Prediction and optimization techniques for performance enhancement of vehicular ad-hoc networks

Imperial Users onl

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (&gt 92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human&ndash machine interaction in a car and especially for driver state monitoring in the field of automated driving. Document type: Articl

Link classification and residual time estimation through adaptive modeling for VANETs

Abstract — Vehicular Ad hoc Networks design has drawn a lot of research attention. High node mobility, an inherent characteristic of VANETs, results in short lived links and rapid topology changes; this makes traditional ad-hoc protocols inefficient for vehicle to vehicle communication. In this paper we propose a cross-layer approach, where physical layer information can be used by upper layers to monitor the well-being of the various links used, and to estimate their residual time. The algorithm proposed comprises of forming a time series based on physical layer measurements. Utilizing adaptive non-linear parameter estimation methods, this time series can be used to estimate the current state of the link and its residual time. The environment in which VANETs usually operate together with the effect of relative node movement introduce considerable noise. For this reason, a data-driven signal processing technique, Empirical Mode Decomposition is used for denoising. The proposed algorithms are tested against real data and simulations

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving

Link residual-time estimation for VANET cross-layer design

Abstract – Traditional network design may underestimate the dependencies between different layers of the protocol stack and fail to exploit the direct coupling of physical-layer operations to the network topology. In the case of a highly dynamic network, as observed in vehicular communications, the network architecture needs to be able to optimally adapt to the changes brought on by nodes ’ mobility. In this paper we propose a crosslayer approach, where the received power metric, logged at the physical layer, can be used to produce estimates of the links’ residual lifetime. Such information is crucial for the decision processes of higher layers, such as hand-off, scheduling and routing. The method comprises of utilizing a time series based on physical-layer measurements to estimate the current state of the link and the remaining time during which the link can be used for efficient communication. Shadowing, small scale fading and limited opportunities to sample the channel make the problem challenging. The proposed algorithms are tested against simulations which depict the mobile wireless channel realistically