The collection of clean power from ambient vibrations is considered a promising method for energy harvesting. For the case of wheel rotation, the present study investigates the effectiveness of a piezoelectric energy harvester, with the application of stochastic resonance to optimize the efficiency of energy harvesting. It is hypothesized that when the wheel rotates at variable speeds, the energy harvester is subjected to on-road noise as ambient excitations and a tangentially acting gravity force as a periodic modulation force, which can stimulate stochastic resonance. The energy harvester was miniaturized with a bistable cantilever structure, and the on-road noise was measured for the implementation of a vibrator in an experimental setting. A validation experiment revealed that the harvesting system was optimized to capture power that was approximately 12 times that captured under only on-road noise excitation and 50 times that captured under only the periodic gravity force. Moreover, the investigation of up-sweep excitations with increasing rotational frequency confirmed that stochastic resonance is effective in optimizing the performance of the energy harvester, with a certain bandwidth of vehicle speeds. An actual-vehicle experiment validates that the prototype harvester using stochastic resonance is capable of improving power generation performance for practical tire application.

Nonlinear energy harvesters are frequently considered in preference to linear devices because they can potentially overcome the narrow frequency bandwidth limitations inherent to linear variants; however, the possibility of variable harvesting efficiency is raised for the nonlinear case. This paper proposes a rotational energy harvester which may be fitted into an automobile tyre, with the advantage that it may broaden the rotating frequency bandwidth and simultaneously stabilise high-energy orbit oscillations. By consideration of the centrifugal effects due to rotation, the overall restoring force will potentially be increased for a cantilever implemented within the harvester, and this manifests as an increase in its equivalent elastic stiffness. In addition, this study reveals that the initial potential well barriers become as shallow as those for a bistable system. When the rotational frequency increases beyond an identifiable boundary frequency, the system transforms into one with a potential barrier of a typical monostable system. On this basis, the inter-well motion of the bistable system can provide sufficient kinetic energy so that the cantilever maintains its high-energy orbit oscillation for monostable hardening behaviour. Furthermore, in a vehicle drive experiment, it has been shown that the effective rotating frequency bandwidth can be widened from 15 km/h–25 km/h to 10 km/h–40 km/h. In addition, it is confirmed that the centrifugal effects can improve the harvester performance, producing a mean power of 61 μW at a driving speed of 40 km/h, and this is achieved by stabilising the high-energy orbit oscillations of the rotational harvester.

Nowadays insight into human-machine interaction is a critical topic with the large-scale development of intelligent vehicles. Biosignal analysis can provide a deeper understanding of driver behaviors that may indicate rationally practical use of the automatic technology. Therefore, this study concentrates on biosignal analysis to quantitatively evaluate mental stress of drivers during automatic driving of trucks, with vehicles set at a closed gap distance apart to reduce air resistance to save energy consumption. By application of two wearable sensor systems, a continuous measurement was realized for palmar perspiration and masseter electromyography, and a biosignal processing method was proposed to assess mental stress levels. In a driving simulator experiment, ten participants completed automatic driving with 4, 8, and 12 m gap distances from the preceding vehicle, and manual driving with about 25 m gap distance as a reference. It was found that mental stress significantly increased when the gap distances decreased, and an abrupt increase in mental stress of drivers was also observed accompanying a sudden change of the gap distance during automatic driving, which corresponded to significantly higher ride discomfort according to subjective reports.

Vibration-based energy harvesters brought the idea of self-powered sensors to reality in the past few years. Many strategies to improve the performance of linear vibration energy harvesters that collect energy over a limited bandwidth have been proposed. In this paper, a bi-stable two degrees of freedom (2-DOF) cut-out vibration energy harvester employing a pair of permanent magnets is designed through a proposed design methodology. Based on this methodology, the nonlinear harvesters can be optimally designed such that the bandwidth can be widened for a targeted output voltage. The proper selection of the harvester parameters as well as the gap distances between the tip and the fixed magnets are the bases of this methodology. The mathematical modeling of the proposed harvester and the formula for the potential energy between the tip and the fixed magnets are presented. Additionally, to enhance the performance of the bi-stable energy harvester (BEH), a quad-stable energy harvester (QEH) was configured by adding more fixed magnets. Experiments were performed to validate the numerical simulations and the results showed that, the simulation and experimental results are consistent. The results indicate that, the QEH covers a wider bandwidth than the BEH and based on a figure of merit the QEH shows the best performance among many harvesters presented in the literature.

Fast and accurate long-term trajectory prediction of surrounding vehicles (SVs) is critical to autonomous driving systems. In high-density traffic flows, strongly correlated vehicle behaviors require considering the interactions among multiple SVs when predicting their future trajectories. However, existing interactive prediction methods, most based on Long Short-Term Memory (LSTM), are suffering from slow prediction because they analyze SVs one by one and analyze trajectory sequence node by node. This paper presents a fast interactive trajectory prediction method called Structural Transformer which learns both spatial and temporal dependencies among multiple SVs in parallel. Specifically, our model first removes the internal states and loops of LSTM and replaces with a weighted self-reference mapping to realize parallel computation. Then, it embeds the relative spatial information of multiple SVs into trajectory states and reorganizes the self-reference mapping with neighbor-only interaction masks to achieve interactive prediction. Results on the NGSIM dataset show satisfyingly speed and accuracy performance on long-term trajectory prediction of multiple SVs. The longitudinal and lateral errors are reduced to 2.67m and 0.25m over 5s time horizon. The computational time of each step is only 12ms on a 2080ti GPU, which is over 4 times faster than the Structural LSTM.