The increasing accessibility of mobility datasets has enabled research in green mobility, road safety, vehicular automation, and transportation planning and optimization. Many stakeholders have leveraged vehicular datasets to study conventional driving characteristics and self-driving tasks. Notably, many of these datasets have been made publicly available, fostering collaboration, scientific comparability, and replication. As these datasets encompass several study domains and contain distinctive characteristics, selecting the appropriate dataset to investigate driving aspects might be challenging. To the best of the authors’ knowledge, this is the first paper that performs a systematic review of a substantial number of vehicular datasets covering various automation levels. In total, 103 datasets have been reviewed, 35 of which focused on naturalistic driving, and 68 on self-driving tasks. The paper gives researchers the possibility of analyzing the datasets’ principal characteristics and their study domains. Most naturalistic datasets have been centered on road safety and driver behavior, although transportation planning and eco-driving have also been studied. Furthermore, datasets for autonomous driving have been analyzed according to their target self-driving tasks. A particular focus has been placed on data-driven risk assessment for the vehicular ecosystem. It is observed that there exists a lack of relevant publicly available datasets that challenge the creation of new risk assessment models for semi- and fully automated vehicles. Therefore, this paper conducts a gap analysis to identify possible approaches using existing datasets and, additionally, a set of relevant vehicular data fields that could be incorporated in future data collection campaigns to address the challenge.

A telematics device is a vehicle instrument that comes preinstalled by the vehicle manufacturer or can be added later. The device records information about driving behavior, including speed, acceleration, and turning force. When connected to vehicle computers, the device can also provide additional information regarding the mechanical usage and condition of the vehicle. All of this information can be transmitted to a central database via mobile networks. The information provided has led to new services such as Usage Based Insurance (UBI). A range of consultants, industry commentators and academics have produced an abundance of projections on how telematics information will allow the introduction of services from personalized insurance, bespoke entertainment and advertise and vehicle energy optimization, particularly for Electric Vehicles (EVs). In this paper we examine these potential services against a backdrop of nascent regulatory limitations and against the technical capacity of the devices. Using a case study approach, we examine three applications that can use telematics information. We find that the expectations of service providers will be significantly tempered by regulatory and technical hurdles. In our discussion we detail these limitations and suggest a more realistic rollout of ancillary services.

Telematics devices have transformed driver risk assessment, allowing insurers to tailor premiums based on detailed evaluations of driving habits. However, integrating Advanced Driver Assistance Systems (ADAS) and contextualized geolocation data for predictive improvements remains underexplored due to the recent emergence of these technologies. This article introduces a novel risk assessment methodology that periodically computes weekly insurance premiums by incorporating ADAS risk indicators and contextualized geolocation data. Using a naturalistic dataset from a fleet of 354 commercial drivers over a year, we modeled the relationship between past claims and driving data, and use that to compute weekly premiums that penalize risky driving situations. Risk predictions are modeled through claims frequency using Poisson regression and claims occurrence probability using machine learning models, including XGBoost and TabNet, and interpreted with SHAP. The dataset is divided into weekly profiles containing aggregated driving behavior, ADAS events, and contextual attributes. Results indicate that both modeling approaches show consistent attribute impacts on driver risk. For claims occurrence probability, XGBoost achieved the lowest Log Loss, reducing it from 0.59 to 0.51 with the inclusion of all attributes; for claims frequency, no statistically significant differences were observed when including all attributes. However, adding ADAS and contextual attributes allows for a comprehensive and disaggregated interpretation of the resulting weekly premium. This dynamic pricing can be incorporated into the insurance lifecycle, enabling bespoke risk assessment based on emerging technologies, the driving context, and driver behavior.

Severe convective storms (SCS), including hail, tornadoes, straight-line winds, lightning, and heavy precipitation, represent a significant and evolving source of climate risk. SCS perils pose significant challenges for sectors such as insurance and finance, where accurate risk quantification is essential for underwriting, portfolio management, and resilience planning. Assessing the risk of these perils requires robust frameworks capable of capturing non-linear dynamics, spatial heterogeneity, and compounding effects. However, current modeling approaches often exhibit limited skills when restricted to narrow hazard scopes (e.g., hail-only) or coarse annual scales, limiting their ability to resolve seasonal and intra-seasonal variability. This research introduces a risk assessment framework that leverages deep learning architectures, specifically, a U-Net model augmented with attention mechanisms, to predict the frequency and severity of SCS perils. The model is trained on high-dimensional interpretable meteorological predictors calculated in-house from reanalysis and climate model data, and georeferenced hazard observations from diverse sources. Attention layers within the U-Net architecture enhance feature localization and interpretability, addressing challenges in modeling rare and spatially complex events critical for risk assessment. The framework produces peril-specific daily probabilities and climatological maps, allowing for modeling cross-peril correlation as well as multi-day outbreaks. By integrating physical understanding with data-driven modeling, this approach offers a scalable and interpretable solution for climate risk assessment to support applications such as underwriting, accumulation management, and risk mitigation.

As a leading commercial insurer specializing in property and infrastructure - including the rapidly expanding renewables sector - FM faces growing exposure to the impacts of severe convective storms (SCS). These high-impact weather events, which encompass hail, tornadoes, straight-line winds, lightning, and heavy precipitation, pose significant risks to insured assets. In this presentation, we introduce a suite of in-house predictive models developed to estimate both the frequency and intensity of these SCS perils. Our approach integrates observational data with state-of-the-art deep learning techniques to process and interpret high-dimensional meteorological inputs. The models are trained on a comprehensive set of predictors derived internally from ERA5 reanalysis data, ensuring consistency and scalability across spatial and temporal domains. Deep learning models are particularly well-suited for this task, as they efficiently capture spatial dependencies and patterns within gridded atmospheric fields, enabling robust identification of conditions conducive to severe weather events. These predictive tools are used internally to support underwriting activities, risk pricing, and accumulation management. In addition to peril-specific risk metrics, we introduce cross-peril correlation maps—particularly between hail and straight-line wind—that are instrumental in supporting FM’s growing Renewables business. By combining physical understanding with data-driven modeling, our framework offers a scalable and interpretable solution for SCS risk assessment. The presentation highlights model architecture, training methodology, and selected case studies demonstrating operational relevance and performance.