TY - BOOK ID - 145014376 TI - Advances in Automated Driving Systems AU - Eichberger, Arno AU - Szalay, Zsolt AU - Fellendorf, Martin AU - Liu, Henry PY - 2022 PB - Basel MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Technology: general issues KW - History of engineering & technology KW - automated driving KW - scenario-based testing KW - software framework KW - traffic signs KW - ADAS KW - traffic sign recognition system KW - cooperative perception KW - ITS KW - digital twin KW - sensor fusion KW - edge cloud KW - autonomous drifting KW - model predictive control (MPC) KW - successive linearization KW - adaptive control KW - vehicle motion control KW - varying road surfaces KW - vehicle dynamics KW - Mask R-CNN KW - transfer learning KW - inverse gamma correction KW - illumination KW - instance segmentation KW - pedestrian custom dataset KW - deep learning KW - wheel loaders KW - throttle prediction KW - state prediction KW - automation KW - safety validation KW - automated driving systems KW - decomposition KW - modular safety approval KW - modular testing KW - fault tree analysis KW - adaptive cruise control KW - informed machine learning KW - physics-guided reinforcement learning KW - safety KW - autonomous vehicles KW - autonomous conflict management KW - UTM KW - UAV KW - UGV KW - U-Space KW - framework development KW - lane detection KW - simulation and modelling KW - multi-layer perceptron KW - convolutional neural network KW - driver drowsiness KW - ECG signal KW - heart rate variability KW - wavelet scalogram KW - automated driving (AD) KW - driving simulator KW - expression of trust KW - acceptance KW - simulator case study KW - NASA TLX KW - advanced driver assistant systems (ADAS) KW - system usability scale KW - driving school KW - virtual validation KW - ground truth KW - reference measurement KW - calibration method KW - simulation KW - traffic evaluation KW - simulation and modeling KW - connected and automated vehicle KW - driver assistance system KW - virtual test and validation KW - radar sensor KW - physical perception model KW - virtual sensor model KW - automated driving KW - scenario-based testing KW - software framework KW - traffic signs KW - ADAS KW - traffic sign recognition system KW - cooperative perception KW - ITS KW - digital twin KW - sensor fusion KW - edge cloud KW - autonomous drifting KW - model predictive control (MPC) KW - successive linearization KW - adaptive control KW - vehicle motion control KW - varying road surfaces KW - vehicle dynamics KW - Mask R-CNN KW - transfer learning KW - inverse gamma correction KW - illumination KW - instance segmentation KW - pedestrian custom dataset KW - deep learning KW - wheel loaders KW - throttle prediction KW - state prediction KW - automation KW - safety validation KW - automated driving systems KW - decomposition KW - modular safety approval KW - modular testing KW - fault tree analysis KW - adaptive cruise control KW - informed machine learning KW - physics-guided reinforcement learning KW - safety KW - autonomous vehicles KW - autonomous conflict management KW - UTM KW - UAV KW - UGV KW - U-Space KW - framework development KW - lane detection KW - simulation and modelling KW - multi-layer perceptron KW - convolutional neural network KW - driver drowsiness KW - ECG signal KW - heart rate variability KW - wavelet scalogram KW - automated driving (AD) KW - driving simulator KW - expression of trust KW - acceptance KW - simulator case study KW - NASA TLX KW - advanced driver assistant systems (ADAS) KW - system usability scale KW - driving school KW - virtual validation KW - ground truth KW - reference measurement KW - calibration method KW - simulation KW - traffic evaluation KW - simulation and modeling KW - connected and automated vehicle KW - driver assistance system KW - virtual test and validation KW - radar sensor KW - physical perception model KW - virtual sensor model UR - https://www.unicat.be/uniCat?func=search&query=sysid:145014376 AB - 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. ER -