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Silva VC, Dias AS, Greve JMD, Davis CL, Soares ALDS, Brech GC, Ayama S, Jacob-Filho W, Busse AL, de Biase MEM, Canonica AC, Alonso AC. Crash Risk Predictors in Older Drivers: A Cross-Sectional Study Based on a Driving Simulator and Machine Learning Algorithms. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4212. [PMID: 36901230 PMCID: PMC10002325 DOI: 10.3390/ijerph20054212] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
The ability to drive depends on the motor, visual, and cognitive functions, which are necessary to integrate information and respond appropriately to different situations that occur in traffic. The study aimed to evaluate older drivers in a driving simulator and identify motor, cognitive and visual variables that interfere with safe driving through a cluster analysis, and identify the main predictors of traffic crashes. We analyzed the data of older drivers (n = 100, mean age of 72.5 ± 5.7 years) recruited in a hospital in São Paulo, Brazil. The assessments were divided into three domains: motor, visual, and cognitive. The K-Means algorithm was used to identify clusters of individuals with similar characteristics that may be associated with the risk of a traffic crash. The Random Forest algorithm was used to predict road crash in older drivers and identify the predictors (main risk factors) related to the outcome (number of crashes). The analysis identified two clusters, one with 59 participants and another with 41 drivers. There were no differences in the mean of crashes (1.7 vs. 1.8) and infractions (2.6 vs. 2.0) by cluster. However, the drivers allocated in Cluster 1, when compared to Cluster 2, had higher age, driving time, and braking time (p < 0.05). The random forest performed well (r = 0.98, R2 = 0.81) in predicting road crash. Advanced age and the functional reach test were the factors representing the highest risk of road crash. There were no differences in the number of crashes and infractions per cluster. However, the Random Forest model performed well in predicting the number of crashes.
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Affiliation(s)
- Vanderlei Carneiro Silva
- Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
| | - Aluane Silva Dias
- Graduate Program in Aging Science, São Judas Tadeu University (USJT), São Paulo 03166-000, Brazil
| | - Julia Maria D’Andréa Greve
- Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
| | - Catherine L. Davis
- Georgia Prevention Institute, Medical College of Georgia, Augusta University, Augusta, GA 30901, USA
| | - André Luiz de Seixas Soares
- Graduate Program in Aging Science, São Judas Tadeu University (USJT), São Paulo 03166-000, Brazil
- Georgia Prevention Institute, Medical College of Georgia, Augusta University, Augusta, GA 30901, USA
| | - Guilherme Carlos Brech
- Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
- Graduate Program in Aging Science, São Judas Tadeu University (USJT), São Paulo 03166-000, Brazil
| | - Sérgio Ayama
- Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
| | - Wilson Jacob-Filho
- Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
| | - Alexandre Leopold Busse
- Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
| | - Maria Eugênia Mayr de Biase
- Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
| | - Alexandra Carolina Canonica
- Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
| | - Angelica Castilho Alonso
- Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
- Graduate Program in Aging Science, São Judas Tadeu University (USJT), São Paulo 03166-000, Brazil
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Ma J, Li J, Huang H. Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems. SENSORS 2022; 22:s22031189. [PMID: 35161934 PMCID: PMC8838494 DOI: 10.3390/s22031189] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 01/31/2022] [Accepted: 01/31/2022] [Indexed: 01/25/2023]
Abstract
V2X is used for communication between the surrounding pedestrians, vehicles, and roadside units. In the Forward Collision Warning (FCW) of Phase One scenarios in V2X, multimodal modalities and multiple warning stages are the two main warning strategies of FCW. In this study, three warning modalities were introduced, namely auditory warning, visual warning, and haptic warning. Moreover, a multimodal warning and a novel multi-staged HUD warning were established. Then, the above warning strategies were evaluated in objective utility, driving performance, visual workload, and subjective evaluation. As for the driving simulator of the experiment, SCANeR was adopted to develop the driving scenario and an open-cab simulator was built based on Fanatec hardware. Kinematic parameters, location-related data and eye-tracking data were then collected. The results of the Analysis of Variance (ANOVA) indicate that the multimodal warning is significantly better than that of every single modality in utility and longitudinal car-following performance, and there is no significant difference in visual workload between multimodal warning and the baseline. The utility and longitudinal driving performance of multi-staged warning are also better than those of single-stage warning. Finally, the results provide a reference for the warning strategy design of the FCW in Intelligent Connected Vehicles.
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