1
|
Wang X, Chen L, Shi H, Han J, Wang G, Wang Q, Zhong F, Li H. A Real-Time Recognition System of Driving Propensity Based on AutoNavi Navigation Data. SENSORS (BASEL, SWITZERLAND) 2022; 22:4883. [PMID: 35808374 PMCID: PMC9269833 DOI: 10.3390/s22134883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/23/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
Driving propensity is the driver's attitude towards the actual traffic situation and the corresponding decision-making or behavior during the driving process. It is of great significance to improve the accuracy of safety early warning and reduce traffic accidents. In this paper, a real-time identification system of driving propensity based on AutoNavi navigation data is proposed. The main work includes: (1) A dynamic data acquisition method of AutoNavi navigation is proposed to obtain the time, speed and acceleration of the driver during the navigation process. (2) The dynamic data collection method of AutoNavi navigation is analyzed and verified through the dynamic data obtained in the real vehicle experiment. The principal component analysis method is used to process the experimental data to extract the driving propensity characteristics variables. (3) The fruit fly optimization algorithm combined with GRNN (generalized neural network) and the feature variable set are used to build a FOA-GRNN-based model. The results show that the overall accuracy of the model can reach 94.17%. (4) A driving propensity identification system is constructed. The system has been verified through real vehicle test experiments. This paper provides a novel and convenient method for building personalized intelligent driver assistance systems in practical applications.
Collapse
Affiliation(s)
- Xiaoyuan Wang
- College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China; (L.C.); (H.S.); (J.H.); (G.W.); (Q.W.); (F.Z.); (H.L.)
- Collaborative Innovation Center for Intelligent Green Manufacturing Technology and Equipment of Shandong, Qingdao 266000, China
| | - Longfei Chen
- College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China; (L.C.); (H.S.); (J.H.); (G.W.); (Q.W.); (F.Z.); (H.L.)
| | - Huili Shi
- College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China; (L.C.); (H.S.); (J.H.); (G.W.); (Q.W.); (F.Z.); (H.L.)
| | - Junyan Han
- College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China; (L.C.); (H.S.); (J.H.); (G.W.); (Q.W.); (F.Z.); (H.L.)
| | - Gang Wang
- College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China; (L.C.); (H.S.); (J.H.); (G.W.); (Q.W.); (F.Z.); (H.L.)
| | - Quanzheng Wang
- College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China; (L.C.); (H.S.); (J.H.); (G.W.); (Q.W.); (F.Z.); (H.L.)
| | - Fusheng Zhong
- College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China; (L.C.); (H.S.); (J.H.); (G.W.); (Q.W.); (F.Z.); (H.L.)
| | - Hao Li
- College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China; (L.C.); (H.S.); (J.H.); (G.W.); (Q.W.); (F.Z.); (H.L.)
| |
Collapse
|
2
|
Suarez J, Makridis M, Anesiadou A, Komnos D, Ciuffo B, Fontaras G. Benchmarking the driver acceleration impact on vehicle energy consumption and CO 2 emissions. TRANSPORTATION RESEARCH. PART D, TRANSPORT AND ENVIRONMENT 2022; 107:103282. [PMID: 35784495 PMCID: PMC9231560 DOI: 10.1016/j.trd.2022.103282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The study proposes a methodology for quantifying the impact of real-world heterogeneous driving behavior on vehicle energy consumption, linking instantaneous acceleration heterogeneity and CO2 emissions. Data recorded from 20 different drivers under real driving are benchmarked against the Worldwide Harmonized Light Vehicle Test Cycle (WLTC), first by correlating the speed cycle with individual driver behavior and then by quantifying the CO2 emissions and consumption. The vehicle-Independent Driving Style metric (IDS) is used to quantify acceleration dynamicity, introducing driving style stochasticity by means of probability distribution functions. Results show that the WLTC cycle assumes a relatively smooth acceleration style compared to the observed ones. The method successfully associates acceleration dynamicity to CO2 emissions. We observe a 5% difference in the CO2 emissions between the most favourable and the least favourable case. The intra-driver variance reached 3%, while the inter-driver variance is below 2%. The approach can be used for quantifying the driving style induced emissions divergence.
Collapse
Affiliation(s)
- Jaime Suarez
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Michail Makridis
- ETH Zürich, Institute for Transport Planning and Systems (IVT), Zürich, Switzerland
| | | | | | - Biagio Ciuffo
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | |
Collapse
|
3
|
Zhao S, Guan W, Qi G, Li P. Heterogeneous overtaking and learning styles with varied EEG patterns in a reinforced driving task. ACCIDENT; ANALYSIS AND PREVENTION 2022; 171:106665. [PMID: 35421817 DOI: 10.1016/j.aap.2022.106665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 03/18/2022] [Accepted: 04/02/2022] [Indexed: 06/14/2023]
Abstract
Overtaking maneuvers occur when vehicle drivers pursue higher driving speeds or comfort scenarios through back-to-back lane-changing behaviors, which require active participation of mental resources and certain self-learning practices. However, few studies have investigated how brain activities change during overtaking. Moreover, the learning process, which indicates the heterogeneity of drivers from a process-based perspective, has been neglected. In this work, we studied varied overtaking and learning styles using electroencephalogram (EEG) signals collected from drivers during a simulated driving task with a possible learning process. The average speed, standard deviation of speed, steering wheel angle and lateral movement distance of overtaking behaviors are analyzed in these reinforced tasks to evaluate overtaking performance. Four types of overtaking styles (i.e., low-speed type, low-speed & strong-oscillation type, high-speed & strong-steering type, and high-speed & close-distance type) and three types of learning styles (i.e., stable, adaptive and changeful) are discovered, not only from eventual overtaking behaviors but also from behavioral changes in a certain learning process. EEG features, such as the power spectral density (PSD) in the θ, α, β and γ bands, are extracted to characterize driver mental states and to correlate with heterogeneous learning styles. The obtained results show that fatigue and fatigue confrontation are more likely with a stable learning style, and the mental workload is reduced with an adaptive learning style, whereas no significant changes in brain activity are apparent with a changeful learning style. Understanding and recognizing heterogeneous overtaking and learning styles with varying EEG patterns will be extremely useful in the future for deep integration of advanced driving assistance systems (ADASs) and brain computer interface (BCI) systems.
Collapse
Affiliation(s)
- Shuo Zhao
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, PR China
| | - Wei Guan
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, PR China
| | - Geqi Qi
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, PR China.
| | - Peihao Li
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, PR China
| |
Collapse
|
4
|
Suarez J, Makridis M, Anesiadou A, Komnos D, Ciuffo B, Fontaras G. Benchmarking the driver acceleration impact on vehicle energy consumption and CO 2 emissions. TRANSPORTATION RESEARCH. PART D, TRANSPORT AND ENVIRONMENT 2022. [PMID: 35784495 DOI: 10.1016/j.trd.2022.103228] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
The study proposes a methodology for quantifying the impact of real-world heterogeneous driving behavior on vehicle energy consumption, linking instantaneous acceleration heterogeneity and CO2 emissions. Data recorded from 20 different drivers under real driving are benchmarked against the Worldwide Harmonized Light Vehicle Test Cycle (WLTC), first by correlating the speed cycle with individual driver behavior and then by quantifying the CO2 emissions and consumption. The vehicle-Independent Driving Style metric (IDS) is used to quantify acceleration dynamicity, introducing driving style stochasticity by means of probability distribution functions. Results show that the WLTC cycle assumes a relatively smooth acceleration style compared to the observed ones. The method successfully associates acceleration dynamicity to CO2 emissions. We observe a 5% difference in the CO2 emissions between the most favourable and the least favourable case. The intra-driver variance reached 3%, while the inter-driver variance is below 2%. The approach can be used for quantifying the driving style induced emissions divergence.
Collapse
Affiliation(s)
- Jaime Suarez
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Michail Makridis
- ETH Zürich, Institute for Transport Planning and Systems (IVT), Zürich, Switzerland
| | | | | | - Biagio Ciuffo
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | |
Collapse
|
5
|
Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System. SENSORS 2021; 21:s21216985. [PMID: 34770304 PMCID: PMC8588463 DOI: 10.3390/s21216985] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 11/16/2022]
Abstract
Physiological signals are immediate and sensitive to neurological changes resulting from the mental workload induced by various driving environments and are considered a quantifying tool for understanding the association between neurological outcomes and driving cognitive workloads. Neurological assessment, outside of a highly-equipped clinical setting, requires an ambulatory electroencephalography (EEG) headset. This study aimed to quantify neurological biomarkers during a resting state and two different scenarios of driving states in a virtual driving environment. We investigated the neurological responses of seventeen healthy male drivers. EEG data were measured in an initial resting state, city-roadways driving state, and expressway driving state using a portable EEG headset in a driving simulator. During the experiment, the participants drove while experiencing cognitive workloads due to various driving environments, such as road traffic conditions, lane changes of surrounding vehicles, the speed limit, etc. The power of the beta and gamma bands decreased, and the power of the delta waves, theta, and frontal theta asymmetry increased in the driving state relative to the resting state. Delta-alpha ratio (DAR) and delta-theta ratio (DTR) showed a strong correlation with a resting state, city-roadways driving state, and expressway driving state. Binary machine-learning (ML) classification models showed a near-perfect accuracy between the resting state and driving state. Moderate classification performances were observed between the resting state, city-roadways state, and expressway state in multi-class classification. An EEG-based neurological state prediction approach may be utilized in an advanced driver-assistance system (ADAS).
Collapse
|
6
|
Haghani M, Bliemer MCJ, Farooq B, Kim I, Li Z, Oh C, Shahhoseini Z, MacDougall H. Applications of brain imaging methods in driving behaviour research. ACCIDENT; ANALYSIS AND PREVENTION 2021; 154:106093. [PMID: 33770719 DOI: 10.1016/j.aap.2021.106093] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 01/14/2021] [Accepted: 03/15/2021] [Indexed: 06/12/2023]
Abstract
Applications of neuroimaging methods have substantially contributed to the scientific understanding of human factors during driving by providing a deeper insight into the neuro-cognitive aspects of driver brain. This has been achieved by conducting simulated (and occasionally, field) driving experiments while collecting driver brain signals of various types. Here, this sector of studies is comprehensively reviewed at both macro and micro scales. At the macro scale, bibliometric aspects of these studies are analysed. At the micro scale, different themes of neuroimaging driving behaviour research are identified and the findings within each theme are synthesised. The surveyed literature has reported on applications of four major brain imaging methods. These include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG), with the first two being the most common methods in this domain. While collecting driver fMRI signal has been particularly instrumental in studying neural correlates of intoxicated driving (e.g. alcohol or cannabis) or distracted driving, the EEG method has been predominantly utilised in relation to the efforts aiming at development of automatic fatigue/drowsiness detection systems, a topic to which the literature on neuro-ergonomics of driving particularly has shown a spike of interest within the last few years. The survey also reveals that topics such as driver brain activity in semi-automated settings or neural activity of drivers with brain injuries or chronic neurological conditions have by contrast been investigated to a very limited extent. Potential topics in driving behaviour research are identified that could benefit from the adoption of neuroimaging methods in future studies. In terms of practicality, while fMRI and MEG experiments have proven rather invasive and technologically challenging for adoption in driving behaviour research, EEG and fNIRS applications have been more diverse. They have even been tested beyond simulated driving settings, in field driving experiments. Advantages and limitations of each of these four neuroimaging methods in the context of driving behaviour experiments are outlined in the paper.
Collapse
Affiliation(s)
- Milad Haghani
- Institute of Transport and Logistics Studies, The University of Sydney Business School, The University of Sydney, NSW, Australia; Centre for Spatial Data Infrastructure and Land Administration (CSDILA), School of Electrical, Mechanical and Infrastructure Engineering, The University of Melbourne, Australia.
| | - Michiel C J Bliemer
- Institute of Transport and Logistics Studies, The University of Sydney Business School, The University of Sydney, NSW, Australia
| | - Bilal Farooq
- Laboratory of Innovations in Transportation, Ryerson University, Toronto, Canada
| | - Inhi Kim
- Institute of Transport Studies, Department of Civil Engineering, Monash University, VIC, Australia; Department of Civil and Environmental Engineering, Kongju National University, Cheonan, Republic of Korea
| | - Zhibin Li
- School of Transportation, Southeast University, Nanjing, China
| | - Cheol Oh
- Department of Transportation and Logistics Engineering, Hanyang University, Republic of Korea
| | | | - Hamish MacDougall
- School of Psychology, Faculty of Science, The University of Sydney, Sydney, Australia
| |
Collapse
|
7
|
Kurisu K, Tsurutani Y, Inoue K, Hoshino Y, Saiki F, Yoshiuchi K. Intra-individual association between C-reactive protein and insulin administration in postoperative lumbar spinal canal stenosis patients: A retrospective cohort study. J Diabetes Investig 2020; 11:980-984. [PMID: 31912618 PMCID: PMC7378432 DOI: 10.1111/jdi.13210] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 12/24/2019] [Accepted: 01/06/2020] [Indexed: 12/05/2022] Open
Abstract
The association of intra-individual variability in insulin requirements with C-reactive protein levels among acute phase patients remains unclear. This retrospective cohort study aimed to evaluate this association. Patients with type 2 diabetes undergoing surgery for lumbar spinal canal stenosis were included in the study. We analyzed 286 records of 49 patients using the linear mixed effects model. The model showed C-reactive protein levels to be significantly associated with insulin requirements, with an effect size of 0.60 U/day for an elevation of 1 mg/dL. The effect size was increased in patients with higher hemoglobin A1c levels. Our findings imply that C-reactive protein levels could be a useful clinical biomarker when blood glucose levels are controlled in acute phase patients.
Collapse
Affiliation(s)
- Ken Kurisu
- Department of Endocrinology and Diabetes CenterYokohama Rosai HospitalYokohamaJapan
- Department of Psychosomatic MedicineYokohama Rosai HospitalYokohamaJapan
- Department of Stress Sciences and Psychosomatic MedicineGraduate School of MedicineThe University of TokyoTokyoJapan
| | - Yuya Tsurutani
- Department of Endocrinology and Diabetes CenterYokohama Rosai HospitalYokohamaJapan
| | - Kosuke Inoue
- Department of EpidemiologyUCLA Fielding School of Public HealthLos AngelesCaliforniaUSA
| | - Yoshitomo Hoshino
- Department of Endocrinology and Diabetes CenterYokohama Rosai HospitalYokohamaJapan
| | - Fumiko Saiki
- Department of Orthopedic SurgeryYokohama Rosai HospitalYokohamaJapan
| | - Kazuhiro Yoshiuchi
- Department of Stress Sciences and Psychosomatic MedicineGraduate School of MedicineThe University of TokyoTokyoJapan
| |
Collapse
|