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In the automotive and transportation sectors, technological advancements and innovations aim to reduce fuel consumption and CO2 emissions of vehicles. In vehicles, a significant portion of fuel energy is wasted in heat, vibrations, and frictional losses. The vibration energy from vehicle suspension systems is always wasted in heat and can be utilized for useful purposes. Many researchers have designed various regenerative shock absorbers (RSA) to transform vibration energy into electrical energy that can charge electric vehicles' batteries and power low-wattage devices. The present work focuses on an in-depth summary of rotary, hydraulic, and linear electromagnetic RSA. Also, the applications of regenerated energy and technical challenges are discussed. In RSA, the maximum energy harvesting, and ride comfort of the vehicle cannot be achieved simultaneously. The weight of RSA may increase due to the integration of some additional components compared with conventional shock absorbers. It is necessary to examine the impact of weight on the vehicle's road handling and ride comfort. The hydraulic RSAs have low energy harvesting efficiency, so they are not suitable for lightweight vehicles despite their higher energy harvestability than rotary and linear RSAs. The bibliometric analysis is conducted using the visualization of similarities (VOS) viewer to visualize the contributing authors and countries and specify the research themes. The articles are collected from the Web of Science using keywords related to energy harvesting from 2000 to 2021. Authors from China are more productive than others, with the highest number of publications related to the energy-harvesting from RSAs in 2019. [ABSTRACT FROM AUTHOR]

Blind zone detection of vehicles, as an essential function of Advanced Driver Assistance System, can effectively reduce the occurrence of traffic accidents and has attracted unprecedented attention. This paper develops an active collision avoidance method for right-turning blind zone, based on vulnerable road users (VRUs) detection. The proposed strategy consists of three main steps. First of all, an improved YOLOv4-tiny algorithm based on deep learning, combining two optimization strategies, is proposed to detect VRUs in right-turning blind zone more accurately and robustly. Secondly, a distance measurement method via monocular camera is used for ranging the distance between the host vehicle and the detected VRUs. Finally, a simple and effective vehicle active speed control algorithm is presented, based on distance and vehicle speed information, to provide early warning to the driver. This method was tested in a large driving dataset and in various actual driving situations. Experimental results show that, compared with the lightweight state-of-the-art methods, the improved YOLOv4-tiny has the best detection accuracy for VRUs and can stabilize at a detection speed of 50FPS on 1920*1080 resolution video, and that the measured distance error remains within 4%. A simulation test also proves that the proposed active speed control algorithm can effectively deliver early warning to drivers and avoid traffic accidents. [ABSTRACT FROM AUTHOR]

Additively manufactured nano-MEH systems are widely used to harvest energy from renewable and sustainable energy sources such as wind, ocean, sunlight, raindrops, and ambient vibrations. A comprehensive study focusing on in-depth technology evolution, applications, problems, and future trends of specifically 3D printed nano-MEH systems with an energy point of view is rarely conducted. Therefore, this paper looks into the state-of-the-art technologies, energy harvesting sources/methods, performance, implementations, emerging applications, potential challenges, and future perspectives of additively manufactured nano-mechanical energy harvesting (3DP-NMEH) systems. The prevailing challenges concerning renewable energy harvesting capacities, optimal energy scavenging, power management, material functionalization, sustainable prototyping strategies, new materials, commercialization, and hybridization are discussed. A novel solution is proposed for renewable energy generation and medicinal purposes based on the sustainable utilization of recyclable municipal and medical waste generated during the COVID-19 pandemic. Finally, recommendations for future research are presented concerning the cutting-edge issues hurdling the optimal exploitation of renewable energy resources through NMEHs. China and the USA are the most significant leading forces in enhancing 3DP-NMEH technology, with more than 75% contributions collectively. The reported output energy capacities of additively manufactured nano-MEH systems were 0.5–32 mW, 0.0002–45.6 mW, and 0.3–4.67 mW for electromagnetic, piezoelectric, and triboelectric nanogenerators, respectively. The optimal strategies and techniques to enhance these energy capacities are compiled in this paper. Highlights: The current technology evolution and advancements in 3DP-NMEHs were evaluated. Categorization and comparison of 3DP-NMEHs were performed in terms of inputs and energy conversion. The emerging applications of 3DP-NMEHs were discussed briefly. The potential challenges confronted by 3DP-NMEHs and respective recommendations were summarized. Sustainable use of recyclable waste generated during the COVID-19 pandemic was proposed. [ABSTRACT FROM AUTHOR]

Vehicle drivers driving cars under the situation of drowsiness can cause serious traffic accidents. In this paper, a vehicle driver drowsiness detection method using wearable electroencephalographic (EEG) based on convolution neural network (CNN) is proposed. The presented method consists of three parts: data collection using wearable EEG, vehicle driver drowsiness detection and the early warning strategy. Firstly, a wearable brain computer interface (BCI) is used to monitor and collect the EEG signals in the simulation environment of drowsy driving and awake driving. Secondly, the neural networks with Inception module and modified AlexNet module are trained to classify the EEG signals. Finally, the early warning strategy module will function and it will sound an alarm if the vehicle driver is judged as drowsy. The method was tested on driving EEG data from simulated drowsy driving. The results show that using neural network with Inception module reached 95.59% classification accuracy based on one second time window samples and using modified AlexNet module reached 94.68%. The simulation and test results demonstrate the feasibility of the proposed drowsiness detection method for vehicle driving safety. [ABSTRACT FROM AUTHOR]

The conventional elevator arrives at the floor in response to the user request and where is to dispatch an elevator car. There is one big problem that when system dispatches the elevator to the button responding floor, but corridor passenger is no there. In this paper, we present a novel elevator group control algorithm based on binocular-cameras corridor passenger detection and tracking, aiming to overcome the difficulty in the conventional elevator group control system. In the above proposed system, the corridor passengers are detected using Haar-like features based on binocular-cameras, and Unscented Kalman Filter (UKF) is introduced to improve robustness and accuracy of corridor passenger motion tracking. A new elevator group control strategy based on corridor passenger detection and tracking is proposed to improve the performance and transport efficiency of the elevator service. Compared with the traditional elevator group control system, the proposed system has potential advantages in minimizing passengers' waiting time and saving electronic energy. The final experimental results show the validity of our method under simulation and realistic condition. [ABSTRACT FROM AUTHOR]

Non-intrusive methods for eye tracking are important for many applications of vision-based human computer interaction. However, due to the high nonlinearity of eye motion, how to ensure the robustness of external interference and accuracy of eye tracking pose the primary obstacle to the integration of eye movements into today's interfaces. In this paper, we present a strong tracking unscented Kalman filter (ST-UKF) algorithm, aiming to overcome the difficulty in nonlinear eye tracking. In the proposed ST-UKF, the Suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking. Compared with the related Kalman filter for eye tracking, the proposed ST-UKF has potential advantages in robustness and tracking accuracy. The last experimental results show the validity of our method for eye tracking under realistic conditions. [ABSTRACT FROM AUTHOR]

A new scheme for driver fatigue detection is presented, which is based on the nonlinear unscented Kalman filter and eye tracking. Assuming a probability distribution than to approximate an arbitrary nonlinear function or transformation, eye nonlinear tracking can be achieved using an unscented transformation (UT), which adopts a set of deterministic sigma points to match the posterior probability density function of the eye movement. Driver fatigue can be detected using the percentage of eye closure (PERCLOS) framework in a realistic driving condition after the eye nonlinear tracking. This system was tested adequately in realistic driving environments with subjects of different genders, with/without glasses, in day/night driving, being commercial/noncommercial drivers, in continuous driving time, and under different road conditions. The last experimental results show that the proposed method not only improves the robustness for nonlinear eye tracking, but also can provide more accurate estimation than the traditional Kalman filter. [ABSTRACT FROM AUTHOR]

Non-intrusive methods for eye tracking are important for many applications of vision-based human computer interaction. However, due to the high nonlinearity of eye motion, how to ensure the robustness of external interference and accuracy of eye tracking poses the primary obstacle to the integration of eye movements into todays’s interfaces. In this paper, we present a strong tracking finite-difference extended Kalman filter algorithm, aiming to overcome the difficulty in modeling nonlinear eye tracking. In filtering calculation, strong tracking factor is introduced to modify a priori covariance matrix and improve the accuracy of the filter. The filter uses finite-difference method to calculate partial derivatives of nonlinear functions for eye tracking. The latest experimental results show the validity of our method for eye tracking under realistic conditions. [ABSTRACT FROM AUTHOR]