Advances in Automated Driving Systems

• Other Authors: Eichberger, Arno (Editor), Szalay, Zsolt (Editor), Fellendorf, Martin (Editor), Liu, Henry (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: 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; n/a
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.