Article
Engineering, Electrical & Electronic
Katrin Sjoberg
Summary: The trucking industry is facing a driver shortage worldwide, mainly due to low salaries and long periods of time away from home. Addressing the driver shortage requires consideration of various factors, including increasing pay, implementing regulatory changes, and improving working conditions.
IEEE VEHICULAR TECHNOLOGY MAGAZINE
(2022)
Article
Chemistry, Physical
Longping Chen, Kang Yuan, Shiyang Chen, Yanjun Huang, Hassan Askari, Ninghai Yu, Jingyue Mo, Nan Xu, Mingzhi Wu, Hong Chen, Amir Khajepour, Zhonglin Wang
Summary: This paper presents a novel intelligent steering wheel developed based on the concept of triboelectricity for automated driving. A sandwich-type sensor is integrated into the steering wheel to identify driver's steering intention. The superiority of the triboelectric nanogenerator (TENG)-based sensor is demonstrated and classification models are developed using machine learning techniques. The TENG-based sensor shows faster reaction time for emergency obstacle avoidance compared to regular steering wheel sensors, leading to improved human-machine interaction and more efficient vehicle control.
Review
Computer Science, Artificial Intelligence
Claudine Badue, Ranik Guidolini, Raphael Vivacqua Carneiro, Pedro Azevedo, Vinicius B. Cardoso, Avelino Forechi, Luan Jesus, Rodrigo Berriel, Thiago M. Paixao, Filipe Mutz, Lucas de Paula Veronese, Thiago Oliveira-Santos, Alberto F. De Souza
Summary: The research survey examined literature on self-driving cars, focusing on the architecture of autonomy system, perception, and decision-making methods. It also provided a detailed description of the autonomy system of the self-driving car developed at the Universidade Federal do Espirito Santo (UFES). Additionally, prominent self-driving car research platforms developed by academia and technology companies were listed.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Economics
Georg Hirte, Renee Laes, Regine Gerike
Summary: Once automatic vehicles are available, working from self-driving cars becomes an option. It allows firms to socialize office land costs to road infrastructure used by AV's mobile offices.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2023)
Article
Chemistry, Multidisciplinary
Xu Yang, Feiyang Wu, Ruchuan Li, Dong Yao, Lei Meng, Ankai He
Summary: This paper proposes a novel local path planning algorithm based on spatial perception for obstacle avoidance maneuvers of intelligent driving sightseeing vehicles. By utilizing a real-time dynamic perception module and a high-precision positioning module, the algorithm acquires real-time spatial information and integrates safety constraints and curvature constraints to generate smooth obstacle avoidance paths. Simulation analysis and real vehicle verification demonstrate that the algorithm significantly enhances the obstacle avoidance stability of sightseeing vehicles.
APPLIED SCIENCES-BASEL
(2023)
Article
Chemistry, Analytical
Simon Genser, Stefan Muckenhuber, Selim Solmaz, Jakob Reckenzaun
Summary: This paper introduces a novel approach for modeling automotive perception sensors, which combines kernel density estimation with regression modeling to address position measurement errors. The method is suitable for automotive perception sensors that provide object-level position estimations, and has shown good performance in lateral and longitudinal position error after field data collection and experimental validation.
Article
Computer Science, Information Systems
Francesco Malandrino, Carla Fabiana Chiasserini, Gian Michele Dell'Aera
Summary: In this work, we propose an assisted driving system that leverages the synergy between connected vehicles and the edge of the network infrastructure to effectively drive local decisions based on global traffic policies. We integrate different entities within an edge-based architecture to share information and make decisions at different time scales. Using a queuing-based model and optimization problem formulation, we develop an iterative algorithm called Bottleneck Hunting (BH) to make global decisions on traffic flows. Our solution demonstrates significant reductions in travel times compared to traditional approaches.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Engineering, Civil
Amolika Sinha, Daniel Bassil, Sai Chand, Navreet Virdi, Vinayak Dixit
Summary: The study investigates the impact of connected automated buses in a mixed fleet with connected automated vehicles on the performance of urban transport systems. Results show that connected automated buses can significantly reduce travel time and standstill times, while also decreasing forced lane changes between vehicles and improving road safety.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Zhong Cao, Shaobing Xu, Huei Peng, Diange Yang, Robert Zidek
Summary: The paper proposes a confidence-aware reinforcement learning (CARL) method to improve autonomous vehicle performance by integrating RL with rule-based driving policies, intervening only in cases where the rule-based method struggles and the RL policy has high confidence. Simulation results demonstrate the superiority of this approach over pure RL policies and baseline rule-based strategies.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Ergonomics
Peng Liu, Siming Zhai, Tingting Li
Summary: A study found that people's evaluations of aggressive behavior towards automated vehicles (AVs) and human drivers differ. When the victim's identity is salient, aggressive behavior towards AVs is more easily accepted, perceived as less risky, and less negative affect and moral judgment are reported. This finding suggests that AVs may need to hide their identity to blend in with regular vehicles.
ACCIDENT ANALYSIS AND PREVENTION
(2022)
Article
Automation & Control Systems
Chen Lv, Yutong Li, Yang Xing, Chao Huang, Dongpu Cao, Yifan Zhao, Yahui Liu
Summary: This study proposes an intelligent haptic interface based on a two-phase human-machine interaction model, which can switch functionality between predictive guidance and haptic assistance based on the varying state and control ability of human drivers, helping drivers gradually resume manual control. The results from vehicle experiments suggest that this new method effectively enhances driving state recovery and control performance during takeover compared to existing approaches, improving the safety and smoothness of human-machine interaction in automated vehicles.
ADVANCED INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Wenbo Li, Jiyong Xue, Ruichen Tan, Cong Wang, Zejian Deng, Shen Li, Gang Guo, Dongpu Cao
Summary: Affective interaction between the intelligent cockpit and humans is an emerging topic. The intelligent cockpit recognizes emotions through the driver's speech, which has a wide range of technical applications.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Quantum Science & Technology
Caio B. D. Goes, Askery Canabarro, Eduardo Duzzioni, Thiago O. Maciel
Summary: In this study, an automated machine learning approach is proposed for classifying states of two qutrits as separable or entangled, even when the traditional PPT criterion fails. The framework was successfully applied to perform complete quantum state tomography without direct measurement of entanglement, and regression techniques were used to estimate the generalized robustness of entanglement and validate the classifiers.
QUANTUM INFORMATION PROCESSING
(2021)
Review
Biochemistry & Molecular Biology
Charlotte U. Zajc, Benjamin Salzer, Joseph M. Taft, Sai T. Reddy, Manfred Lehner, Michael W. Traxlmayr
Summary: CAR T cells, genetically engineered T cells expressing chimeric antigen receptors, have shown impressive clinical efficacy against B-cell malignancies. However, there are limitations with CAR T cells directed against other tumor entities and antigens, leading to the discussion of alternative engineered binding scaffolds and natural ligands/receptors for CAR design in recent studies. The risk of immunogenicity is also critically discussed, showing that engineered binding scaffolds based on nonhuman proteins can be more similar to humanized scFvs than expected.
Article
Engineering, Civil
Bo Yang, Koichiro Inoue, Zhanhong Yan, Zheng Wang, Satoshi Kitazaki, Kimihiko Nakano
Summary: To ensure driving safety while using level 2 automated driving systems, it is important to understand the influence of these systems on driver behavior. Previous studies focused on drivers' reactions to emergency events, but it is still unclear how drivers interact with level 2 automated driving systems during normal conditions. In a driving simulator experiment, it was observed that drivers' attention levels, especially for the front areas, were significantly lower during level 2 automated driving compared to manual driving.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Sarah Pink, Katalin Osz, Kaspar Raats, Thomas Lindgren, Vaike Fors
Article
Computer Science, Information Systems
Thomas Lindgren, Vaike Fors, Sarah Pink, Katalin Osz
PERSONAL AND UBIQUITOUS COMPUTING
(2020)
Article
Business
Thomas Lindgren, Sarah Pink, Vaike Fors
Summary: Research has shown that understanding user needs and creating positive user experience are crucial for successfully introducing Autonomous Driving vehicles. By using an ethnographic approach with iterative speculative scenarios, we are able to identify new concepts and gain insights into user foresights.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2021)
Proceedings Paper
Computer Science, Cybernetics
Katalin Osz, Kaspar Raats, Vaike Fors, Sarah Pink, Thomas Lindgren
PROCEEDINGS OF THE 30TH AUSTRALIAN COMPUTER-HUMAN INTERACTION CONFERENCE (OZCHI 2018)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Thomas Lindgren, Vaike Fors, Sarah Pink, Magnus Bergquist, Martin Berg
NORDICHI'18: PROCEEDINGS OF THE 10TH NORDIC CONFERENCE ON HUMAN-COMPUTER INTERACTION
(2018)
