Panacea project partner:  Datik

Q1. Briefly describe the use case in one or two sentences (site, vehicles, technologies tested, etc):

The truck and coach case study in Spain takes a holistic approach to fatigue and cognitive load management. It takes into account events before, during, and after the working shift.

Both will likely be carried out during the night shift. The technologies to be implemented and tested are as follows:

• Saliva sensor from Leitat to detect substance (illicit and licit drugs) prior to drive
• App Pre-Questionnaire application from Datik to detect risk sensitivity prior to drive
• Alcohol sensor from SENSEAIR to detect BrAC (Breath Alcohol Concentration) before and during driving
• Smart PWA system from AIT to detect stress parameters prior to and during driving
• ViF steering algorithm to detect swerve and distraction situations during driving
• Datik system to detect fatigue events and risk levels during driving

Depending on the features, all these technologies will be integrated in different ways (via the cloud, hardware, ethernet, etc.)

Q2. What is the main objective of the use case?

The main objective of this use case is to achieve a CHT (Commercial Health Toolkit) capable of evaluating the aptitude for work of long-distance drivers and dustcart collectors, as well as submitting short-, medium-, and long-term proposals that optimise the performance of their activity. To achieve this it is necessary to:

• Create Early Intervention Events (visual/acoustic alerts) aimed at long haul transport and refuse vehicle drivers as an important tool in bringing their awareness to any potential sign of fatigue and/or impairment because of a combination of reasons.
• Complete a Remote Ability Assessment in app format for easy use. This will suppose undertaking an assessment of an individual who is either suspected, observed, or reported to be impaired, or through monitoring has recorded a high-risk alert. In addition, the systems will record the reason for the assessment, possible contributing factors and allow supervisors to make an informed, analysis, and diagnostic assessment of the risk and the required actions and appropriate control measures to implement and follow.

Q3. What are the difficulties encountered in developing the use case?

We would like to clarify that, at this stage of the project, we are making certain assumptions about these difficulties considering the information that has been shared during meetings and focus group meetings.  At the time of writing, the main challenges are seen as follows:

• Complete integration of all systems involved.
• Holistic assessment, this means prioritisation of systems input to make an appropriate countermeasure decision.
• Off-duty input integration for correct diagnosis and thus appropriate countermeasures.
• Driver training to understand what we are monitoring and collecting and about the countermeasure expected to be used to ensure safety.

Q4. How will the collection of data/study be carried out?

Data will be collected using:

• CAN Communication protocol for vehicle ECU
• Digital Inputs for SenseAir integration
• UDP or TCP/IP for reading VIF information
• Inputs for AIT smart PWA integration
• Cameras stream data
• Internal information from the ECU such as timestamp, driverID, …

On the one hand, after reading all the sensor data at the embedded level on the vehicle, the processed information will be sent to the iPanel* database, where it can be analysed by the researchers.

On the other hand, sensors which are not embedded, but collect driver information to be considered by the PANACEA solution, send the information to the PANACEA cloud.

iPanel, via APIs, shares the information with PANACEA cloud.

The information displayed through the PANACEA cloud can be checked and monitored by operators.

From the cloud, the countermeasures are released, and the type of countermeasure depends on many different parameters such as data treated, detection time, and so on (these rules are still undeveloped, at the time of writing).

*iPanel is the platform which currently displays information coming from the Datik system

Q5. How does the use-case contribute to the PANACEA project and to improved road safety more broadly?

Use case C is especially focused on long-distance and dustcart drivers, this approach implies addressing certain circumstances of professional drivers.

On one hand, due to the idiosyncrasy of night shift drivers (cognitive load and fatigue due to night shift conditions), and on the other hand, due to coach drivers who work an uninterrupted shift (8 hours) performing the same service repeatedly.

Considerations about both professional situations are essential to further improve road safety in general.

In the same vein, taking into account the organisational aspects, and extending both participation and responsibility to all the actors involved in road safety (operators, enforcement, etc.) implies a significant improvement in road safety.

The proposal of different countermeasures for professional drivers and the subsequent analysis of their impact will also mean a significant improvement in road safety.

The integration of different sensors (onboard or not) both at the hardware level and at the cloud level is a great advantage in terms of safety, thus expanding the scope of the PANACEA solution.