Article
Computer Science, Artificial Intelligence
Alex Gilbert, Dobrila Petrovic, James E. Pickering, Kevin Warwick
Summary: Autonomous vehicles show potential in improving automotive safety by reducing human error in collisions, but complete elimination of collisions cannot be guaranteed. A decision making system has been proposed to select the least severe collision, which has shown promising results in autonomous vehicles.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Ergonomics
Hung Duy Nguyen, Mooryong Choi, Kyoungseok Han
Summary: This paper proposes risk-informed decision-making and control methods for autonomous vehicles (AVs) under severe driving conditions. By using a finite-state machine for decision-making and trajectory optimization theory for control, the AVs can behave appropriately in both normal and emergency situations.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Engineering, Electrical & Electronic
Zhiqiang Zhang, Lei Zhang, Cong Wang, Mingqiang Wang, Dongpu Cao, Zhenpo Wang
Summary: This paper presents an integrated framework for emergency avoidance in complex driving scenarios, combining reasonable decision making and efficient motion control. The decision making is based on driving primitives transition and the motion control utilizes quintic polynomial-based path planning and Linear Time-Varying Model Predictive Control. The effectiveness of the proposed framework is verified through comprehensive Hardware-in-Loop tests, demonstrating timely emergency avoidance with guaranteed vehicle dynamics stability through proper decision-making and accurate path tracking control.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Chuna Wu, Jing Cao, Yuchuan Du
Summary: This study investigates the impacts of advanced driver assistance systems (ADAS) on commercial truck drivers' behaviours using naturalistic data. The findings suggest that ADAS warnings can reduce the number of warnings received by drivers and positively affect their behaviour performance.
IET INTELLIGENT TRANSPORT SYSTEMS
(2023)
Article
Computer Science, Information Systems
Khansa Salsabila Suhaimi, Abdurraafi' Syauqy, Mohammad Salman Subki, Bambang Riyanto Trilaksono, Arief Syaichu Rohman, Yulyan Wahyu Hadi, Handoko Supeno, Dhimas Bintang Kusumawardhana, Dewi Nala Husna
Summary: This paper proposes a modular architecture for a decision-making system for autonomous trams. The architecture consists of risk assessment and decision & planning modules, integrating key functions such as trajectory prediction, safety assessment, adaptive cruise control, collision avoidance, and emergency braking system. Simulation results show a high percentage of mission successes of the decision-making system in mixed-traffic scenarios.
Article
Engineering, Electrical & Electronic
Michael Gerstmair, Martin Gschwandtner, Rainer Findenig, Oliver Lang, Alexander Melzer, Mario Huemer
Summary: One exciting challenge for future scientists and engineers will be to contribute to the development and introduction of self-driving cars. To spark young talents' enthusiasm for advanced driver assistance systems (ADASs) and autonomous driving, we have developed an educational platform with low cost and scalable complexity, providing all sensors needed for vehicle automation in a miniaturized environment.
IEEE SIGNAL PROCESSING MAGAZINE
(2021)
Article
Engineering, Civil
Manel Ammour, Rodolfo Orjuela, Michel Basset
Summary: This paper addresses the challenge of collision avoidance in autonomous driving by utilizing Model Predictive Control (MPC) and a simplified prediction model. The proposed algorithm considers the relative positions and velocities of surrounding vehicles and incorporates decision making and safety constraints to ensure safe driving.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Review
Mathematics
Izaz Raouf, Asif Khan, Salman Khalid, Muhammad Sohail, Muhammad Muzammil Azad, Heung Soo Kim
Summary: The advanced driver assistance system (ADAS) of autonomous vehicles (AVs) has significantly improved passenger safety. However, the continuous use of multiple sensors and actuators in ADAS can lead to failures of AV sensors. Therefore, prognostic health management (PHM) of ADAS is crucial for the smooth and continuous operation of AVs.
Article
Computer Science, Artificial Intelligence
Shaobo Wang, Yingjun Zhang, Xiuguo Zhang, Zongjiang Gao
Summary: This study develops a novel Maritime Autonomous Navigation Decision-making System (MANDS) and successfully carries out real ship trials of autonomous navigation. The system integrates four different navigation tasks and has been tested and verified in both a simulation-based environment and real ship trials.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Matteo Dollorenzo, Vincenzo Dodde, Nicola Ivan Giannoccaro, Davide Palermo
Summary: This paper focuses on the post-processing phase of automotive testing, analyzing the tests and generating summary reports using suitable software and routines. With the collaboration of Nardo Technical Center S.r.l, the goal of improving test maneuver generation and automating data collection and analysis was achieved.
Article
Automation & Control Systems
Icaro Bezerra Viana, Husain Kanchwala, Kenan Ahiska, Nabil Aouf
Summary: This work addresses the cooperative trajectory-planning problem in a double lane change scenario for autonomous driving using distributed model predictive control. Two frameworks are developed to solve this problem: one approach introduces a collision cost function to achieve smooth and bounded collision functions, while the second method uses a hierarchical scheme with trajectory-planning and tracking controller units. Tests and evaluations are conducted using matlab-carsim co-simulation to compare the approaches for cooperative double lane change maneuver in a three-lane road with obstacles.
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME
(2021)
Article
Engineering, Electrical & Electronic
Yongli Chen, Shen Li, Xiaolin Tang, Kai Yang, Dongpu Cao, Xianke Lin
Summary: This article proposes an interaction-aware decision-making approach for autonomous vehicles, which models the interaction between vehicles and pedestrians, and balances safety and efficiency through optimization.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Ergonomics
Andrew J. Leslie, Raymond J. Kiefer, Michael R. Meitzner, Carol A. Flannagan
Summary: The study demonstrated the effectiveness of General Motors' advanced driver assistance and headlighting systems in reducing crashes and injuries. Various systems showed significant reductions in different types of accidents, with some systems showing even more pronounced effects in injury analyses.
ACCIDENT ANALYSIS AND PREVENTION
(2021)
Article
Engineering, Electrical & Electronic
Shaohua Cui, Yongjie Xue, Maolong Lv, Baozhen Yao, Bin Yu
Summary: This paper proposes a cooperative constrained control algorithm for autonomous vehicles (AVs) to reduce inter-vehicle spacing errors, avoid high acceleration, and prevent vehicle collisions. The algorithm considers input quantization and inter-vehicle collision avoidance, and constructs position trajectories of consecutive AVs as collision-free constraints.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Rafal Kot
Summary: This article presents a global trajectory planning system for autonomous underwater vehicles (AUVs) based on a multimodal approach. The system divides the trajectory of the vehicle's movement into segments and calculates them using advanced path planning methods in parallel. The shortest paths in each segment are selected and combined to give the resulting trajectory. The proposed approach improves the system's effectiveness by ensuring a shorter and more reliable trajectory. The final trajectory can be expressed in geographical coordinates with a specific arrival time, making it applicable for mission planning in commercial and military AUVs and autonomous surface vehicles (ASVs) equipped with trajectory tracking control systems.
Article
Engineering, Electrical & Electronic
Zizheng Guo, Ming Zhou, Guofa Li
Summary: Accurately estimating the driver's vigilance state is crucial for improving driving safety and preventing accidents. Previous studies have investigated driving performance measures and eye movement measures for vigilance state estimation, but these measures may be affected by various factors. This study comprehensively uses measures from different domains, including driving performance, eye movement, and EEG, to establish a model for estimating the driver's vigilance state. The model shows promising results and can be combined with arousal methods to enhance driving safety.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Civil
Qingkun Li, Zhenyuan Wang, Wenjun Wang, Chao Zeng, Changxu Wu, Guofa Li, Jia-Sheng Heh, Bo Cheng
Summary: In this study, a human-centered comprehensive measure of take-over performance (HCMTP) was proposed. It involved steps such as sparse principal component analysis, a scale of take-over performance assessment, nonlinear individual mapping functions, and a relabeling algorithm. The results of a verification experiment showed that the HCMTP was effective in reducing interference from individual differences, stochasticity, and data imbalance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Environmental Sciences
Da Tao, Xiaofeng Diao, Xingda Qu, Xiaoting Ma, Tingru Zhang
Summary: Unsafe behaviors, such as violations and human errors, are recognized as the main causes of accidents in nuclear power plants (NPPs). This study proposes an integrated contextual mediated model to examine personal factors that influence unsafe behaviors among commissioning workers at NPPs. The findings suggest that personality traits, executive function, and safety attitudes significantly affect unsafe behaviors, and the effects of certain factors are mediated by safety attitudes.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2023)
Article
Engineering, Industrial
Tingru Zhang, Xing Liu, Weisheng Zeng, Da Tao, Guofa Li, Xingda Qu
Summary: This study compared the effects of three novel input modalities (touchscreen, speech-based, and gesture-based) on driving performance and driver visual behaviors. The results showed that touchscreen interaction had a negative impact on driving performance, while gesture-based interaction had a smaller but still significant crash risk. Speech-based interaction had the least influence on driving and visual performance. The effects of different modalities were robust across different non-driving related tasks (NDRTs).
APPLIED ERGONOMICS
(2023)
Article
Engineering, Electrical & Electronic
Guofa Li, Long Zhang, Ying Zou, Delin Ouyang, Yufei Yuan, Qiuyan Lian, Wenbo Chu, Gang Guo
Summary: This article examines the potential of using EEG signals from only one frequency band or from only a small subset of related electrode channels in recognizing driver vigilance state. The experimental results show that the recognition accuracy is higher when using EEG signals from the selected frequency band (i.e., alpha band) or the selected electrodes (i.e., T7, TP7, and CP1) than when using all the data. These results indicate that higher driver vigilance recognition accuracy can be achieved with much less amount of data, which would facilitate the development of wearable equipment based on EEG signals.
IEEE SENSORS JOURNAL
(2023)
Review
Engineering, Electrical & Electronic
Xinyao Hu, Yang Xu, Haoxi Zhang, Jianhao Xie, Dengqiang Niu, Zhong Zhao, Xingda Qu
Summary: This article provides a comprehensive survey of fiber Bragg grating (FBG) sensing gloves, focusing on sensing characteristics, functionality, performance, system design, structure, and motion tracking algorithms. The survey identifies challenges and research gaps in FBG sensing gloves, including the need for innovation in system and structure design, the development of more advanced models, and the improvement of multiplexing sensing capabilities. This survey can serve as a guide for future research in this field.
IEEE SENSORS JOURNAL
(2023)
Review
Engineering, Electrical & Electronic
Ashish Pandharipande, Chih-Hong Cheng, Justin Dauwels, Sevgi Z. Gurbuz, Javier Ibanez-Guzman, Guofa Li, Andrea Piazzoni, Pu Wang, Avik Santra
Summary: Automotive perception is crucial for achieving high levels of safety and autonomy in driving, involving the understanding of the external driving environment and the internal state of the vehicle cabin and occupants using sensor data. This article provides an overview of different sensor modalities commonly used for perception, along with data processing techniques. Critical aspects such as architectures, algorithms, and safety are discussed, with a focus on machine learning approaches. Future research opportunities in automotive perception are also outlined.
IEEE SENSORS JOURNAL
(2023)
Editorial Material
Engineering, Electrical & Electronic
Avik Santra, Ashish Pandharipande, Pu (Perry) Wang, Sevgi Zubeyde Gurbuz, Javier Ibanez-Guzman, Chih-Hong Cheng, Justin Dauwels, Guofa Li
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Industrial
Xinyao Hu, Liyao Jia, Junpeng Tang, Qingsong Duan, Chao Chen, Zhong Zhao, Xingda Qu
Summary: This study aimed to investigate the effects of backpack load on spatiotemporal turning gait parameters. Twelve young male participants performed 900 left turns under three backpack load conditions. The findings revealed that people might adjust their stepping patterns during turning when carrying moderate backpack load, which could increase the risk of falls. These insights provide practical implications for interventions in occupational settings to minimize fall risk.
INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS
(2023)
Article
Computer Science, Artificial Intelligence
Guofa Li, Zefeng Ji, Shen Li, Xiao Luo, Xingda Qu
Summary: This paper proposes a new off-policy imitation learning method for autonomous driving by using task knowledge distillation, which overcomes the dependence on large scales of time-consuming, laborious, and reliable labels in existing behavioral cloning methods. The experiment results show that our method can achieve satisfactory route-following performance in realistic urban driving scenes and can transfer the driving strategies to new unknown scenes under various illumination and weather scenarios for autonomous driving.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Engineering, Electrical & Electronic
Guofa Li, Xingyu Chi, Xingda Qu
Summary: This paper proposes an improved bidirectional feature pyramid module (BiFPN) and a channel attention module (Seblock) to address issues in existing methods based on monocular camera sensor. The improved BiFPN facilitates efficient fusion of multi-scale features, while Seblock enhances useful information by redistributing channel feature weights. Experimental results demonstrate that this method is competitive with state-of-the-art algorithms and preserves fine-grained texture of scene depth.
AUTOMOTIVE INNOVATION
(2023)
Article
Engineering, Civil
Guofa Li, Xuanhu Qian, Xingda Qu
Summary: High-quality fusion images with infrared and visible information play a crucial role in intelligent and safe driving. To address the issue of unclear fusion images due to noise in the infrared image and loss of texture information from the visible image, we propose a novel two-stage network called SOSMaskFuse. The experimental results demonstrate that our proposed network outperforms other algorithms in terms of quality and effectiveness.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Qingkun Li, Yizi Su, Wenjun Wang, Zhenyuan Wang, Jibo He, Guofa Li, Chao Zeng, Bo Cheng
Summary: Although latent hazard notification for highly automated driving is expected to enhance traffic safety, its practical effects have yet to be verified. This study investigated the expected safety benefits and driver behavioral adaptation based on structural equation modeling. The findings reveal that while latent hazard notification significantly improves driver attention and enhances traffic safety, it also increases driver trust and impairs traffic safety due to driver behavioral adaptation.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Guofa Li, Delin Ouyang, Liu Yang, Qingkun Li, Kai Tian, Baiheng Wu, Gang Guo
Summary: This study proposes a new linear domain adaptation approach using experiment-level batch normalization and a single-layer depthwise convolutional neural network to improve emotion recognition accuracy across individuals. The experiment results show that EEG signal waves are similar and EEG data have characteristics including integration of channels and hierarchy of frequency bands.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Guofa Li, Weiyan Zhou, Siyan Lin, Shen Li, Xingda Qu
Summary: This paper proposes an improved decision-making method based on deep reinforcement learning to address on-ramp merging challenges in highway autonomous driving. It introduces a novel safety indicator called time difference to merging (TDTM) in conjunction with the classic time to collision (TTC) indicator to evaluate driving safety and assist the merging vehicle in finding a suitable gap in traffic. The proposed solution achieved a higher on-ramp merging success rate of 99.96% compared to other methods.
AUTOMOTIVE INNOVATION
(2023)
Article
Transportation Science & Technology
Yue Zhao, Liujiang Kang, Huijun Sun, Jianjun Wu, Nsabimana Buhigiro
Summary: This study proposes a 2-population 3-strategy evolutionary game model to address the issue of subway network operation extension. The analysis reveals that the rule of maximum total fitness ensures the priority of evolutionary equilibrium strategies, and proper adjustment minutes can enhance the effectiveness of operation extension.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Hongtao Hu, Jiao Mob, Lu Zhen
Summary: This study investigates the challenges of daily storage yard management in marine container terminals considering delayed transshipment of containers. A mixed-integer linear programming model is proposed to minimize various costs associated with transportation and yard management. The improved Benders decomposition algorithm is applied to solve the problem effectively and efficiently.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Zhandong Xu, Yiyang Peng, Guoyuan Li, Anthony Chen, Xiaobo Liu
Summary: This paper studied the impact of range anxiety among electric vehicle drivers on traffic assignment. Two types of range-constrained traffic assignment problems were defined based on discrete or continuous distributed range anxiety. Models and algorithms were proposed to solve the two types of problems. Experimental results showed the superiority of the proposed algorithm and revealed that drivers with heightened range anxiety may cause severe congestion.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Chuanjia Li, Maosi Geng, Yong Chen, Zeen Cai, Zheng Zhu, Xiqun (Michael) Chen
Summary: Understanding spatial-temporal stochasticity in shared mobility is crucial, and this study introduces the Bi-STTNP prediction model that provides probabilistic predictions and uncertainty estimations for ride-sourcing demand, outperforming conventional deep learning methods. The model captures the multivariate spatial-temporal Gaussian distribution of demand and offers comprehensive uncertainty representations.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Benjamin Coifman, Lizhe Li
Summary: This paper develops a partial trajectory method for aligning views from successive fixed cameras in order to ensure high fidelity with the actual vehicle movements. The method operates on the output of vehicle tracking to provide direct feedback and improve alignment quality. Experimental results show that this method can enhance accuracy and increase the number of vehicles in the dataset.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Mohsen Dastpak, Fausto Errico, Ola Jabali, Federico Malucelli
Summary: This article discusses the problem of an Electric Vehicle (EV) finding the shortest route from an origin to a destination and proposes a problem model that considers the occupancy indicator information of charging stations. A Markov Decision Process formulation is presented to optimize the EV routing and charging policy. A reoptimization algorithm is developed to establish the sequence of charging station visits and charging amounts based on system updates. Results from a comprehensive computational study show that the proposed method significantly reduces waiting times and total trip duration.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
