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Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic.

Technology: general issues --- History of engineering & technology --- automated driving --- scenario-based testing --- software framework --- traffic signs --- ADAS --- traffic sign recognition system --- cooperative perception --- ITS --- digital twin --- sensor fusion --- edge cloud --- autonomous drifting --- model predictive control (MPC) --- successive linearization --- adaptive control --- vehicle motion control --- varying road surfaces --- vehicle dynamics --- Mask R-CNN --- transfer learning --- inverse gamma correction --- illumination --- instance segmentation --- pedestrian custom dataset --- deep learning --- wheel loaders --- throttle prediction --- state prediction --- automation --- safety validation --- automated driving systems --- decomposition --- modular safety approval --- modular testing --- fault tree analysis --- adaptive cruise control --- informed machine learning --- physics-guided reinforcement learning --- safety --- autonomous vehicles --- autonomous conflict management --- UTM --- UAV --- UGV --- U-Space --- framework development --- lane detection --- simulation and modelling --- multi-layer perceptron --- convolutional neural network --- driver drowsiness --- ECG signal --- heart rate variability --- wavelet scalogram --- automated driving (AD) --- driving simulator --- expression of trust --- acceptance --- simulator case study --- NASA TLX --- advanced driver assistant systems (ADAS) --- system usability scale --- driving school --- virtual validation --- ground truth --- reference measurement --- calibration method --- simulation --- traffic evaluation --- simulation and modeling --- connected and automated vehicle --- driver assistance system --- virtual test and validation --- radar sensor --- physical perception model --- virtual sensor model --- automated driving --- scenario-based testing --- software framework --- traffic signs --- ADAS --- traffic sign recognition system --- cooperative perception --- ITS --- digital twin --- sensor fusion --- edge cloud --- autonomous drifting --- model predictive control (MPC) --- successive linearization --- adaptive control --- vehicle motion control --- varying road surfaces --- vehicle dynamics --- Mask R-CNN --- transfer learning --- inverse gamma correction --- illumination --- instance segmentation --- pedestrian custom dataset --- deep learning --- wheel loaders --- throttle prediction --- state prediction --- automation --- safety validation --- automated driving systems --- decomposition --- modular safety approval --- modular testing --- fault tree analysis --- adaptive cruise control --- informed machine learning --- physics-guided reinforcement learning --- safety --- autonomous vehicles --- autonomous conflict management --- UTM --- UAV --- UGV --- U-Space --- framework development --- lane detection --- simulation and modelling --- multi-layer perceptron --- convolutional neural network --- driver drowsiness --- ECG signal --- heart rate variability --- wavelet scalogram --- automated driving (AD) --- driving simulator --- expression of trust --- acceptance --- simulator case study --- NASA TLX --- advanced driver assistant systems (ADAS) --- system usability scale --- driving school --- virtual validation --- ground truth --- reference measurement --- calibration method --- simulation --- traffic evaluation --- simulation and modeling --- connected and automated vehicle --- driver assistance system --- virtual test and validation --- radar sensor --- physical perception model --- virtual sensor model

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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.

Technology: general issues --- History of engineering & technology --- Mechanical engineering & materials --- curb detection --- intelligent vehicles --- autonomous driving --- electro-hydraulic brake system --- master cylinder pressure estimation --- vehicle longitudinal dynamics --- brake linings’ coefficient of friction --- ACC --- safety evaluation --- human-like evaluation --- naturalistic driving study --- driving behavior characteristic --- electric vehicles --- independent drive --- direct yaw control --- torque distribution --- ultra-wideband --- relative localization --- enhanced precision --- clock self-correction --- homotopy --- Levenberg–Marquardt --- electric power steering --- steering actuator --- driverless racing vehicles --- control --- autonomous vehicles --- lane-changing --- decision-making --- path planning --- four-wheel independent drive --- four-wheel independent steering --- path tracking --- handling stability --- active safety control --- electric vehicle --- intelligent sanitation vehicle --- trash can-handling robot --- truss structure --- multi-objective parameter optimization --- topology optimization --- discrete optimization --- multiple load cases --- intelligent electric vehicles --- driver behavior recognition --- multi-semantic description --- confidence fusion --- drift parking --- open-loop control --- supervision mechanism --- two-speed AMT --- in-wheel-drive --- shifting process --- selectable one-way clutch --- five-degree-of-freedom vehicle model --- pressure–position model --- recursive least square --- advanced driver assistant systems --- adaptive cruise control --- direct yaw moment control --- extension control --- model predictive control --- optimization design --- vehicle structure design --- uncertainty --- deceleration device --- tyre-road peak friction coefficient estimation --- tyre model --- normalization --- incentive sensitivity --- four-wheel steering --- semantic segmentation --- high-resolution atlas training --- super-resolution

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

curb detection --- intelligent vehicles --- autonomous driving --- electro-hydraulic brake system --- master cylinder pressure estimation --- vehicle longitudinal dynamics --- brake linings’ coefficient of friction --- ACC --- safety evaluation --- human-like evaluation --- naturalistic driving study --- driving behavior characteristic --- electric vehicles --- independent drive --- direct yaw control --- torque distribution --- ultra-wideband --- relative localization --- enhanced precision --- clock self-correction --- homotopy --- Levenberg–Marquardt --- electric power steering --- steering actuator --- driverless racing vehicles --- control --- autonomous vehicles --- lane-changing --- decision-making --- path planning --- four-wheel independent drive --- four-wheel independent steering --- path tracking --- handling stability --- active safety control --- electric vehicle --- intelligent sanitation vehicle --- trash can-handling robot --- truss structure --- multi-objective parameter optimization --- topology optimization --- discrete optimization --- multiple load cases --- intelligent electric vehicles --- driver behavior recognition --- multi-semantic description --- confidence fusion --- drift parking --- open-loop control --- supervision mechanism --- two-speed AMT --- in-wheel-drive --- shifting process --- selectable one-way clutch --- five-degree-of-freedom vehicle model --- pressure–position model --- recursive least square --- advanced driver assistant systems --- adaptive cruise control --- direct yaw moment control --- extension control --- model predictive control --- optimization design --- vehicle structure design --- uncertainty --- deceleration device --- tyre-road peak friction coefficient estimation --- tyre model --- normalization --- incentive sensitivity --- four-wheel steering --- semantic segmentation --- high-resolution atlas training --- super-resolution