Intelligent Transportation Systems: Avoiding Socio-Technical Pitfalls

Motor vehicle crashes account for almost 1.3 million deaths globally every year, 90 per cent of which are attributable to human factors [1]. Automated vehicles could eliminate these deadly errors through smarter technology and sensors; by 2040-41, automated vehicles are expected to form over 40 per cent of the overall vehicle fleet [2], helping to reduce road fatalities. These automated systems could save countless lives worldwide, if — and this is a big if — they are adopted quickly and used effectively.

To maximize the value from new technology, it must be incorporated and accepted by society – a marriage of the social and technical, to form a socio-technical system. As in any real marriage, it can only deliver its promised benefits if there is understanding, communication and acceptance from both sides.

Human factor researchers’ goal for socio-technical systems is that they integrate humans and technology so well that they “address a wide range of problems that are too complex to be addressed by individuals or machines working alone.” [3]

Too often, the benefits of our engineering solutions come up short because it is almost impossible to foresee how people will interact with the technology, and especially where discrepancies will ocurr between intended and adopted use. The very people who should benefit avoid due to perceived risks, uncertainty about the benefits, or lack of awareness of the technology’s availability or use. Plus, if they saw, heard or experienced something negative related to the technology in the past, their views will be even harder to change – a phenomenon known as sticky information.

To increase the likelihood of technology uptake and success, opinion leaders like futurists, technology developers, and city and national officials who see the wider benefits of the new technologies, must work together to create trust in new technology, by building in awareness, experience, ease of understanding, observability and credibility across a wide variety of potential stakeholders, which include both users and non-users of the technology. The first step in building trust is to familiarize people with the new technology – make them aware of it and provide training so they can understand how it might improve their everyday life, and how to use it effectively.

1. Involve credible opinion leaders in the planning and development phases

For greater familiarity and greater optimization of its benefits, people should be aware of the technology well before it’s developed. Involving credible opinion leaders in the planning and development phases means that they’ll be sharing information about possible uses, benefits and pitfalls as the technology develops – bringing the wider community of early adopters along on the journey. If opinion leaders and early adopters see and share the benefits of the technology, and want to use it, they will convince later adopters to use it too. Moreover, integrating relevant stakeholder feedback in the product development process can enhance the applicability of a technology and mitigate certain upfront risks of new technology deployment.

2. Share information about uptake in the community

Word of mouth, spread by early adopters and opinion leaders, is a way of spreading knowledge and decoding the technical aspects of new technology into familiar and understandable ideas. The more people learn about early adopters and opinion leaders using the technology, the more comfortable they’ll be to try it themselves. The use of word of mouth and other communication tools like marketing ads is a strong indication that the technology is starting to transition into being mainstream.

3. Visualization through sociotechnical models

Parallel to information sharing, decision-makers must understand how the technology will be integrated into existing systems and the effects it will have on existing systems. Will it disrupt them or will it make it easier for them to achieve their aims? Will they have to build new infrastructure to accommodate the new technology? Providing models that show how the technology would fit into the flow of the existing system is critical. Using an agent-based model is one way of identifying potentially unexpected behaviours and outcomes arising from introducing new technology.

4. Virtual reality

Virtual Reality (VR) has proven hugely valuable to ensure people use new technology correctly and are aware of the wider benefits. In the design of hospitals, for example, staff and patients have been able to provide feedback that results in better user experience and more productive health care. The use of VR in the design of automated vehicles and related transportation systems could help us understand the user experience, for a much smoother initial rollout. Further, as we transition to fully automated vehicles, there will be instances in which drivers are still able, or expected to, react. In test scenarios, drivers often fail to react because they trust the system to take full control. Studies show that drivers who had VR training before driving an automated vehicle were more likely to understand and react to potential risks [4]. Including VR headsets in driving schools would ease the automated vehicle transition. VR can also be used to create a deeper understanding of how automated vehicles operate. In VR, the driver would be able to see a real-time visual representation of how the car processes sensor data such as road signs, traffic lights and other vehicles. This training would lead to a greater level of acceptance and trust. The use of VR will be akin to flight simulators that pilots use for certification.

5. Augmented reality

As automated vehicle technology advances, other fields of technology could be embedded to improve the functionality and reduce risks. Augmented reality can provide real-time information to the driver using Level 2 or 3 automated vehicles. (footnote 1) Augmented reality would work in conjunction with the car’s sensors to provide real-time feedback to the driver regarding hazards – allowing the driver to take appropriate actions.

Considering infrastructure and transportation systems as integrated, coordinated socio-technical systems is crucial when developing new technology, and is an essential aspect of intelligent infrastructure. New technologies are only as effective as adoption allows; people must be prepared to use these technologies if they are to fulfill their purpose and improve our lives.

• Opinion leaders must co-ordinate efforts to understand the full effects that the new technology will have on users and the current infrastructure.
• Active communication is required with the wider community to educate them so they can develop their own informed opinion.
• To help understand the system-wide impact, modelling techniques that incorporate user behaviours are strongly recommended.
• Virtual reality is recommended for training potential users and further informing users and early adopters.
• Augmented reality can be used as a tool to help people use new technology more correctly.

1. Level 2: The automated system takes full control of the vehicle (accelerating, braking, and steering). However, the driver must be prepared to intervene immediately at any time if the automated system fails to respond properly. Level 3: The driver can safely turn their attention away from the driving tasks, as the vehicle will handle situations that call for an immediate response, like emergency braking. The driver must still be prepared to intervene within some limited time, specified by the manufacturer, when called upon by the vehicle to do so.

1. Sam, D., Velanganni, C. and Evangelin, T.E. (2016). A vehicle control system using a time synchronized Hybrid VANET to reduce road accidents caused by human error. DR.M.G.R Educational and Research Institute, Chemai, India
    
2. Conclusion based on research performed for CAV Readiness Plan for the GTHA and Kitchener/Waterloo Corridor project in Ontario, Canada from the following references:
• Bansal, P. (2017). Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies. The University of Texas at Austin
• Litman, T. (2018). Autonomous Vehicle Implementation Predictions. Victoria Transport Policy Institute
• McKinsey&Company. (2016) Automotive revolution – perspective towards 2030
• Milkais, D., van Arem., B., van Wee, B. (2017). Policy and society related implications of automated driving: A review of literature and directions for future research. Journal of Intelligent Transportation Systems
• Ticoll, D. (2015). Driving Changes: Automated Vehicles in Toronto. University of Toronto
• Woudsma, C., Braun, L. (2017). Tomorrow has arrived: Cities and Autonomous Vehicles. Pragma Council
   
3. Gorman J. C., Cooke N. J., Salas E. (2010). Preface to the special issue on collaboration, coordination, and adaptation in complex socio-technical settings. Human Factors, 52, 143–146
    
4. Sportillo, D., Paljic, A., Ojeda, L. (2018). Get ready for automated vehicles using Virtual Reality. Accident Analysis & Prevention: Vol. 118, pp. 102-113


5. Bush, S., Henning, T., Ingham, J., & Raith, A. (2014). Agent-Based modelling, a quiet revolution in asset management. In IPWEA Conference, Auckland, Nueva Zelandia.