Detection of the toughest: Pedestrian injury risk as a smooth function of age

Objective: Though it is common to refer to age-specific groups (e.g., children, adults, elderly), smooth trends conditional on age are mainly ignored in the literature. The present study examines the pedestrian injury risk in full-frontal pedestrian-to–passenger car accidents and incorporates age—in addition to collision speed and injury severity—as a plug-in parameter.

Methods: Recent work introduced a model for pedestrian injury risk functions using explicit formulae with easily interpretable model parameters. This model is expanded by pedestrian age as another model parameter. Using the German In-Depth Accident Study (GIDAS) to obtain age-specific risk proportions, the model parameters are fitted to the raw data and then smoothed by broken-line regression.

Results: The approach supplies explicit probabilities for pedestrian injury risk conditional on pedestrian age, collision speed, and injury severity under investigation. All results yield consistency to each other in the sense that risks for more severe injuries are less probable than those for less severe injuries. As a side product, the approach indicates specific ages at which the risk behavior fundamentally changes. These threshold values can be interpreted as the most robust ages for pedestrians.

Conclusions: The obtained age-wise risk functions can be aggregated and adapted to any population. The presented approach is formulated in such general terms that in can be directly used for other data sets or additional parameters; for example, the pedestrian's sex. Thus far, no other study using age as a plug-in parameter can be found.     

Quantifying Vision Zero: Crash avoidance in rural and motorway accident scenarios by combination of ACC,AEB, and LKS projected to German accident occurrence

Abstract

Objective: The Vision Zero initiative pursues the goal of eliminating all traffic fatalities and severe injuries. Today’s advanced driver assistance systems (ADAS) are an important part of the strategy toward Vision Zero. In Germany in 2018 more than 26,000 people were killed or severely injured by traffic accidents on motorways and rural roads due to road accidents. Focusing on collision avoidance, a simulative evaluation can be the key to estimating the performance of state-of-the-art ADAS and identifying resulting potentials for system improvements and future systems.

This project deals with the effectiveness assessment of a combination of ADAS for longitudinal and lateral intervention based on German accident data. Considered systems are adaptive cruise control (ACC), autonomous emergency braking (AEB), and lane keeping support (LKS).

Methods: As an approach for benefit estimation of ADAS, the method of prospective effectiveness assessment is applied. Using the software rateEFFECT, a closed-loop simulation is performed on accident scenario data from the German In-Depth Accident Study (GIDAS) precrash matrix (PCM). To enable projection of results, the simulative assessment is amended with detailed single case studies of all treated cases without PCM data.

Results: Three categories among today’s accidents on German rural roads and motorways are reported in this study: Green, grey, and white spots.

Green spots identify accidents that can be avoided by state-of-the-art ADAS ACC, AEB, and LKS. Grey spots contain scenarios that require minor system modifications, such as reducing the activation speed or increasing the steering torque. Scenarios in the white category cannot be addressed by state-of-the-art ADAS. Thus, which situations demand future systems are shown. The proportions of green, grey, and white spots are determined related to the considered data set and projected to the entire GIDAS.

Conclusions: This article describes a systematic approach for assessing the effectiveness of ADAS using GIDAS PCM data to be able to project results to Germany. The closed-loop simulation run in rateEFFECT covers ACC, AEB, and LKS as well as relevant sensors for environment recognition and actuators for longitudinal and lateral vehicle control.

Identification of green spots evaluates safety benefits of state-of-the-art level 0–2 functions as a baseline for further system improvements to address grey spots. Knowing which accidents could be avoided by standard ADAS helps focus the evolution of future driving functions on white spots and thus aim for Vision Zero.     

General Evaluation of Risk Associated With the Use of Pesticides and Other Chemical Substances on Animal Breeding and Plant Production Farms

Abstract

A general characteristic of chemical risk on plant production farms in Poland is presented. The paper describes risk associated with the natural occurrence of chemical substances (such as ammonium and hydrogen sulfide) in the process of animai breeding and risk connected with the use of artificial fertilizers and pesticides. Pesticides are briefly described taking into consideration toxicity classes and the toxic effect of individual compounds. Exposure to pesticides is presented for individual methods and related activities. Finally, the author discusses pesticide risk on fruit-growing farms and in greenhouses.     

Safety assessment of automated vehicles: how to determine whether we have collected enough field data?

Abstract

Objective: The amount of collected field data from naturalistic driving studies is quickly increasing. The data are used for, among others, developing automated driving technologies (such as crash avoidance systems), studying driver interaction with such technologies, and gaining insights into the variety of scenarios in real-world traffic. Because data collection is time consuming and requires high investments and resources, questions like “Do we have enough data?,” “How much more information can we gain when obtaining more data?,” and “How far are we from obtaining completeness?” are highly relevant. In fact, deducing safety claims based on collected data—for example, through testing scenarios based on collected data—requires knowledge about the degree of completeness of the data used. We propose a method for quantifying the completeness of the so-called activities in a data set. This enables us to partly answer the aforementioned questions.

Method: In this article, the (traffic) data are interpreted as a sequence of different so-called scenarios that can be grouped into a finite set of scenario classes. The building blocks of scenarios are the activities. For every activity, there exists a parameterization that encodes all information in the data of each recorded activity. For each type of activity, we estimate a probability density function (pdf) of the associated parameters. Our proposed method quantifies the degree of completeness of a data set using the estimated pdfs.

Results: To illustrate the proposed method, 2 different case studies are presented. First, a case study with an artificial data set, of which the underlying pdfs are known, is carried out to illustrate that the proposed method correctly quantifies the completeness of the activities. Next, a case study with real-world data is performed to quantify the degree of completeness of the acquired data for which the true pdfs are unknown.

Conclusion: The presented case studies illustrate that the proposed method is able to quantify the degree of completeness of a small set of field data and can be used to deduce whether sufficient data have been collected for the purpose of the field study. Future work will focus on applying the proposed method to larger data sets. The proposed method will be used to evaluate the level of completeness of the data collection on Singaporean roads, aimed at defining relevant test cases for the autonomous vehicle road approval procedure that is being developed in Singapore.     

Enzymatic conversion of newspaper and office paper to fermentable sugars

Two types of waste paper materials, newspaper and office paper, were evaluated for their potential to be used as renewable feedstock for the production of fermentable sugars via the enzymatic hydrolysis of their cellulose fractions. The effects of four factors (hydrolysis time, enzyme loading, surfactant addition and phosphoric acid pretreatment) on the extent of sugar yield were assessed and quantified by using a methodical approach based on response surface methodology. The statistical experimental design used in this study requires fewer experimental runs compared to some commonly used experimental designs. In the newspaper hydrolysis case, response surface plots revealed that the degree of sugar release increased with an increase in hydrolysis time but it was hardly affected by the enzyme loading and acid pretreatment factors. The surfactant addition factor exhibited a positive effect when the enzyme loading level was relatively low. With office paper as the substrate, three of the four factors (hydrolysis time, enzyme loading and acid pretreatment) exhibited positive effects on the extent of sugar release. At local optimum conditions, the maximum sugar yield from office paper was found to be 0.82 g of reducing sugars per gram of paper, which was about 4.8 times higher than the maximum sugar release from the newspaper substrate.     

Role of naphthenic acid contaminants in the removal of p-xylene from synthetic produced water by MEUF

The high demand for water in oil sands operations in addition to the shortage of fresh water resources mandate continuous search for reliable and cost effective water treatment technologies. Micellar-enhanced ultrafiltration (MEUF) is potentially applicable to produced water treatment. MEUF is, however, susceptible to membrane plugging and back contamination, especially at high surfactant dosages. Recent investigations showed that addition of small amounts of a non-ionic surfactant to an ionic surfactant solution reduces the critical micellar concentration (CMC) of the mixed surfactant solution, and consequently reduces the surfactant dosage required to achieve certain removal of contaminants using MEUF. In the present study a naphthenic acid, octanoic acid, typically existing in produced water is shown to play a similar role as the non-ionic surfactant when added to the cationic surfactant cetylpyridinium chloride (CPC). Cross-flow MEUF using three different molecular weight cutoffs (MWCOs) of polyacrylonitrile (PAN) hollow fiber membranes was used to treat synthetic produced water containing p-xylene. The mixed CPC/octanoic acid solution removed comparable percentages of p-xylene contaminant at much lower concentrations of CPC. Decreasing CPC concentration in the feed resulted in less fouling and higher permeate flux and reduced back contamination. This, in turn, enhanced the performance of MEUF.     

The Real-World Safety Potential of Connected Vehicle Technology

Objective: This article estimates the safety potential of a current commercially available connected vehicle technology in real-world crashes.

Method: Data from the Centre for Automotive Safety Research's at-scene in-depth crash investigations in South Australia were used to simulate the circumstances of real-world crashes. A total of 89 crashes were selected for inclusion in the study. The crashes were selected as representative of the most prevalent crash types for injury or fatal crashes and had potential to be mitigated by connected vehicle technology. The trajectory, speeds, braking, and impact configuration of the selected in-depth cases were replicated in a software package and converted to a file format allowing “replay” of the scenario in real time as input to 2 Cohda Wireless MK2 onboard units. The Cohda Wireless onboard units are a mature connected vehicle technology that has been used in both the German simTD field trial and the U.S. Department of Transport's Safety Pilot project and have been tuned for low false alarm rates when used in the real world. The crash replay was achieved by replacing each of the onboard unit Global Positioning System (GPS) inputs with the simulated data of each of the involved vehicles. The time at which the Cohda Wireless threat detection software issued an elevated warning was used to calculate a new impact speed using 3 different reaction scenarios and 2 levels of braking.

Results: It was found that between 37 and 86% of the simulated crashes could be avoided, with highest percentage due a fully autonomous system braking at 0.7 g. The same system also reduced the impact speed relative to the actual crash in all cases. Even when a human reaction time of 1.2 s and moderate braking of 0.4 g was assumed, the impact speed was reduced in 78% of the crashes. Crash types that proved difficult for the threat detection engine were head-on crashes where the approach angle was low and right turn–opposite crashes.

Conclusions: These results indicate that connected vehicle technology can be greatly beneficial in real-world crash scenarios and that this benefit would be maximized by having the vehicle intervene autonomously with heavy braking. The crash types that proved difficult for the connected vehicle technology could be better addressed if controller area network (CAN) information is available, such as steering wheel angle, so that driver intent can be inferred sooner. More accurate positioning in the real world (e.g., combining satellite positioning and accelerometer data) would allow the technology to be more effective for near-collinear head-on and rear-end crashes, because the low approach angles that are common in such crashes are currently ignored in order to minimize false alarms due to positioning uncertainty.     

Comparison of self-report and objective measures of driving behavior and road safety: A systematic review

IntroductionThis research systematically reviewed the existing literature in regards to studies which have used both self-report and objective measures of driving behavior. The objective of the current review was to evaluate disparities or similarities between self-report and objective measures of driving behavior.MethodsSearches were undertaken in the following electronic databases, PsycINFO, PubMed, and Scopus, for peer-reviewed full-text articles that (1) focused on road safety, and (2) compared both subjective and objective measures of driving performance or driver safety. A total of 22,728 articles were identified, with 19 articles, comprising 20 studies, included as part of the review.ResultsThe research reported herein suggested that for some behaviors (e.g., driving in stressful situations) there were similarities between self-report and objective measures while for other behaviors (e.g., sleepiness and vigilance states) there were differences between these measurement techniques. In addition, findings from some studies suggested that in-vehicle devices may be a valid measurement tool to assess driving exposure in older drivers.ConclusionsFurther research is needed to examine the correspondence between self-report and objective measures of driving behavior. In particular, there is a need to increase the number of studies which compare “like with like” as it is difficult to draw comparisons when there are variations in measurement tools used.Practical applicationsIncorporating a range of objective and self-report measurements tools in research would help to ensure that the methods used offer the most reliable measures of assessing on-road behaviors.     

Effect of pavement condition and geometrics at signalised intersections on casualty crashes

IntroductionThis study investigated the effects of pavement surface condition and other control factors on casualty crashes at signalized intersections. It involved conducting a before and after study for road surface condition and situational factors. It also included assessing the effects of geometric characteristics on safety performance of signalized intersections post resurfacing to control for the effect of pavement surface condition. Pavement surface condition included roughness, rutting, and skid resistance. The control factors included traffic volume, light and surface moisture condition, and speed limit. The geometric characteristics included approach width, number of lanes, intersection depth, presence of median, presence of shared lane, and presence of bus stop.MethodTo account for the repeated observations of the effect of light and surface moisture conditions in four occasions (day-dry, day-wet, night-dry and night-wet) Generalized Estimating Equation (GEE) with Negative Binomial (NB) and log link function was applied. For each signalized intersection in the sample, condition data are collected for the year before and after the year of surface treatment. Crash data, however, are collected for a minimum of three and maximum of five years before and after treatment years.ResultsThe results show that before treatment, light condition, road surface moisture condition, and skid resistance interaction with traffic volume are the significant contributors to crash occurrence. For after treatment; light condition, road surface moisture condition, their interaction product, and roughness interaction with light condition, surface moisture condition, and traffic volume are the significant contributors. The geometric variables that were found to have significant effects on crash frequency post resurfacing were approach width interactions with presence of shared lane, bus stop, or median.ConclusionsThe findings confirm that resurfacing is significant in reducing crash frequency and severity levels.Practical Applications: The study findings would help for better understanding of how geometric characteristics can be improved to reduce crash occurrence.     

Combining Coordination of Motion Actuators with Driver Steering Interaction

Objective: A new method is suggested for coordination of vehicle motion actuators; where driver feedback and capabilities become natural elements in the prioritization.

Methods: The method is using a weighted least squares control allocation formulation, where driver characteristics can be added as virtual force constraints. The approach is in particular suitable for heavy commercial vehicles that in general are over actuated. The method is applied, in a specific use case, by running a simulation of a truck applying automatic braking on a split friction surface. Here the required driver steering angle, to maintain the intended direction, is limited by a constant threshold. This constant is automatically accounted for when balancing actuator usage in the method.

Results: Simulation results show that the actual required driver steering angle can be expected to match the set constant well. Furthermore, the stopping distance is very much affected by this set capability of the driver to handle the lateral disturbance, as expected.

Conclusion: In general the capability of the driver to handle disturbances should be estimated in real-time, considering driver mental state. By using the method it will then be possible to estimate e.g. stopping distance implied from this. The setup has the potential of even shortening the stopping distance, when the driver is estimated as active, this compared to currently available systems. The approach is feasible for real-time applications and requires only measurable vehicle quantities for parameterization. Examples of other suitable applications in scope of the method would be electronic stability control, lateral stability control at launch and optimal cornering arbitration.     

A framework for definition of logical scenarios for safety assurance of automated driving

Abstract

Objective: In order to introduce automated vehicles on public roads, it is necessary to ensure that these vehicles are safe to operate in traffic. One challenge is to prove that all physically possible variations of situations can be handled safely within the operational design domain of the vehicle. A promising approach to handling the set of possible situations is to identify a manageable number of logical scenarios, which provide an abstraction for object properties and behavior within the situations. These can then be transferred into concrete scenarios defining all parameters necessary to reproduce the situation in different test environments.

Methods: This article proposes a framework for defining safety-relevant scenarios based on the potential collision between the subject vehicle and a challenging object, which forces the subject vehicle to depart from its planned course of action to avoid a collision. This allows defining only safety-relevant scenarios, which can directly be related to accident classification. The first criterion for defining a scenario is the area of the subject vehicle with which the object would collide. As a second criterion, 8 different positions around the subject vehicle are considered. To account for other relevant objects in the scenario, factors that influence the challenge for the subject vehicle can be added to the scenario. These are grouped as action constraints, dynamic occlusions, and causal chains.

Results: By applying the proposed systematics, a catalog of base scenarios for a vehicle traveling on controlled-access highways has been generated, which can directly be linked to parameters in accident classification. The catalog serves as a basis for scenario classification within the PEGASUS project.

Conclusions: Defining a limited number of safety-relevant scenarios helps to realize a systematic safety assurance process for automated vehicles. Scenarios are defined based on the point of the potential collision of a challenging object with the subject vehicle and its initial position. This approach allows defining scenarios for different environments and different driving states of the subject vehicle using the same mechanisms. A next step is the generation of logical scenarios for other driving states of the subject vehicle and for other traffic environments.     

Test procedure for evaluating the human–machine interface of vehicles with automated driving systems

Abstract

Objective: The human–machine interface (HMI) is a crucial part of every automated driving system (ADS). In the near future, it is likely that—depending on the operational design domain (ODD)—different levels of automation will be available within the same vehicle. The capabilities of a given automation level as well as the operator’s responsibilities must be communicated in an appropriate way. To date, however, there are no agreed-upon evaluation methods that can be used by human factors practitioners as well as researchers to test this.

Methods: We developed an iterative test procedure that can be applied during the product development cycle of ADS. The test procedure is specifically designed to evaluate whether minimum requirements as proposed in NHTSA’s automated vehicle policy are met.

Results: The proposed evaluation protocol includes (a) a method to identify relevant use cases for testing on the basis of all theoretically possible steady states and mode transitions of a given ADS; (b) an expert-based heuristic assessment to evaluate whether the HMI complies with applicable norms, standards, and best practices; and (c) an empirical evaluation of ADS HMIs using a standardized design for user studies and performance metrics.

Conclusions: Each can be used as a stand-alone method or in combination to generate objective, reliable, and valid evaluations of HMIs, focusing on whether they meet minimum requirements. However, we also emphasize that other evaluation aspects such as controllability, misuse, and acceptance are not within the scope of the evaluation protocol.     

Are you an ambitious cyclist? Results of the cyclist profile questionnaire in Germany

Abstract

Objective: A number of studies have already grouped cyclists according to different aspects of their mobility behavior. This could be used e.g., to improve the bicycle infrastructure planning, to detect critical spots and, to reduce obstacles for cycling. This wide, preexisting, range of cyclist typologies usually concentrates on one or two influence factors and differs, content-wise, in both factors used, as well as, methodically. Based on existing cyclist typologies we extracted all possible influence factors to integrate them in one single questionnaire. The objective of this study, using an empirical, based approach, is to compare this typology of cyclists with existing ones, integrating all known influence factors of recent studies.

Methods: To address these issues, we conducted a Germany-wide online survey on cycling behavior, covering all relevant aspects we derived from both literature and especially, former cyclist typology studies including: social factors; the impact of environmental, individual; and route factors; as well as motives. The main goal was to identify distinct types of cyclists, and describe them as detailed as possible. The heterogeneous sample included a total of 10,294 responses.

Results: Using factor and cluster analyses, a multidimensional typology with four groups of cyclists was derived which were interpreted as: ambitious, functional, pragmatic, and passionate cyclists. In addition, socio-economic factors, cyclist’s motivation, and crash history were analyzed.

Conclusion: The results produced by grouping different characteristics of cyclists can lead to policy recommendations or communal bicycle traffic planning. Policy planners can estimate reactions of the different types on interventions and adjust their decisions which can serve to support already passionate cyclists or, encourage normally under-represented infrequent cyclists to cycle more. The extent of perceived safety plays here an important role in the classification, e.g., the handling of high-risk areas for crashes.     

A case study analysis to examine motorcycle crashes in Bogota,Colombia

IntroductionContributory factors to motorcycle crashes vary among populations depending on several aspects such as the users' profiles, the composition and density of traffic, and the infrastructure features. A better understanding of local motorcycle crashes can be reached in those places where a comprehensive analysis is performed. This paper presents the results obtained from a case study analysis of 400 police records of accidents involving motorcycles in Bogota.MethodTo achieve a deeper level of understanding of how these accidents occur, we propose a systemic approach that uses available crash data. The methodology is inspired by accident prototypical scenarios, a tool for analysis developed in France.ResultsWhen grouping cases we identified three categories: solo motorcycle accidents, motorcyclist and pedestrian accidents, and accidents involving a motorcycle and another vehicle. Within these categories we undertook in-depth analyses of 32 groups of accidents obtaining valuable information to better comprehend motorcyclists' road crashes in a local context. Recurrent contributory factors in the groups of accidents include: inexperienced motorcyclists, wide urban roads that incite speeding and risky overtaking maneuvers, flowing urban roads that encourage high speed and increased interaction between vehicles, and lack of infrastructure maintenance.Practical ApplicationsThe results obtained are a valuable asset to define measures that will be conveniently adapted to the group of accident on which we want to act. The methodology exposed in this paper is applicable to the study of road crashes that involve all types of actors, not only the motorcyclists, and in contexts different than those presented in Bogota.     

Assessing the risk of secondary crashes on highways

IntroductionThe occurrence of “secondary crashes” is one of the critical yet understudied highway safety issues. Induced by the primary crashes, the occurrence of secondary crashes does not only increase traffic delays but also the risk of inducing additional incidents. Many highway agencies are highly interested in the implementation of safety countermeasures to reduce this type of crashes. However, due to the limited understanding of the key contributing factors, they face a great challenge for determining the most appropriate countermeasures.MethodTo bridge this gap, this study makes important contributions to the existing literature of secondary incidents by developing a novel methodology to assess the risk of having secondary crashes on highways. The proposed methodology consists of two major components, namely: (a) accurate identification of secondary crashes and (b) statistically robust assessment of causal effects of contributing factors. The first component is concerned with the development of an improved identification approach for secondary accidents that relies on the rich traffic information obtained from traffic sensors. The second component of the proposed methodology is aimed at understanding the key mechanisms that are hypothesized to cause secondary crashes through the use of a modified logistic regression model that can efficiently deal with relatively rare events such as secondary incidents. The feasibility and improved performance of using the proposed methodology are tested using real-world crash and traffic flow data.ResultsThe risk of inducing secondary crashes after the occurrence of individual primary crashes under different circumstances is studied by employing the estimated regression model. Marginal effect of each factor on the risk of secondary crashes is also quantified and important contributing factors are highlighted and discussed.Practical applicationsMassive sensor data can be used to support the identification of secondary crashes. The occurrence mechanism of these secondary crashes can be investigate by the proposed model. Understanding the mechanism helps deploy appropriate countermeasures to mitigate or prevent the secondary crashes.     

Development and Validation of the Total HUman Model for Safety (THUMS) Toward Further Understanding of Occupant Injury Mechanisms in Precrash and During Crash

Objective: Active safety devices such as automatic emergency brake (AEB) and precrash seat belt have the potential to accomplish further reduction in the number of the fatalities due to automotive accidents. However, their effectiveness should be investigated by more accurate estimations of their interaction with human bodies. Computational human body models are suitable for investigation, especially considering muscular tone effects on occupant motions and injury outcomes. However, the conventional modeling approaches such as multibody models and detailed finite element (FE) models have advantages and disadvantages in computational costs and injury predictions considering muscular tone effects. The objective of this study is to develop and validate a human body FE model with whole body muscles, which can be used for the detailed investigation of interaction between human bodies and vehicular structures including some safety devices precrash and during a crash with relatively low computational costs.

Methods: In this study, we developed a human body FE model called THUMS (Total HUman Model for Safety) with a body size of 50th percentile adult male (AM50) and a sitting posture. The model has anatomical structures of bones, ligaments, muscles, brain, and internal organs. The total number of elements is 281,260, which would realize relatively low computational costs. Deformable material models were assigned to all body parts. The muscle–tendon complexes were modeled by truss elements with Hill-type muscle material and seat belt elements with tension-only material. The THUMS was validated against 35 series of cadaver or volunteer test data on frontal, lateral, and rear impacts. Model validations for 15 series of cadaver test data associated with frontal impacts are presented in this article. The THUMS with a vehicle sled model was applied to investigate effects of muscle activations on occupant kinematics and injury outcomes in specific frontal impact situations with AEB.

Results and Conclusions: In the validations using 5 series of cadaver test data, force–time curves predicted by the THUMS were quantitatively evaluated using correlation and analysis (CORA), which showed good or acceptable agreement with cadaver test data in most cases. The investigation of muscular effects showed that muscle activation levels and timing had significant effects on occupant kinematics and injury outcomes. Although further studies on accident injury reconstruction are needed, the THUMS has the potential for predictions of occupant kinematics and injury outcomes considering muscular tone effects with relatively low computational costs.     

Drunk driving detection based on classification of multivariate time series

IntroductionThis paper addresses the problem of detecting drunk driving based on classification of multivariate time series.MethodsFirst, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features.ResultsThe proposed approach achieved an accuracy of 80.0%.Conclusions and practical applicationsDrunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection.     

Evaluating an intervention to improve belt fit for adult occupants: Promoting positive beliefs

IntroductionSeat belt use provides significant public health benefit, however, most public awareness campaigns have generally focused on seat belt use rather than encouraging adults to improve seat belt fit with belt placement. This study provides an evaluation of a video-based intervention to improve adult belt fit assessing whether a video-based intervention can target beliefs and knowledge of seat belt placement and be perceived as relevant by the target audience.MethodAn intervention group of 29 adults (15 women and 14 men) and a comparison group of 99 adults (41 women and 47 men) participated.ResultsThe intervention group had significantly more favorable beliefs around belt fit than the comparison group related to Health Belief Model constructs of higher self-efficacy, greater benefits, and fewer barriers. The intervention group was also significantly better at accurately drawing belt fit than the comparison group. The video intervention was described as relevant, interesting, and the intervention group favored the provision of a diverse sample of models in the intervention.ConclusionsOverall, the study provides insight into relevant target beliefs for an intervention focused on belt fit and suggests that a brief video-based intervention in the style of a public service announcement may be effective in promoting positive beliefs and knowledge around belt fit. Future efforts should confirm these findings with a larger sample size spanning multiple geographic and demographic areas.Practical applicationsThese findings can help better inform intervention initiatives to improve occupant belt fit.     

Fleet analysis of headway distance for autonomous driving

IntroductionModern automobiles are going through a paradigm shift, where the driver may no longer be needed to drive the vehicle. As the self-driving vehicles are making their way to public roads the automakers have to ensure the naturalistic driving feel to gain drivers’ confidence and accelerate adoption rates.MethodThis paper filters and analyzes a subset of radar data collected from SHRP2 with focus on characterizing the naturalistic headway distance with respect to the vehicle speed.ResultsThe paper identifies naturalistic headway distance and compares it with the previous findings from the literature.ConclusionA clear relation between time headway and speed was confirmed and quantified. A significant difference exists among individual drivers which supports a need to further refine the analysis.Practical applicationsBy understanding the relationship between human driving and their surroundings, the naturalistic driving behavior can be quantified and used to increase the adoption rates of autonomous driving. Dangerous and safety-compromising driving can be identified as well in order to avoid its replication in the control algorithms.