The KDamper is a novel passive vibration isolation and damping concept, based essentially on the optimal combination of appropriate stiffness elements, which include a negative stiffness element. The KDamper concept does not require any reduction in the overall structural stiffness, thus overcoming the corresponding inherent disadvantage of the “Quazi Zero Stiffness” (QZS) isolators, which require a drastic reduction of the structure load bearing capacity. Compared to the traditional Tuned Mass damper (TMD), the KDamper can achieve better isolation characteristics, without the need of additional heavy masses, as in the case of the T Tuned Mass damper. Contrary to the TMD and its variants, the KDamper substitutes the necessary high inertial forces of the added mass by the stiffness force of the negative stiffness element. Among others, this can provide comparative advantages in the very low frequency range. The paper proceeds to a systematic analytical approach for the optimal design and selection of the parameters of the KDamper, following exactly the classical approach used for the design of the Tuned Mass damper. It is thus theoretically proven that the KDamper can inherently offer far better isolation and damping properties than the Tuned Mass damper. Moreover, since the isolation and damping properties of the KDamper essentially result from the stiffness elements of the system, further technological advantages can emerge, in terms of weight, complexity and reliability. A simple vertical vibration isolation example is provided, implemented by a set of optimally combined conventional linear springs. The system is designed so that the system presents an adequate static load bearing capacity, whereas the Transfer Function of the system is below unity in the entire frequency range. Further insight is provided to the physical behavior of the system, indicating a proper phase difference between the positive and the negative stiffness elastic forces. This fact ensures that an adequate level of elastic forces exists throughout the entire frequency range, able to counteract the inertial and the external excitation forces, whereas the damping forces and the inertia forces of the additional mass remain minimal in the entire frequency range, including the natural frequencies. It should be mentioned that the approach presented does not simply refer to discrete vibration absorption device, but it consists a general vibration absorption concept, applicable also for the design of advanced materials or complex structures. Such a concept thus presents the potential for numerous implementations in a large variety of technological applications, whereas further potential may emerge in a multi-physics environment.

In this paper, a review on road friction virtual sensing approaches is provided. In particular, this work attempts to address whether the road grip potential can be estimated accurately under regular driving conditions in which the vehicle responses remain within low longitudinal and lateral excitation levels. This review covers in detail the most relevant effect-based estimation methods; these are methods in which the road friction characteristics are inferred from the tyre responses: tyre slip, tyre vibration, and tyre noise. Slip-based approaches (longitudinal dynamics, lateral dynamics, and tyre self-alignment moment) are covered in the first part of the review, while low frequency and high frequency vibration-based works are presented in the following sections. Finally, a brief summary containing the main advantages and drawbacks derived from each estimation method and the future envisaged research lines are presented in the last sections of the paper.

As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN) , Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated.