In order to quantitatively characterize the influence of cell phone call mode on the drivers visual behavior in different traffic conditions and to analyze the risk level of distracted driving from the visual level, a distracted driving simulation test was carried out based on a driving simulator and eye tracker. Ages 60-74: 67.3 percent. 2: Drug-impaired driving Risky driving behavior No. Nowadays, many countries regulate the use of cell phones while driving and most explicitly prohibit hand-held calls, but there are no clear requirements for other cell phone call methods [27, 28]. Combined with the mechanical division method, the fixation area was divided into six parts, in which: A is the left rear view mirror; B is the road ahead, which is the main focus area during driving; C is the distant areas of the road ahead; D is the vehicle dashboard area; E is the cell phone placement area of the central console; and F is the right rear view mirror. (4)Calculate the grey correlation degree. The IEFA was mainly distributed from 0.769 to 1.842bits in the free flow scenario and from 0.828 to 1.672bits in the congested flow scenario. Learn how to grow your business reselling best-in-class Lytx solutions. 647653, 2021. 575588, 2018. But because driving comes so easily, we often forget how dangerous it can be. Meet our team and learn how our services help ensure your success. Risky Driving Behaviors of Drivers Who Use Alcohol and Marijuana 10, no. 2, pp. (2)Determine the reference sequence and comparison sequence. 45, pp. With each new video clip reviewed and processed, Lytxs AI algorithms become even more nuanced and accurate in identifying high-risk driving behaviors in real time, enabling a safer and more productive future for fleets and drivers alike. 830846, 2022. Watch our videos to see the latest in driver safety, coaching best practices, risk management and more. One-way ANOVA of the influence of driving state on IEFA. In this article, we investigated the influence of the combined effect of traffic conditions and driving states on drivers visual behavior by constructing four visual characteristics indicators, IEFA, saccade amplitude, PARSV, and RCPA, combined with descriptive statistics and ANOVA methods. Of 3 million events coached this summer, the following five behaviors were the most prevalent, with driver unbelted and unsafe following distance together accounting for 54% of the top five. 26362641, 2016. Risky Driving Behaviors The NHTSA says buckling up is the most effective way to protect yourself in a crash. Advertised example rates are returned based on the driver's self-reported data and the driver meeting certain criteria. According to the U.S. Department of Transportations National Highway Traffic Safety Administration, which recently released its latest projections for 2022 traffic fatalities, an estimated 31,785 people were killed in traffic crashes in the first nine months of last year just a slight decrease from the 31,850 estimated deaths that occurred during the same period in 2021. As an objective weighting calculation model, the CRITIC method fully exploits the attributes of the indicator data, takes into account the variability within the indicators and the conflict between the indicators to determine the objective weights, and the weighting results are more reliable and reasonable. The results show that different cell phone call modes increased driving risk in both traffic conditions. The coordinates of fixation points of the 18 drivers were hierarchical clustering, and the intercluster distance decreased with the increase of the number of clusters and finally stabilized gradually with six clusters as the cut-off point. Firstly, the one-way ANOVA was conducted on the PARSV, as shown in Table 7. WebWhat Are Risky Driving Behaviors? Combine Lytx with best-in-class fleet technology for a powerful fleet management solution. This is an open access article distributed under the, Construction of original data matrix. Seat belts keep you from being ejected from a vehicle and are designed to work with airbags, protecting passengers and drivers in the event of a crash. In the two traffic conditions, compared with normal driving, the percentage of fixation point offset distance in the medium and long interval above 300px increased significantly, and the percentage in the short interval of 0100px decreased significantly in the video call state, indicating that most drivers were in a state of visual distraction during this process, and needed to shift their eyes frequently between the cell phone screen and the road ahead, expanding the visual search range of the road area to ensure driving safety. The significance level is set to 0.1, then =1.25, and the value range of is between 0.25 and 0.5. In order to analyze the stability of drivers visual information processing in different driving states and evaluate the negative impact of different driving states on driving safety based on microscopic visual characteristics indicators, the comprehensive scores of drivers visual characteristics indicators in different driving states under two traffic conditions were calculated, respectively, based on the objective weighting results of the improved CRITIC method, which were defined as the visual stability coefficient (VSC) of drivers. Based on the objective weighting of indicators using the improved CRITIC method, the VSC was constructed to quantitatively characterize the visual stability of drivers in different driving states, and the K-means clustering method was used to divide the range of VSC values for four types of visual stability, and to comprehensively assess the safety level of driving states from the perspective of visual psychological safety.