The introduction of Autonomous Haulage Systems (AHS) is a major paradigm shift in the global transportation and mining industries. In order to understand this shift, two main notions must be defined: AHS and reskilling of a workforce. AHS is a fleet of trucks that do not have drivers inside but use advanced positioning technologies, fleet intelligence, and sensors in order to maneuver through an industrial environment. Workforce reskilling, in this case, means the systematic process of educating traditional truck drivers in order to make them proficient in digital skills and remote supervising.
One of the key challenges of the successful adoption of AHS technology is socioeconomic adaptation of the existing labor force. The incorporation of artificial intelligence in heavy transport raises concerns about autonomy, dignity, and job security of professional drivers. Empirical studies show that these professionals are significantly concerned about the necessity to retake education and demand comprehensive information from their employers about the impact of AHS (Dubljević et al., 2022). Dealing with human factor and overcoming job insecurity is an essential part of the technological transition.
In order to resolve this problem, an appropriate reskilling method must include careful change management in multiple phases. The deployment of commercial autonomous road haulage implies the gradual process of transition from traditional driver positions to remote supervising roles. Industry opinions highlight the necessity of organizational readiness and workforce development in accordance with the future needs before the deployment of autonomous trucks (Sindi & Woodman, 2021). Engaging drivers in the early planning process makes it possible to accurately map the transition from traditional jobs to the future ones, e.g., fleet coordinators or logistics analysts.
The next step should be aimed at the technical upskilling of workers. Traditional driving is based on the mechanical reaction of a driver, whereas the controlling of autonomous vehicles is associated with a greater need for digital skills. Reskilling programs must include education about human-machine interaction, telemetry data analysis, and monitoring of communication between vehicle and infrastructure. Workers need formal training in order to understand how to detect problems, solve them, and understand algorithms, thus transforming traditional manual operators into system supervisors.
Another crucial part of reskilling process is related to the training in terms of cognitive skills. Even though AHS does not require human intervention in the case of routine tasks, these machines frequently face unpredictable situations, bad weather conditions, and complex loading/unloading tasks. In this case, reskilled drivers are needed to perform human-in-the-loop operations, using their skills of making decisions during emergencies. Training programs should focus on such issues as safety procedures, critical thinking, and process optimization.
To summarize, the correct methodology of reskilling a truck-driving workforce prior to AHS deployment is a multi-faceted and human-oriented task. It implies the need to plan transitions carefully, maintain a transparent communication, and invest considerable amount of money into both technical and cognitive education. With the help of the proper reskilling, traditional drivers can be transformed into highly qualified system supervisors.
References
Dubljević, V., Douglas, S., Milojevich, J., Ajmeri, N., Bauer, W. A., List, G., & Singh, M. P. (2022). Moral and social ramifications of autonomous vehicles: a qualitative study of the perceptions of professional drivers. Behaviour & Information Technology, 42, 1271–1278. https://doi.org/10.1080/0144929x.2022.2070078
Sindi, S., & Woodman, R. (2021). Implementing commercial autonomous road haulage in freight operations: An industry perspective. Transportation Research Part A: Policy and Practice, 152, 235–253. https://doi.org/10.1016/j.tra.2021.08.003
