerations, dangerous overtaking), the identification and detection of abnormal driving episodes becomes one of the most relevance to STZ estimation. </div