Driver distraction is one of the main causes of traffic accidents. While there are different types of distraction (manual, visual, cognitive), cognitive distraction is particularly challenging, being only partially related to visual features detectable through cameras or an eye tracker system. Moreover, since cognitive distraction is not a point in time phenomenon, spotting this kind of distraction requires the processing of a certain time interval, which poses a further challenge for real-time performance. After a data collection campaign with N=42 subjects undertaking a twenty-question task (TQT) in a driving simulator, we developed a driver cognitive distraction detection system, addressing some key gaps we identified in literature. First, we assessed the effectiveness of state-of-the-art time-series-oriented deep learning models in learning features from 60 Hz raw input signals, thus implementing an end-to-end machine learning approach, without manual feature engineering. We demonstrated that such models are able to classify time-windows as small as 0.5 seconds, and are also more robust to sensor failures. Then, also with the support of AI explainability, we showed that processing vehicular data is fundamental to ensure performance, while physiological signals provide a less important, but still useful, contribution. Moreover, through a between- and within-subject design comparison, we showed that eye-tracker and, particularly, physiological signals are much more prone to inter-individual variability, thus overfitting. This is fundamental to consider for commercial deployment, as it would require fine-tuning the system with data from the actual end-user. Our analysis has quantitatively measured the effect of such variability for all types of signals, demonstrating its huge relevance, and shown that deep learning models dedicated to timeseries processing are better able to generalize across users than the more commonly employed shallow machine learning models. With a focus on in-vehicle deployability, which is of significant industrial interest, we measured also such metrics as model size, inference time, and energy consumption, showing feasibility on two embedded platforms, which is a key advancement towards on-board deployment of robust, real-time cognitive distraction detection systems.

The “classical” SAE LoA for automated driving can present several drawbacks, and the SAE-L2 and SAE-L3, in particular, can lead to the so-called “irony of automation”, where the driver is substituted by the artificial system, but is still regarded as a “supervisor” or as a “fallback mechanism”. To overcome this problem, while taking advantage of the latest technology, we regard both human and machine as members of a unique team that share the driving task. Depending on the available resources (in terms of driver’s status, system state, and environment conditions) and considering that they are very dynamic, an adaptive assignment of authority for each member of the team is needed. This is achieved by designing a technology enabler, constituted by the intelligent and adaptive co-pilot. It comprises (1) a lateral shared controller based on NMPC, which applies the authority, (2) an arbitration module based on FIS, which calculates the authority, and (3) a visual HMI, as an enabler of trust in automation decisions and actions. The benefits of such a system are shown in this paper through a comparison of the shared control driving mode, with manual driving (as a baseline) and lane-keeping and lane-centering (as two commercial ADAS). Tests are performed in a use case where support for a distracted driver is given. Quantitative and qualitative results confirm the hypothesis that shared control offers the best balance between performance, safety, and comfort during the driving task.

This work describes an approach to develop a model of driver's distraction induced by an on-board information system basing on vehicle data. Machine learning techniques have been adopted to find the model able to better predict distraction, given a target value. Results pointed in favor of a model obtained with the ANFIS (adaptive neuro fuzzy inference system) technique. Further investigations will be carried out by porting the model on real car.