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Isolated actuated signalized intersection is a pressing challenge for conventional eco-approach methods, due to the ever-changing signal timing strategy. This research proposes an optimal control based eco-approach method tailored to tackle this challenge. The proposed method bears the following features: i) capable of predicting the ever-changing actuated signal timing; ii) with enhanced fuel efficiency via proactively catching a feasible passing time window; iii) with real-time computation efficiency for implementation. Simulation results demonstrate that the proposed method enhances fuel efficiency by 9.1%, reduces stop count by 14.8%, and enhances safety performance by 317.14%, compared to conventional human-driven vehicles. The passing time window predic- tion capability is confirmed with an accuracy of 3.1 s. All the aforementioned benefit is at a cost of a minimal travel time increase of 5.5 s. Moreover, the average computation time of the proposed method is 12 ms, demonstrating its readiness for field implementation. 

Connected and automated vehicles (CAVs) are sup- posed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic envi- ronment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to under- stand HDV behaviors to make safe actions. In this study, we develop a driver digital twin (DDT) for the online prediction of personalized lane-change behavior, allowing CAVs to predict surrounding vehicles’ behaviors with the help of the digital twin technology. DDT is deployed on a vehicle-edge–cloud architec- ture, where the cloud server models the driver behavior for each HDV based on the historical naturalistic driving data, while the edge server processes the real-time data from each driver with his/her digital twin on the cloud to predict the personalized lane- change maneuver. The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles driving along an on/off ramp segment connecting to the edge server and cloud through the 4G/LTE cellular network. The lane-change intention can be recognized in 6 s on average before the vehicle crosses the lane separation line, and the Mean Euclidean Distance between the predicted trajectory and GPS ground truth is 1.03 m within a 4-s prediction window. Compared to the general model, using a personalized model can improve prediction accuracy by 27.8%. The demonstration video of the proposed system can be watched at https://youtu.be/5cbsabgIOdM. 

Recent work has considered personalized route planning based on user profiles, but none of it accounts for human trust. We argue that human trust is an important factor to consider when planning routes for automated vehicles. This article presents a trust-based route-planning approach for automated vehicles. We formalize the human-vehicle interaction as a partially observable Markov decision process (POMDP) and model trust as a partially observable state variable of the POMDP, representing the human’s hidden mental state. We build data-driven models of human trust dynamics and takeover decisions, which are incorporated in the POMDP framework, using data collected from an online user study with 100 participants on the Amazon Mechanical Turk platform. We compute optimal routes for automated vehicles by solving optimal policies in the POMDP planning and evaluate the resulting routes via human subject experiments with 22 participants on a driving simulator. The experimental results show that participants taking the trust-based route generally reported more positive responses in the after-driving survey than those taking the baseline (trust-free) route. In addition, we analyze the trade-offs between multiple planning objectives (e.g., trust, distance, energy consumption) via multi-objective optimization of the POMDP. We also identify a set of open issues and implications for real-world deployment of the proposed approach in automated vehicles. 

An ultraflexible and stretchable field‐effect transistor nanosensor is presented that uses aptamer‐functionalized monolayer graphene as the conducting channel. Specific binding of the aptamer with the target biomarker induces a change in the carrier concentration of the graphene, which is measured to determine the biomarker concentration. Based on a Mylar substrate that is only 2.5‐µm thick, the nanosensor is capable of conforming to underlying surfaces (e.g., those of human tissue or skin) that undergo large bending, twisting, and stretching deformations. In experimental testing, the device is rolled on cylindrical surfaces with radii down to 40 µm, twisted by angles ranging from −180° to 180°, or stretched by extensions up to 125%. With these large deformations applied either cyclically or non‐recurrently, the device is shown to incur no visible mechanical damage, maintain consistent electrical properties, and allow detection of TNF‐α, an inflammatory cytokine biomarker, with consistently high selectivity and low limit of detection (down to 5 × 10⁻¹²m). The nanosensor can thus potentially enable consistent and reliable detection of liquid‐borne biomarkers on human skin or tissue surfaces that undergo large mechanical deformations.

 

Wearable sensors that can conveniently detect cytokine levels in human biofluids are essential for assisting hospitals to maximize the benefits of anti‐inflammatory therapies and avoid cytokine storms. Measurement of cytokine levels in biofluids still remains challenging for existing sensors due to high interference from the background. Here, this challenge is overcome through developing a flexible and regenerative aptameric field‐effect transistor biosensor, consisting of a graphene–Nafion composite film, for detecting cytokine storm biomarkers in undiluted human biofluids. The composite film enables the minimization of nonspecific adsorption and empowers the renewability to the biosensor. With these capabilities, the device is capable of consistently and sensitively monitoring cytokines (e.g., IFN‐γ, an inflammatory and cancer biomarker) in undiluted human sweat with a detection range from 0.015 to 250 nmand limit of detection down to 740 fm. The biosensor is also shown to incur no visible mechanical damage and maintain a consistent sensing response throughout the regenerative (up to 80 cycles) and crumpling (up to 100 cycles) tests. Experimental results demonstrate that the biosensor is expected to offer opportunities for developing wearable biosensing systems for distinguishing acute infectious disease patients and monitoring of patients’ health conditions in daily life.