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


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    Titel :

    Advances in Automated Driving Systems


    Beteiligte:
    Eichberger, Arno (Herausgeber:in) / Szalay, Zsolt (Herausgeber:in) / Fellendorf, Martin (Herausgeber:in) / Liu, Henry (Herausgeber:in)

    Erscheinungsdatum :

    2022


    Format / Umfang :

    1 Online-Ressource (294 p.)



    Medientyp :

    Buch


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt


    Schlagwörter :

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