E-scooters have rapidly become a popular option for first- and last-mile mobility, yet their integration into urban transportation systems has raised significant safety concerns. This study investigates the feasibility of permitting E-scooter riding on sidewalks under controlled conditions to minimize pedestrian conflicts. Analysis of E-scooter crashes in Daejeon, South Korea, showed that 98.09% of crashes were caused by rider negligence, with “Failure to Fulfill Safe Driving Duty” as the leading factor. To investigate the applicability of safe sidewalk usage, a VR-based simulator experiment was conducted with 41 participants across four scenarios with varying sidewalk widths and pedestrian densities, under speed limits of 10, 15, and 20 km/h. Riding behaviors—including speed stability, braking, steering, and conflict frequency—and gaze behaviors were measured. Results showed that riding at 10 km/h improved riding stability and minimized conflicts. Regression analysis identified pedestrian density as the strongest predictor of conflicts, followed by sidewalk width and riding speed. These findings suggest specific policy needs: ensuring a minimum sidewalk width of 4 m for safe shared use, restricting operation to environments with low-to-moderate pedestrian density, and implementing a 10 km/h speed limit. This study provides evidence-based recommendations for safer integration of E-scooters into pedestrian environments.

The rapid growth of the global aging population presents a significant challenge to road safety, as older drivers face unique risk factors that lead to disproportionately higher rates of traffic-related injuries and fatalities when adjusted for distance traveled. One major contributor to this elevated risk is age-related physical decline. This study aimed to develop and evaluate the effectiveness of DriveSafe60+, a mobile application designed to enhance driving competence and learning among older adults. The application comprises five core components: Driving Ability Test, Health & Alert System, Driving Tips & Training, Safe Driving Report, and Emergency Support (SOS), which were developed based on the competency assessment frameworks of AAMVA, WHO road safety guidelines, Connected Health principles, and Adult Learning Theory. A one-group time-series design was employed with 100 licensed older drivers who used the application for 4 weeks. Driving competence, specifically confidence and decision-making, was assessed before, during, and after application use. Results from a One-way Repeated Measures ANOVA indicated statistically significant improvements in driving competence across all measurement points (F = 1599.932, p < .001). These findings demonstrate that DriveSafe60+ effectively enhanced older adults’ self-awareness and confidence in driving-related decision-making by supporting self-regulated learning and adaptive driving behaviors. The study suggests that mobile technology is a feasible and practical approach to promoting safer, longer driving among older adults in an aging society.

Assessing the effectiveness of driver training interventions in improving safe engagement with vehicle automation systems.

Driving after stroke: A trichotomous logistic regression model to support decision making in uncertain cases.

Impact of Safe Driving Training Programs on Driver Safety Culture

Risky driving behaviour is closely related to traffic accidents, and the tendency to engage in such behaviour is related to a driver’s ability to resist peer pressure. However, to our knowledge, the relationship between risky driving behaviour and the ability to resist peer pressure among young drivers in China remains unexplored. This study aimed to translate and adapt the Resistance to Peer Influence (RPI) Scale to Chinese drivers and examine whether RPI can moderate the influence of peer pressure on risky driving behaviours. A total of 269 drivers were recruited for this research. These drivers completed the Safe Driving Climate among Friends (SDCaF) Scale, the Peer Pressure on Risky Driving Scale (PPRDS), the RPI Scale and a scale that measured risky driving behaviours. The Chinese version of the RPI scale consists of 10 items and has acceptable reliability. The significant correlations observed among the RPI scale, the SDCaF, the PPRDS and risky driving behaviour indicate that the convergent and discriminant validity of the RPI scale is satisfactory. RPI, friend pressure and shared commitment explained 16.5% of the variance in risky driving behaviour, whereas RPI and risk-encouraging direct peer pressure explained 15.8% of this variance. RPI moderated the relationship between shared commitment and risky driving behaviour. Lower levels of shared commitment combined with low RPI were linked to higher levels of risky driving. RPI also moderated the relationship between risk-encouraging direct peer pressure and risky driving behaviour. Higher levels of risk-encouraging peer pressure were associated with more risky driving regardless of the level of RPI. RPI has acceptable internal consistency and validity and has the potential to serve as a valid tool for assessing and training young drivers in China.

Advanced Driver Assistance Systems (ADAS) have become an integral part of modern vehicles, with the potential to significantly enhance safety on the road. ADAS technology involves the use of sensors, algorithms, and software to assist drivers and provide them with real-time information about their surroundings, traffic conditions, and potential hazards. Sensors utilized for object tracking and environmental detection, particularly those based on laser, radar, and camera technologies, are fundamental to the functional performance of ADAS. Within automotive applications, the majority of camera systems are equipped with wide-angle or fish-eye lenses, both of which are known to introduce substantial optical distortion. To ensure accurate environmental perception, particularly in the context of geometric feature recognition and distance estimation, such cameras require meticulous calibration. Therefore, this paper describes a case study concerning cameras used in vision-based ADAS, as well as the most frequently used calibrating techniques. It describes the fundamentals of camera calibration and implementation, with results given for different lenses and distortion models. By engaging with this article, readers will gain a comprehensive understanding of the technological foundations, functional principles, and practical challenges associated with camera-based ADAS that need to be addressed to ensure its safe and effective operation on the road. The article serves as a technical reference that not only enhances the reader’s theoretical knowledge but also informs practical decision-making in the development of safe and effective driver assistance systems.

With the rapid adoption of automated driving systems, ensuring safe and efficient driver takeover has become a crucial challenge for road safety. This study introduces a novel psychological framework for understanding and predicting takeover behavior in conditionally automated vehicles, leveraging an extended Theory of Planned Behavior (TPB) model enriched by real-world driver experience. Drawing on survey data from 385 automated driving system users recruited in Shaoguan City, China, through face-to-face questionnaire administration covering various ADS types (ACC, lane-keeping, automatic parking), we demonstrate that driver attitudes, perceived behavioral control, and subjective norms are significant determinants of takeover intention, collectively explaining nearly half of its variance (R2 = 48.7%). Importantly, our analysis uncovers that both intention and perceived behavioral control have robust, direct effects on actual takeover behavior. Crucially, this work is among the first to reveal that individual user characteristics—such as driving experience and ADS (automated driving system) usage frequency—substantially moderate these psychological pathways: experienced or frequent users rely more on perceived control and attitude, while less experienced drivers are more susceptible to social influences. By advancing a multi-dimensional psychological model that integrates personal, social, and experiential moderators, our findings deliver actionable insights for the design of adaptive human–machine interfaces, tailored driver training, and targeted safety interventions in the context of automated driving. Using structural equation modeling with maximum likelihood estimation (χ2/df = 2.25, CFI = 0.941, RMSEA = 0.057), this psychological approach complements traditional engineering models by revealing that takeover behavior variance is explained at 58.3%.

A multi-class driver behavior dataset for real-time detection and road safety enhancement.

Gating Syn-to-Real Knowledge for Pedestrian Crossing Prediction in Safe Driving

Reliability-Driven Fuzzy Rule Inference for Critical Safe Driving in Vehicle Tracking Control

The SGSM Framework: Enabling the Specification and Monitor Synthesis of Safe Driving Properties through Scene Graphs

Psychometrics and risk perceptions influence motorists' travel modes. Driver conduct is crucial to calculating travel cost, distance, and time. Psychometrics, risk perception, and travel behaviour of Lagos State motorists were examined in this study. Using the survey approach with the multiphase sampling techniques which comprised of quota and convenience, data were gathered through a structured questionnaire from the selected registered motorists. The sample size of two hundred and eighty-nine (289) was estimated. The data analytical techniques employed were descriptive statistics and Friedman rank test. The findings revealed rank test of psychometrics, risk perceptions, and travel behaviour of selected motorists in Lagos State. It also established that risk perceptions metrics such as roads’ vulnerabilities, choices, trusts, and fears be quantified while the motorists are on the Lagos roads. Conclusively, the study ensure that motorists are conscious of their travel behaviour in terms of travel quality, costs, distance, modes, and times for effective transport systems. The study recommended that traffic authorities are advised to use psychometrics to detect high-risk drivers and train them for their psychological characteristics. The government should reward safe drivers, as indicated by psychometric evaluations, with public acknowledgement, lower insurance premiums, or other incentives.

Rewarding Safe Driving: An Arduino-Powered System for Real-Time Driver Behavior Monitoring in Smart Auto Insurance

Intelligent transportation systems rely greatly on their capacity to identify and recognize traffic signs. Traffic signs are important for modern transportation systems because they keep roads safe and help drivers, especially in areas like Ethiopia where sign designs are unique and diversified. In this study, we presented a convolutional neural network (CNN)–based model for Ethiopian traffic sign recognition (ETSR) purposes. We applied the transfer learning technique to fine‐tune the pretrained models, namely, MobileNet, VGG16, and ResNet50. Both training and model hyperparameters are fine‐tuned, and the 11,000 Ethiopian traffic sign images, which have 156 unique signs, are leveraged to build the new models. Optimizer, batch size, learning rate, and epoch are among the tuned training hyperparameters. All convolutional bases (learning layers) are trained using new weights. We built the fully connected layer of each model from two batch normalization layers and two dense layers. The output layer of the models has 156 units (neurons) with a softmax activation layer. The performances of newly developed models are rigorously compared with those of the base (pretrained) models. The best model was also selected after rigorous experiments. Based on the experiment, we achieved testing accuracy of 97.91%, 93.45%, and 80.18% for fine‐tuned VGG16, MobileNet, and ResNet50, respectively.

妨害安全驾驶罪入罪路径限缩分析

Legal Characterization and Practical Application of the Offense of Obstructing Safe Driving

Integrated Intelligent Control Systems for Eco and Safe Driving in Autonomous Vehicles

Safety Riding is a way of driving that is safe and comfortable for a rider against other riders. Based on data from the Pekanbaru Police Traffic Unit in the last 3 years (2018-2020) there have been 576 traffic accidents due to safety riding issues such as not using personal protective equipment, not obeying traffic signs, driving at high speed, and driving in underage. motor vehicle. good traffic awareness and safe driving behavior are needed for the community, especially among teenagers, such as in Pekanbaru. This study was to determine the relationship between knowledge level and safe driving behavior (safety riding) in class XII students at SMK Muhammadiyah 1 Pekanbaru. This research is a quantitative research using analytic methods using a cross sectional approach which will take a sample of 216 students by using the two proportions difference hypothesis test. Analysis with chi square = 0,05. Data collection used a questionnaire with ethical number No.10.169.B/KEPK- FKMUMJ/VI/2023. Related variables include knowledge variable with p-value 0.021, attitude variable with p-value 0.010, gender variable with p-value 0.006, SIM ownership variable with p-value 0.044, driving period variable with p-value 0.005, distance variable travel with a p-value of 0.009. while the variables that have no relationship include, the age variable with a p-value of 0.864. The level of knowledge related to safe driving behavior is knowledge, attitude, gender, driving license ownership, driving period, and mileage. For Pekanbaru Police, they should pay more attention to and provide education to schools about driving safety from an early age so that it is hoped that this will reduce the risk of accidents in young adults because they have received knowledge from an early age.

This study aimed to determine whether the Safe Driving Program (SDP), a behaviour change program ordered by the courts operating in Victoria, Australia, was successful in reducing the offending and crashes by hoon drivers. A sample of 3,324 hoon drivers who completed the SDP were compared with 1,063 hoon drivers who were ordered to complete the program but failed to do so. These two groups were also compared with a third group consisting of 30,678 hoon drivers who, for various reasons, had not been ordered to complete the SDP. Longitudinal group comparisons were made regarding overall, hoon and serious offending, as well as the number of crashes, fatalities and serious injuries and the proportion of offenders receiving bans and vehicle impoundments. Generalised Estimating Equations were used for this purpose, providing estimates of group differences. It was found that statistically significant benefits were obtained through the placement of SDP orders. However, differences between the offenders that completed their SDP order and those that failed to complete their SDP order were not always as expected. Reasons for this are explained and implications for the program, policies and penalties are discussed. No changes are recommended for current impoundment and SDP arrangements.