Detecting motorcycle helmet use with deep learning

The continuous motorization of traffic has led to a sustained increase in the global number of road related fatalities and injuries. To counter this, governments are focusing on enforcing safe and law-abiding behavior in traffic. However, especially in developing countries where the motorcycle is the main form of transportation, there is a lack of comprehensive data on the safety-critical behavioral metric of motorcycle helmet use. This lack of data prohibits targeted enforcement and education campaigns which are crucial for injury prevention. Hence, we have developed an algorithm for the automated registration of motorcycle helmet usage from video data, using a deep learning approach. Based on 91,000 annotated frames of video data, collected at multiple observation sites in 7 cities across the country of Myanmar, we trained our algorithm to detect active motorcycles, the number and position of riders on the motorcycle, as well as their helmet use. An analysis of the algorithm's accuracy on an annotated test data set, and a comparison to available human-registered helmet use data reveals a high accuracy of our approach. Our algorithm registers motorcycle helmet use rates with an accuracy of −4.4% and +2.1% in comparison to a human observer, with minimal training for individual observation sites. Without observation site specific training, the accuracy of helmet use detection decreases slightly, depending on a number of factors. Our approach can be implemented in existing roadside traffic surveillance infrastructure and can facilitate targeted data-driven injury prevention campaigns with real-time speed. Implications of the proposed method, as well as measures that can further improve detection accuracy are discussed

How speed and visibility influence preferred headway distances in highly automated driving

While the introduction of highly automated vehicles promises lower accident numbers, a main requirement for wide use of these vehicles will be the acceptance by drivers. In this study a crucial variable for the acceptance of highly automated vehicles, the vehicle to vehicle distance expressed in time headway, was researched in a driving simulator. Research has shown that time headway distances, perceived as comfortable in self-driving and assisted driving with adaptive cruise control, remain constant over a range of different speeds. This study aims to test these findings for highly automated driving. Since time headway is perceived visually, the driving situation was varied to investigate the influence of visibility on the subjective comfort of the driver in a highly automated driving situation. In a within-subject design, drivers followed a passenger car in clear weather conditions, the same passenger car in fog which occluded parts of the traffic environment, as well as a truck that occluded the lane ahead, also in clear weather condition. Subjective comfort of drivers in each condition was rated with a haptic rating lever. Results suggest that comfortable time headway following distances in highly automated driving are not constant over different speeds, but that these distances decrease with increasing speed. Reduced visibility generally led to a shift in comfortable following distances towards larger headways. These results have implications for the introduction of highly automated vehicles and their time headway adjustments, which will need to be adaptive to speed and visibility in the road environment

The influence of time headway on subjective driver states in adaptive cruise control

There is no agreement on the relation between driving parameters and drivers’ subjective states. A linear as well as a threshold relationship for different subjective variables and driving parameters has been put forward. In this study we investigate the relationship between time headway and the ratings of risk, task difficulty, effort, and comfort. Knowledge about this interrelation may advance the development of adaptive cruise control and autonomous driving and can add to the discussion about driver behavior models. An earlier study (Lewis-Evans, De Waard, & Brookhuis, 2010) found a threshold effect for drivers’ ratings of subjective variables for time headways between 0.5 and 4.0 s at a speed of 50 km/h. This study aims to replicate the threshold effect and to expand the findings to time headways at different speeds. A new measure for criticality was added as a categorical variable, indicating the controllability of a driving situation to give indications for the appliance of time headway in adaptive cruise control systems. Participants drove 24 short routes in a driving simulator with predefined speed and time headway to a leading vehicle. Time headway was varied eightfold (0.5–4 s in 0.5 s increments) and speed was varied threefold (50, 100, 150 km/h). A threshold effect for the ratings of risk, task difficulty, effort, and comfort was found for all three different speeds. Criticality proved to be a useful variable in assessing the preferred time headway of drivers

Introducing a multivariate model for predicting driving performance: The role of driving anger and personal characteristics

Introduction: Maladaptive driving is an important source of self-inflicted accidents and this driving style could include high speeds, speeding violations, and poor lateral control of the vehicle. The literature suggests that certain groups of drivers, such as novice drivers, males, highly motivated drivers, and those who frequently experience anger in traffic, tend to exhibit more maladaptive driving patterns compared to other drivers. Remarkably, no coherent framework is currently available to describe the relationships and distinct influences of these factors. Method: We conducted two studies with the aim of creating a multivariate model that combines the aforementioned factors, describes their relationships, and predicts driving performance more precisely. The studies employed different techniques to elicit emotion and different tracks designed to explore the driving behaviors of participants in potentially anger-provoking situations. Study 1 induced emotions with short film clips. Study 2 confronted the participants with potentially anger-inducing traffic situations during the simulated drive. Results: In both studies, participants who experienced high levels of anger drove faster and exhibited greater longitudinal and lateral acceleration. Furthermore, multiple linear regressions and path-models revealed that highly motivated male drivers displayed the same behavior independent of their emotional state. The results indicate that anger and specific risk characteristics lead to maladaptive changes in important driving parameters and that drivers with these specific risk factors are prone to experience more anger while driving, which further worsens their driving performance. Driver trainings and anger management courses will profit from these findings because they help to improve the validity of assessments of anger related driving behavior

Adjustable automation and manoeuvre control in automated driving

Current implementations of automated driving rely on the driver to monitor the vehicle and be ready to assume control in situations that the automation cannot successfully manage. However, research has shown that drivers are not able to monitor an automated vehicle for longer periods of time, as the monotonous monitoring task leads to attention reallocation or fatigue. Driver involvement in the automated driving task promises to counter this effect. The authors researched how the implementation of a haptic human–vehicle interface, which allows the driver to adjust driving parameters and initiate manoeuvres, influences the subjective experience of drivers in automated vehicles. In a simulator study, they varied the level of control that drivers have over the vehicle, between manual driving, automated driving without the possibility to adjust the automation, as well as automated driving with the possibility to initiate manoeuvres and adjust driving parameters of the vehicle. Results show that drivers have a higher level of perceived control and perceived level of responsibility when they have the ability to interact with the automated vehicle through the haptic interface. The authors conclude that the possibility to interact with automated vehicles can be beneficial for driver experience and safety

The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception

Robots that are designed to work in close proximity to humans are required to move and act in a way that ensures social acceptance by their users. Hence, a robot's proximal behavior toward a human is a main concern, especially in human-robot interaction that relies on relatively close proximity. This study investigated how the distance and lateral offset of “Follow Me” robots influences how they are perceived by humans. To this end, a Follow Me robot was built and tested in a user study for a number of subjective variables. A total of 18 participants interacted with the robot, with the robot's lateral offset and distance varied in a within-subject design. After each interaction, participants were asked to rate the movement of the robot on the dimensions of comfort, expectancy conformity, human likeness, safety, trust, and unobtrusiveness. Results show that users generally prefer robot following distances in the social space, without a lateral offset. However, we found a main influence of affinity for technology, as those participants with a high affinity for technology preferred closer following distances than participants with low affinity for technology. The results of this study show the importance of user-adaptiveness in human-robot-interaction.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

The exact determination of subjective risk and comfort thresholds in car following

In this study the location of vehicle to vehicle distance thresholds for self-reported subjective risk and comfort was researched. Participants were presented with ascending and descending time headway sequences in a driving simulator. This so called method of limits of ascending and descending stimuli (Gouy, Diels, Reed, Stevens, & Burnett, 2012) was refined to efficiently determine individual thresholds for stable time headways with a granularity of 0.1 seconds. Time headway thresholds were researched for 50, 100, and 150 km/h in a city, rural, and highway setting. Furthermore, thresholds for self-driving (level 0 automation: NHTSA, 2013) were compared with thresholds for the experience of subjective risk and comfort in assisted driving, similar to adaptive cruise control (level 1 automation). Results show that preferred individual time headways vary between subjects. Within subjects however, time headway thresholds do not significantly differ for different speeds. Furthermore we found that there was no significant difference between time headways of self-driving and distance-assisted driving. The relevance of these findings for the development of adaptive cruise control systems, autonomous driving and driver behavior modelling is discussed

Helmet use detection of tracked motorcycles using CNN-based multi-task learning.

Automated detection of motorcycle helmet use through video surveillance can facilitate efficient education and enforcement campaigns that increase road safety. However, existing detection approaches have a number of shortcomings, such as the inabilities to track individual motorcycles through multiple frames, or to distinguish drivers from passengers in helmet use. Furthermore, datasets used to develop approaches are limited in terms of traffic environments and traffic density variations. In this paper, we propose a CNN-based multi-task learning (MTL) method for identifying and tracking individual motorcycles, and register rider specific helmet use. We further release the HELMET dataset, which includes 91,000 annotated frames of 10,006 individual motorcycles from 12 observation sites in Myanmar. Along with the dataset, we introduce an evaluation metric for helmet use and rider detection accuracy, which can be used as a benchmark for evaluating future detection approaches. We show that the use of MTL for concurrent visual similarity learning and helmet use classification improves the efficiency of our approach compared to earlier studies, allowing a processing speed of more than 8 FPS on consumer hardware, and a weighted average F-measure of 67.3% for detecting the number of riders and helmet use of tracked motorcycles. Our work demonstrates the capability of deep learning as a highly accurate and resource efficient approach to collect critical road safety related data

Calibration-free gaze interfaces based on linear smooth pursuit

Since smooth pursuit eye movements can be used without calibration in spontaneous gaze interaction, the intuitiveness of the gaze interface design has been a topic of great interest in the human-computer interaction field. However, since most related research focuses on curved smooth-pursuit trajectories, the design issues of linear trajectories are poorly understood. Hence, this study evaluated the user performance of gaze interfaces based on linear smooth pursuit eye movements. We conducted an experiment to investigate how the number of objects (6, 8, 10, 12, or 15) and object moving speed (7.73 ˚/s vs. 12.89 ˚/s) affect the user performance in a gaze-based interface. Results show that the number and speed of the displayed objects influence users’ performance with the interface. The number of objects significantly affected the correct and false detection rates when selecting objects in the display. Participants’ performance was highest on interfaces containing 6 and 8 objects and decreased for interfaces with 10, 12, and 15 objects. Detection rates and orientation error were significantly influenced by the moving speed of displayed objects. Faster moving speed (12.89 ˚/s) resulted in higher detection rates and smaller orientation error compared to slower moving speeds (7.73 ˚/s). Our findings can help to enable a calibration-free accessible interaction with gaze interfaces.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Patterns of motorcycle helmet use – a naturalistic observation study in Myanmar

Developing countries are subject to increased motorization, particularly in the number of motorcycles. As helmet use is critical to the safety of motorcycle riders, the goal of this study was to identify observable patterns of helmet use, which allow a more accurate assessment of helmet use in developing countries. In a video based observation study, 124,784 motorcycle riders were observed at seven observation sites throughout Myanmar. Recorded videos were coded for helmet use, number of riders on the motorcycle, rider position, gender, and time of day. Generally, motorcycle helmet use in Myanmar was found to be low with only 51.5% percent of riders wearing a helmet. Helmet use was highest for drivers (68.1%) and decreased for every additional passenger. It was lowest for children standing on the floorboard of the motorcycle (11.3%). During the day, helmet use followed a unimodal distribution, with the highest use observed during the late morning and lowest use observed in the early morning and late afternoon. Helmet use varied significantly between observation sites, ranging from 74.8% in Mandalay to 26.9% in Pakokku. In Mandalay, female riders had a higher helmet use than male riders, and helmet use decreased drastically on a national holiday in the city. Helmet use of motorcycle riders in Myanmar follows distinct patterns. Knowledge of these patterns can be used to design more precise helmet use evaluations and guide traffic law policy and police enforcement measures. Video based observation proved to be an efficient tool to collect helmet use data