Autonomous Vehicles (AVs) have shown indelible and revolutionary effects on accident reduction and more efficient use of travel time, with outstanding socio‐economic impact. Despite these benefits, to make AVs accepted by a wide demographic and produce them on an industrial scale with a reasonable price, there are still a number of technological and social challenges that need to be tackled. Path Tracking Controller (PTC) of AVs is one of the high potential subsystems that can be further improved in order to achieve more accurate, robust and comfortable tracking performance. This study provides a critical review and simulation study of several selected techniques used for the design of PTC of AVs. The AVs are assumed to have limited controllability due to non‐holonomic constraints, such as car‐like vehicles and differential drive mobile robots. A detailed discussion will be provided on the simulation outcomes as well as the pros and cons of each technique for the sake of implementation and improvement of state‐of‐the‐art PTC.

Real road vehicle tests are time consuming, laborious, and costly, and involve several safety concerns. Road vehicle motion simulators (RVMS) could assist with vehicle testing, and eliminate or reduce the difficulties traditionally associated with conducting vehicle tests. However, such simulators must exhibit a high level of fidelity and accuracy in order to provide realistic and reliable outcomes. In this paper, we review existing RVMS and discuss each of the major RVMS subsystems related to the research and development of vehicle dynamics. The possibility of utilising motion simulators to conduct ride and handling test scenarios is also investigated.

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Energy harvesting for wireless sensors and consumer electronic devices can significantly improve reliability and environmental sustainability of the devices. This is achieved by eliminating the dependency of these devices on rechargeable batteries, using clean and/or renewable energy sources. Energy harvesting from various energy sources is widely discussed among researchers and entrepreneurs, including harvesting energy from microscale phenomena. This topic is receiving increasing attention due to the rising numbers of low-power consumer electronic devices and wireless sensors, but also the increasing demand for more convenient and available devices. This article presents a feasibility study for an energy harvesting system based on a human’s breathing motion. The system is based on a modified pants belt that is integrated with an array of piezoelectric films and a harvesting circuit. The proposed energy harvester generates electricity from reciprocal abdominal motions of the human subject. In comparison with existing breathing-based energy harvesters, the proposed system allows for safe and convenient energy harvesting with no influence on the natural movement of the lungs. Stomach pressure analysis and measurement, as well as the design and simulations of the proposed harvester, are presented.

Model Predictive Controller (MPC) is a capable technique for designing Path Tracking Controller (PTC) of Autonomous Vehicles (AVs). The performance of MPC can be significantly enhanced by adopting a high-fidelity and accurate vehicle model. This model should be capable of capturing the full dynamics of the vehicle, including nonlinearities and uncertainties, without imposing a high computational cost for MPC. A data-driven approach realised by learning vehicle dynamics using vehicle operation data can offer a promising solution by providing a suitable trade-off between accurate state predictions and the computational cost for MPC. This work proposes a framework for designing an MPC with a Neural Network (NN)-based learned dynamic model of the vehicle using the plethora of data available from modern vehicle systems. The objective is to integrate an NN-based model with higher accuracy than the conventional vehicle models for the required prediction horizon into MPC for improved tracking performances. The proposed NN-based model is highly capable of approximating latent system states, which are difficult to estimate, and provides more accurate predictions in the presence of parametric uncertainties. The observation of the results in various road conditions shows that the proposed approach outperforms the MPCs with conventional vehicle models.

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