| Summary: |
The project TRAIN aims to address the challenges and risks associated with the introduction of partially-automated truck platooning on public roads. The project involves a multi-stage approach that includes engaging professional truck drivers and representatives of freight companies in focus group interviews and nationwide questionnaires to identify their knowledge, perceptions, opinions, and feelings about autonomous driving and truck platooning.
Driving simulations will be conducted to assess the main risks associated with truck platooning under partial automation, such as the reduction of driving tasks performed by following drivers in a truck platoon and their capabilities to respond to unexpected events. The simulations will also test safe following distances and evaluate the levels of understanding of the system and the levels of situation awareness of truck drivers in platoon.
The results from these activities will be processed using advanced statistical and machine learning techniques to predict the evolution of drivers' alertness levels across time and to derive safe and efficient thresholds for takeover and following distances. The project will deliver essential guidelines and recommendations to the truck platooning industry, operators, and safety authorities, based on robust driver acceptance and behavioral models, which will reflect a comprehensive mapping of training needs and risk factors for a safe and acceptable deployment of truck platooning on public roads. |