Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

A Context Aware Classification System for Monitoring Driver’s Distraction Levels

Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner

Affective scenarios in automotive design: a human-centred approach towards understanding of emotional experience

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe automotive industry is facing a period of significant transformation due to the arrival of many new digital technologies. As the focus of automotive engineering has shifted from hardware to software, the conventional processes of making, buying and owning an automobile have changed. Peoples’ desires for new automotive experiences are increasing; they demand more sophisticated approaches to the automotive experience beyond merely improving functional requirements for advanced automation systems, interfaces and connectivity. Thus, it is essential to understand human experience in order to help people deal with the high degree of complexity in the driving environment and to help them to cope with unanticipated driving events that involve emotional, psychological or sociological issues. This research takes a human-centred approach to investigating real-life scenarios in which people emotionally engage with automobiles with the aim of developing a relevant set of scenarios for this context. An extensive literature review was conducted of human emotion, memory systems, emotional memory characteristics, scenarios, and scenarios with emotional aspects, followed by a discussion defining scenario development process and affective scenarios. This research provides a methodology for in-depth qualitative studies that develop affective design scenarios with automobiles. As a triangulation approach, two independent studies in different settings explored affective scenario themes in automotive contexts of people’s real-life car stories that made them respond emotionally. The themes that were revealed from both studies were consolidated, and exemplary scenarios of 13 consolidated main themes were formulated to illustrate a set of affective scenarios in automotive contexts. This research leads to an enhanced understanding of a set of critical contexts that automotive practitioners should take into account for future automotive design. Suggestions with possible questions based on the research outcome provide opportunities for them to agilely cope with unanticipated future events, whereby highly complex driving environment by connected and autonomous vehicles. This methodology used here can be replicated for future affective scenario studies focusing on specific products, sub-systems or services such as navigation systems or car-sharing services. The results, which have been validated through a triangulation approach, can bolster the automobile design process by addressing potential issues and challenges in automotive experience by facilitating idea generation, enhancing a shared understanding of critical contexts and by assisting decision-making among stakeholders from different departments

Rural implementation of connected, autonomous and electric vehicles

Connected, autonomous and electric vehicles (CAEV) are at the forefront of transport development. They are intended to provide efficient, safe and sustainable transport solutions to solve everyday transport problems including congestion, accidents and pollution. However, despite significant industry and government investment in the technology, little has been done in the way of exploring the implementation of CAEVs in rural scenarios. This thesis investigates the potential for rural road CAEV implementation in the UK. In this work, the rural digital and physical infrastructure requirements for CAEVs were first investigated through physical road-based experimentation of CAEV technologies. Further investigations into the challenges facing the rural implementation of CAEVs were then conducted through qualitative consultations with transport planning professionals. Quantitative and qualitative analysis of these investigations revealed a need for better rural infrastructure, and an overall lack of understanding regarding CAEVs and their rural implementation requirements amongst the transport planning industry. The need for a measurement tool for transport planners was identified, to expose the industry to, and educate them about, CAEVs and their rural potential. As a result, a CAEV Rural Transport Index (CARTI) is proposed as a simple measurement tool to assess the potential for rural CAEV implementation. The CARTI was implemented, and its effectiveness tested, through further consultation with transport planning professionals. The results indicate the potential for the CARTI to be used as a component of decision-making processes at both local authority and national levels. In conclusion, effective rural CAEV implementation relies on transport planners having a strong understanding of rural community transport needs, the solutions CAEV technologies can offer and the supporting infrastructure they require. Further, the CARTI was found to be an effective tool to support the development of this required understanding and recommendations have therefore been made for its future development