Hands on the wheel, eyes on the road" is the central guideline of safe vehicle driving practices. Many advanced driver assistance systems can effectively detect abnormal vehicle motions. However, these systems often leave insufficient time for drivers to respond to complex road situations, especially when the drivers are distracted. To reduce accidents, it is essential to detect whether a driver complies with safe driving guidelines in real time and provide warnings early before any dangerous maneuvers occur. There are vision-based driver distraction monitoring systems which rely on cameras in high-end vehicles, but their performances are heavily constrained by visibility requirements. In this paper, we present MagTrack, a driver monitoring system that is based on tracking magnetic tags worn by the user. With a single smartwatch and two low-cost magnetic accessories: a hand magnetic ring and a head magnetic eyeglasses clip, our system tracks and classifies a driver's bimanual and head movements simultaneously using both analytical and approximation sensing models. Our approach is robust to driver's postures, vehicles, and environmental changes. We demonstrate that a wide range of activities can be detected by our system, including bimanual steering, visual and manual distractions, and lane changes and turns. In extensive road tests with 500+ instances of driving activities and 500+ minutes of road driving with 10 subjects, MagTrack achieves 87% of precision and 90% of recall rate on the detection of unsafe driving activities.
CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing.
Resiliency is the ability to quickly recover from a violation and avoid future violations for as long as possible. Such a property is of fundamental importance for Cyber-Physical Systems (CPS), and yet, to date, there is no widely agreed-upon formal treatment of CPS resiliency. We present an STL-based framework for reasoning about resiliency in CPS in which resiliency has a syntactic characterization in the form of an STL-based Resiliency Specification (SRS). Given an arbitrary STL formula ϕ, time bounds α and β, the SRS of ϕ, R α,β (ϕ), is the STL formula ¬ϕU [0,α] G [0,β) ϕ, specifying that recovery from a violation of ϕ occur within time α (recoverability), and subsequently that ϕ be maintained for duration β (durability). These R-expressions, which are atoms in our SRS logic, can be combined using STL operators, allowing one to express composite resiliency specifications, e.g., multiple SRSs must hold simultaneously, or the system must eventually be resilient. We define a quantitative semantics for SRSs in the form of a Resilience Satisfaction Value (ReSV) function r and prove its soundness and completeness w.r.t. STL's Boolean semantics. The r-value for R α,β (ϕ) atoms is a singleton set containing a pair quantifying recoverability and durability. The r-value for a composite SRS formula results in a set of non-dominated recoverabilitydurability pairs, given that the ReSVs of subformulas might not be directly comparable (e.g., one subformula has superior durability but worse recoverability than another). To the best of our knowledge, this is the first multi-dimensional quantitative semantics for an STL-based logic. Two case studies demonstrate the practical utility of our approach.
Even though model predictive control (MPC) is currently the main algorithm for insulin control in the artificial pancreas (AP), it usually requires complex online optimizations, which are infeasible for resource-constrained medical devices. MPC also typically relies on state estimation, an error-prone process. In this paper, we introduce a novel approach to AP control that uses Imitation Learning to synthesize neural-network insulin policies from MPC-computed demonstrations. Such policies are computationally efficient and, by instrumenting MPC at training time with full state information, they can directly map measurements into optimal therapy decisions, thus bypassing state estimation. We apply Bayesian inference via Monte Carlo Dropout to learn policies, which allows us to quantify prediction uncertainty and thereby derive safer therapy decisions. We show that our control policies trained under a specific patient model readily generalize (in terms of model parameters and disturbance distributions) to patient cohorts, consistently outperforming traditional MPC with state estimation.
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