Low-speed automated driving (LSAD) shuttles operate in complex urban environments where abrupt braking can affect safety, service quality, and operational interpretability. This study proposes a telemetry-based workflow for the detection and characterization of hard braking (HB) events in autonomous shuttle operations. The workflow includes preprocessing of autonomous in-service telemetry data, deterministic HB detection under irregular sampling, evidence-based attribution using diagnostic and obstacle-related signals, and driving-context characterization through K-means clustering, applied to a 62-day dataset from an autonomous on-demand shuttle operating on a fixed 2.8 km urban loop in Turin. After preprocessing, 71% of the 16,670,518 observations are retained. The analysis identified 734 HB events, of which 89% are linked to specific contextual conditions, highlighting environmental and operational influences on safety-critical situations. Driving-context analysis relies on 316,280 observations collected at 1 Hz and yields a nine-cluster solution. When projected back onto the route through waypoint-level modal regimes, HB events are found to be over-represented in high-speed segments. These results show that HB events can be interpreted not only as a threshold exceedance, but as an operational indicator linked to route-level driving regimes. The proposed framework supports data-driven safety assessment and operational decision-making in autonomous shuttle systems by researchers and practitioners.
Grano et al. (Sat,) studied this question.