Acceptance and Usability of a Soft Robotic, Haptic Feedback Seat for Autonomy Level Transitions in Highly Automated Vehicles.

Fully autonomous vehicles, capable of completing entire end-to-end journeys without the interference of a human driver, will be one of the biggest transforming technologies of the next decades. As the journey towards fully autonomous vehicles progresses, there will be an increase in the number of highly automated vehicles on the roads, requiring the human driver to take back control in situations, which cannot be handled by the vehicle autonomously. These human-robot take-over requests can lead to safety risks, in particular in scenarios when the driver fails to understand the take-over request and, hence, lacks situational awareness. This paper presents the acceptance and usability assessment of a haptic feedback driver seat capable of informing the driver of a take-over request through static mechano-tactile haptic feedback. The seat is equipped with an embedded array of soft pneumatic actuators, that have been fully modelled and characterised. The evaluation process of the haptic feedback seat engaged 21 participants who experienced both auditory and haptic feedback from the seat in a number of simulation experiments within a driving simulator. The vehicular technology was assessed through well-established methods to understand the acceptance (usefulness and satisfaction) and usability of the haptic feedback driver seat.

Blind classification of e-scooter trips according to their relationship with public transport.

E-scooter services have multiplied worldwide as a form of urban transport. Their use has grown so quickly that policymakers and researchers still need to understand their interrelation with other transport modes. At present, e-scooter services are primarily seen as a first-and-last-mile solution for public transport. However, we demonstrate that 50% of e-scooter trips are either substituting it or covering areas with little public transportation infrastructure. To this end, we have developed a novel data-driven methodology that autonomously classifies e-scooter trips according to their relation to public transit. Instead of predefined design criteria, the blind nature of our approach extracts the city's intrinsic parameters from real data. We applied this methodology to Rome (Italy), and our findings reveal that e-scooters provide specific mobility solutions in areas with particular needs. Thus, we believe that the proposed methodology will contribute to the understanding of e-scooter services as part of shared urban mobility.