An Improved Distraction Behavior Detection Algorithm Based on YOLOv5

Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies.Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents,thereby providing a guarantee for the safety of drivers.However,detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds,varying target scales,and different resolutions.Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios,this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5.The algorithm integrates Attention-based Intra-scale Feature Interaction(AIFI)into the backbone network,enabling it to focus on enhancing feature interactions within the same scale through the attention mechanism.By emphasizing important features,this approach improves detection accuracy,thereby enhancing performance in complex backgrounds.Additionally,a Triple Feature Encoding(TFE)module has been added to the neck network.This module utilizes multi-scale features,encoding and fusing them to create a more detailed and comprehensive feature representation,enhancing object detection and localization,and enabling the algorithm to fully understand the image.Finally,the shape-IoU(Intersection over Union)loss function is adopted to replace the original IoU for more precise bounding box regression.Comparative evaluation of the improved YOLOv5 distraction detection algorithm against the original YOLOv5 algorithm shows an average accuracy improvement of 1.8%,indicating significant advantages in solving distracted driving problems.

An Investigation of Drivers’ Comprehension of Distracted Driving Slogans

Distracted driving occurs when a driver diverts the primary attention from driving to another task. Using mobile devices such as a cellphone for texting, calls, or other manipulation while driving has the highest potential for distraction because it combines both forms of distractions, manual, visual, and cognitive. Some states in the US have posted slogans including “W8 2 TXT, it’s a law”, “Don’t Drive inTEXTicated”, “PLS dnt txt n drv”, “Don’t tempt F8 that txt can w8”, and “DNT TXT & DRV” along highways to convey the dangers and laws regarding distracted driving to minimize incidences of distracted-related crashes This study surveyed 347 people using the five distraction slogans in a college town. The results showed that younger drivers have a higher level of comprehension compared to older drivers. Further, the results showed that drivers with university education or more years of driving experience have a higher comprehension level of distraction signs compared to their counterparts.

Determining driver perceptions about distractions and modeling their effects on driving behavior at different age groups

This study was aimed at determining the driving distractions which are perceived most hazardous and determining the effects of these distractions and age on speed and headway. A questionnaire survey was done to find out the opinion of drivers related to the most hazardous distraction. 639 responses were collected in the survey which were used to determine the top-rated distractions for drivers in Bahrain. Roadside observations were taken to observe the speed, headway, age and type of distraction for the driver. Speed was observed for 48 drivers while headway was observed for 36 drivers along with other parameters. The most hazardous distractions, according to the participants of the questionnaire, are using mobile phones, handling children, and accidents or incidents on the road. Further, the results of the two-way analysis of variance(ANOVA) test and regression analysis demonstrated that using mobile phones and age have a significant effect on both speed and headway. Speed tends to decrease with distraction for all age groups while decreasing the headway for young and middle-aged drivers. The effect of distraction is higher than the effect of age on speed, as well as headway. Texting has the most significant effect among distractions on headway. It is hereby recommended that policymakers should focus on increasing awareness and stringent law enforcement related to handling mobile phones and children, especially for young and middle-aged drivers.

Analysis of HAZMAT truck driver fatigue and distracted driving with warning-based data and association rules mining

Professional drivers are more frequently exposed to longer driving distance and travel time,leading to a higher possibility of safety risk for distraction and fatigue.The widespread and common use of commercial driver monitoring systems(DMS)provides a potential for data collection.It increases the amount of data characterizing driver behavior that can be used for further safety research.This study utilized DMS warning-based data and applied an association rule mining approach to explore risk factors contributing to hazardous materials(HAZMAT)truck driver inattention.A total of 499 HAZMAT truck driver inattentive warning events were used to find rules that will predict the occurrence of driver’s fatigue and distraction.First,Fisher’s exact tests were performed to examine the association between the frequency of driver inattentive behavior warnings and risk factors.Second,support,confidence,and lift values were used as measurements to quantify the relative strength of the association rules generated by the Apriori algorithm.Results show that speed between 40and 49 km/h,relatively longer travel time(3-6 h),freeway,tangent section,off-peak hour and clear weather condition are found to be highly associated with fatigue driving,while nighttime during 18:00 to 23:59,speed between 70 and 80 km/h,travel time between 1 and 3 h,freeways,acceleration less than 0.5 m/s^(2),visibility greater than 1000 m,and tangent roadway section are found to be highly associated with distracted driving.By focusing on the specific feature groups,these association rules would help in the development of mitigating distraction and fatigue driving countermeasures and enforcement approaches.

Investigating the trip configured causal effect of distracted driving on aggressive driving behavior for e-hailing taxi drivers

Risky driving behavior of taxi drivers typically evaluated for full operation or sometimes sorted into occupied and empty running trips.In this paper,we simultaneously analyze aggressive driving and distracted driving of taxi drivers under three different trip categories.Trip origin is considered a transition from without ride-order to with ride-order travelling or from with ride-order to occupied travelling,and a destination as a transition from occupied to without ride-order travelling and vice versa.Distracted driving is characterized by driver interference,driver mobile use and some entertainment aspects,while specific harmful and risky actions are considered for aggressive driving.High-resolution and real-time kinematic parameters of taxis were recorded by the in-vehicle recorder VBOX for overall 562 trips.The distracted driving parameters and aggressive driving actions were monitored through python data collector web application that was specially programmed for this particular research.Besides dual dash cam(i.e.,front and inside car camera),drivers’ whole day driving history from their Chinese ride-hailing Di Di smart application was used to differentiate occupied trips,unoccupied trips with ride-order and unoccupied trips without ride-order.Structural equation modeling(SEM) is practiced in this paper to understand the influence of distracted driving indicators on aggressive driving behaviors.The multi-group model analysis of SEM indicated that handling distracted risky driving could control aggressive driving behavior up to 96% and 98% inunoccupied without ride-order trips and unoccupied trips with ride-order respectively.The model has also identified the sensitive risky driving indicators for each group separately.

An investigation on the link between driver demographic characteristics and distracted driving by using the SHRP 2 naturalistic driving data

Purpose–The purpose of this paper is to investigate the influence of driver demographic characteristics on the driving safety involving cell phone usages.Design/methodology/approach–A total of 1,432 crashes and 19,714 baselines were collected for the Strategic Highway Research Program 2 naturalistic driving research.The authors used a case-control approach to estimate the prevalence and the population attributable risk percentage.The mixed logistic regression model is used to evaluate the correlation between different driver demographic characteristics(age,driving experience or their combination)and the crash risk regarding cell phone engagements,as well as the correlation among the likelihood of the cell phone engagement during the driving,multiple driver demographic characteristics(gender,age and driving experience)and environment conditions.Findings–Senior drivers face an extremely high crash risk when distracted by cell phone during driving,but they are not involved in crashes at a large scale.On the contrary,cell phone usages account for a far larger percentage of total crashes for young drivers.Similarly,experienced drivers and experienced-middle-aged drivers seem less likely to be impacted by the cell phone while driving,and cell phone engagements are attributed to a lower percentage of total crashes for them.Furthermore,experienced,senior or male drivers are less likely to engage in cell phone-related secondary tasks while driving.Originality/value–The results provide support to guide countermeasures and vehicle design.

Opportunities and challenges of smart mobile applications in transportation

Smart mobile applications are software applications that are designed to run on smart phones, tablets, and other mobile electronic devices. In this era of rapid technological advances, these applications have become one of the primary tools we use daily both in our personal and professional lives. The applications play key roles in facilitating many applications that are pivotal in our today＇s society including communication, education, business, entertainment, medical, finance, travel, utilities, social, and transportation. This paper reviewed the opportunities and challenges of the applications related to transportation. The opportunities revealed include route planning, ridesharing/carpooling, traffic safety, parking information, transportation data collection, fuel emissions and consumption, and travel information. The potential users of these applications in the field of transportation include （I） transportation agencies for travel data collection, travel information, ridesharing/carpooling, and traffic safety, （2） engineering students for field data collection such as travel speed, travel time, and vehicle count, and （3） general traveling public for route planning, ridesharing/carpooling, parking, traffic safety, and travel information. Significant usage of smart mobile applications can be potentially very beneficial, particularly in automobile travel mode to reduce travel time, cost, and vehicle emissions. In the end this would make travel safer and living environments greener and healthier. However, road users＇ interactions with these applications could manually, visually, and cognitively divert their attention from the primary task of driving or walking. Distracted road users expose themselves and others to unsafe behavior than undistracted. Road safety education and awareness programs are vital to discourage the use of applications that stimulate unsafe driving/walking behaviors. Educating the traveling public about the dangers of unsafe driving/walking behavior could have significant safety benefits to all road users. Future research needs to compare accuracies of the applications and provide guidelines for selecting them for certain transportation related applications.