Cycle Aware Package: Using Haddon’s matrix to reduce novice driver crashes with cyclists

Abstract

Objectives: What are we teaching drivers about safely interacting with cyclists? This is the main objective of the Cycle Aware project. As part of the study, a teaching and learning package is being developed to fill this gap in Australia.

Methods: Cycle Aware is a major national project that takes a mixed methods approach to investigate how cyclists are taught to share the road with cyclists when learning to drive. The project has five stages: (1) a national review of the driver licensing documentation; (2) interviews with key stakeholders (n?=?35) involved with novice driver education; (3) analysis of cyclist-novice driver crash data to identify the most frequent crash types; (4) develop a teaching and learning package (Cycle Aware Package), and; trial and evaluate Cycle Aware Package.

Results: Overall, drivers are taught little about sharing the road with cyclists. Representation of cyclists in government documentation is mixed and mostly negative and interviews identified driver-cyclist tensions related to attitudes and awareness of sharing the road. Crash types were similar among novice and experienced drivers. The Cycle Aware Package is being finalised and will be trialled in South Australia and Northern Territory in early 2019.

Conclusions: Cycle Aware will provide new insights into the way drivers are taught to share the road with cyclists. The Cycle Aware Package with interactive online content and driver competencies will facilitate a new approach to addressing this gap among Australia drivers.     

Cycle Aware: Piloting a module for novice drivers

Introduction: In low-cycling countries, motor-vehicle traffic and driver behavior are well known barriers to the uptake of bicycles, particularly for utility cycling. Lack of separation between cyclists and faster-moving traffic is one key issue, while attitudes of drivers toward and/or harassment of cyclists is another. Cyclist-related driver education has been recommended as a means to improve driver-cyclist interactions. Methods: The driver licensing process provides an opportunity for such education. The Cycle Aware module was developed to test and enhance novice drivers’ knowledge of interacting safely with cyclists. It was piloted across three Australian jurisdictions targeting both novice and experienced drivers. Participants were asked to complete the Cycle Aware module and an accompanying survey. A total of 134 novice and 97 experienced drivers completed the survey with 42 novice and 50 experienced drivers going on to complete the module. Results: Both groups of drivers scored equally well in the module but the very youngest and very oldest participants were more likely to have some incorrect responses. We did not find any relationship between correct module scores and attitudes toward cyclists. Survey results showed both novice and experienced drivers had somewhat positive attitudes toward cyclists. The two cohorts differed on several attitude questions. Sixty percent (60%) of novices compared to 30% of experienced drivers reported feeling concerned when sharing the road with cyclists, and novices were less likely to agree that cyclists had a right to use the roads. Conclusions and practical applications: The analysis suggests novices need to be better equipped to share roads confidently with cyclists and to recognize cyclists as legitimate traffic participants.     

Drivers’ visual scanning behavior at signalized and unsignalized intersections: A naturalistic driving study in China

Introduction: Intersections are the most dangerous locations in urban traffic. The present study aims to investigate drivers’ visual scanning behavior at signalized and unsignalized intersections. Method: Naturalistic driving data at 318 green phase signalized intersections and 300 unsignalized ones were collected. Drivers’ glance allocations were manually categorized into 10 areas of interest (AOIs), based on which three feature subsets were extracted including glance allocation frequencies, durations and AOI transition probabilities. The extracted features at signalized and unsignalized intersections were compared. Features with statistical significances were integrated to characterize drivers’ scanning patterns using the hierarchical clustering method. Andrews Curve was adopted to visually illustrate the clustering results of high-dimensional data. Results: Results showed that drivers going straight across signalized intersections had more often glances at the left view mirror and longer fixation on the near left area. When turning left, drivers near signalized intersections had more frequent glances at the left view mirror, fixated much longer on the forward and rearview mirror area, and had higher transition probabilities from near left to far left. Compared with drivers’ scanning patterns in left turning maneuver at signalized intersections, drivers with higher situation awareness levels would divide more attention to the forward and right areas than at unsignalized intersections. Conclusions: This study revealed that intersection types made differences on drivers’ scanning behavior. Practical applications: These findings suggest that future applications in advanced driver assistance systems and driver training programs should recommend different scanning strategies to drivers at different types of intersections.     

Forward collision warning based on a driver model to increase drivers’ acceptance

Abstract

Objective: Systems that can warn the driver of a possible collision with a vulnerable road user (VRU) have significant safety benefits. However, incorrect warning times can have adverse effects on the driver. If the warning is too late, drivers might not be able to react; if the warning is too early, drivers can become annoyed and might turn off the system. Currently, there are no methods to determine the right timing for a warning to achieve high effectiveness and acceptance by the driver. This study aims to validate a driver model as the basis for selecting appropriate warning times. The timing of the forward collision warnings (FCWs) selected for the current study was based on the comfort boundary (CB) model developed during a previous project, which describes the moment a driver would brake. Drivers’ acceptance toward these warnings was analyzed. The present study was conducted as part of the European research project PROSPECT (“Proactive Safety for Pedestrians and Cyclists”).

Methods: Two warnings were selected: One inside the CB and one outside the CB. The scenario tested was a cyclist crossing scenario with time to arrival (TTA) of 4?s (it takes the cyclist 4?s to reach the intersection). The timing of the warning inside the CB was at a time to collision (TTC) of 2.6?s (asymptotic value of the model at TTA = 4?s) and the warning outside the CB was at TTC = 1.7?s (below the lower 95% value at TTA = 4?s). Thirty-one participants took part in the test track study (between-subjects design where warning time was the independent variable). Participants were informed that they could brake any moment after the warning was issued. After the experiment, participants completed an acceptance survey.

Results: Participants reacted faster to the warning outside the CB compared to the warning inside the CB. This confirms that the CB model represents the criticality felt by the driver. Participants also rated the warning inside the CB as more disturbing, and they had a higher acceptance of the system with the warning outside the CB. The above results confirm the possibility of developing wellsaccepted warnings based on driver models.

Conclusions: Similar to other studies’ results, drivers prefer warning times that compare with their driving behavior. It is important to consider that the study tested only one scenario. In addition, in this study, participants were aware of the appearance of the cyclist and the warning. A further investigation should be conducted to determine the acceptance of distracted drivers.     

Cycling Skill Inventory: Assessment of motor–tactical skills and safety motives

Abstract

Objective: It is well established within the traffic psychology literature that a distinction can be made between driving skill and driving style. The majority of self-report questionnaires have been developed for car drivers, whereas only limited knowledge exists on the riding skill and style of cyclists. Individual differences in cycling skills need to be understood in order to apply targeted interventions.

Methods: This study reports on a psychometric analysis of the Cycling Skill Inventory (CSI), a self-report questionnaire that asks cyclists to rate themselves from definitely weak to definitely strong on 17 items. Herein, we administered the CSI using an online crowdsourcing method, complemented with respondents who answered the questionnaire using paper and pencil (n?=?1,138 in total). Our analysis focuses on understanding the major sources of variance of the CSI and its correlates with gender, age, exposure, and self-reported accident involvement as a cyclist.

Results: The results showed that 2 components underlie the item data: Motor–tactical skills and safety motives. Correlational analyses indicated that participants with a higher safety motives score were involved in fewer self-reported cycling accidents in the past 3 years. The analysis also confirmed well-established gender differences, with male cyclists having lower safety motives but higher motor–tactical skills than female cyclists.

Conclusions: The nomological network of the CSI for cyclists is similar to that of the Driving Skill Inventory for car drivers. Safety motives are a predictor of self-reported accident involvement among cyclists.     

Driver Behavior During Overtaking Maneuvers from the 100-Car Naturalistic Driving Study

Objective: Lane changes with the intention to overtake the vehicle in front are especially challenging scenarios for forward collision warning (FCW) designs. These overtaking maneuvers can occur at high relative vehicle speeds and often involve no brake and/or turn signal application. Therefore, overtaking presents the potential of erroneously triggering the FCW. A better understanding of driver behavior during lane change events can improve designs of this human–machine interface and increase driver acceptance of FCW. The objective of this study was to aid FCW design by characterizing driver behavior during lane change events using naturalistic driving study data.

Methods: The analysis was based on data from the 100-Car Naturalistic Driving Study, collected by the Virginia Tech Transportation Institute. The 100-Car study contains approximately 1.2 million vehicle miles of driving and 43,000 h of data collected from 108 primary drivers. In order to identify overtaking maneuvers from a large sample of driving data, an algorithm to automatically identify overtaking events was developed. The lead vehicle and minimum time to collision (TTC) at the start of lane change events was identified using radar processing techniques developed in a previous study. The lane change identification algorithm was validated against video analysis, which manually identified 1,425 lane change events from approximately 126 full trips.

Results: Forty-five drivers with valid time series data were selected from the 100-Car study. From the sample of drivers, our algorithm identified 326,238 lane change events. A total of 90,639 lane change events were found to involve a closing lead vehicle. Lane change events were evenly distributed between left side and right side lane changes. The characterization of lane change frequency and minimum TTC was divided into 10 mph speed bins for vehicle travel speeds between 10 and 90 mph. For all lane change events with a closing lead vehicle, the results showed that drivers change lanes most frequently in the 40–50 mph speed range. Minimum TTC was found to increase with travel speed. The variability in minimum TTC between drivers also increased with travel speed.

Conclusions: This study developed and validated an algorithm to detect lane change events in the 100-Car Naturalistic Driving Study and characterized lane change events in the database. The characterization of driver behavior in lane change events showed that driver lane change frequency and minimum TTC vary with travel speed. The characterization of overtaking maneuvers from this study will aid in improving the overall effectiveness of FCW systems by providing active safety system designers with further understanding of driver action in overtaking maneuvers, thereby increasing system warning accuracy, reducing erroneous warnings, and improving driver acceptance.     

Fatal crash between a car operating with automated control systems and a tractor-semitrailer truck

Abstract

Objective: The objective of this investigation was to evaluate the interaction between an SAE level 2 automated vehicle and the driver, including the limitations imposed by the vehicle on the driver.

Methods: A case study of the first fatal crash involving a vehicle operating with an automated control system was performed using scene evidence, vehicle damage, and recorded data available from the vehicle, and information from both drivers, including experience, phone records, computer systems, and medical information, was reviewed.

Results: System performance data downloaded from the car indicated that the driver was operating it using the Traffic-Aware Cruise Control and Autosteer lane-keeping systems, which are automated vehicle control systems within Tesla’s Autopilot suite. As the car crested the hill, a tractor trailer began its left turn onto a crossing roadway. Although reconstruction of the crash determined that there was sufficient sight distance for both drivers to see each other and take action, neither responded to the circumstances leading to the collision. Further, based on the speeds of the vehicles and simulations of the truck’s path, the car driver had at least 10.4?s to detect the truck and take evasive action. Neither the car driver nor the Autopilot system changed the vehicle’s velocity.

?At the time of the crash, the system performance data indicated that the last driver interaction with the system was 1?min 51?s prior when the cruise control speed was set to 74?mph. The driver was operating the vehicle using the Autopilot system for 37 of the 41?min in the last trip. During this period, the vehicle detected the driver’s hands on the steering wheel for a total of 25?s; each time his hands were detected on the wheel was preceded by a visual alert or auditory warning.

Conclusions: The National Transportation Safety Board (NTSB) determined that the probable cause of the Williston, Florida, crash was the truck driver’s failure to yield the right of way to the car, combined with the car driver’s inattention due to overreliance on vehicle automation, which resulted in the car driver’s lack of reaction to the presence of the truck. Contributing to the car driver’s overreliance on the vehicle automation was the car’s operational design, which permitted the driver’s prolonged disengagement from the driving task and his use of the automation in ways inconsistent with guidance and warnings from the manufacturer.     

Fatal and serious collisions involving pedal cyclists and trucks in London between 2007 and 2011

Objective: Increased numbers of people riding pedal cycles have led to a greater focus on pedal cycle safety. The aim of this article is to explore factors that are associated with fatal and a small number of serious-injury pedal cyclist crashes involving trucks that occurred in London between 2007 and 2011.

Methods: Data were collected from police collision files for 53 crashes, 27 of which involved a truck (≥3.5 tonnes) and a pedal cycle. A systematic case review approach was used to identify the infrastructure, vehicle road user, and management factors that contributed to these crashes and injuries and how these factors interacted.

Results: Trucks turning left conflicting with pedal cyclists traveling straight ahead was a common crash scenario. Key contributory factors identified included the pedal cyclists not being visible to the truck drivers, road narrowing, and inappropriate positioning of pedal cyclists.

Conclusions: Crashes involving trucks and pedal cyclists are complex events that are caused by multiple interacting factors; therefore, multiple measures are required to prevent them from occurring.     

The effect of age and gender on motor vehicle driver injury severity at highway-rail grade crossings in the United States

IntroductionBased on the Federal Railway Administration (FRA) database, there were 25,945 highway-rail crossing accidents in the United States between 2002 and 2011. With an extensive database of highway-rail grade crossing accidents in the United States from 2002 to 2011, estimation results showed that there were substantial differences across age/gender groups for driver's injury severity.MethodThe study applied an ordered probit model to explore the determinants of driver injury severity for motor vehicle drivers at highway-rail grade crossings.ResultsThe analysis found that there are important behavioral and physical differences between male and female drivers given a highway-rail grade crossing accident happened.Practical applicationsOlder drivers have higher fatality probabilities when driving in open space under passive control especially during bad weather condition. Younger male drivers are found to be more likely to have severe injuries at rush hour with high vehicle speed passing unpaved highway-rail grade crossings under passive control. Synthesizing these results led to the conclusion that the primary problem with young is risk-taking and lack of vehicle handling skills. The strength of older drivers lies in their aversion to risk, but physical degradation issues which result in longer reaction/perception times and degradation in vision and hearing often counterbalance this attribute.     

Crash and injury prevention estimates for intersection driver assistance systems in left turn across path/opposite direction crashes in the United States

Abstract

Objective: The objective of this research study was to estimate the number of left turn across path/opposite direction (LTAP/OD) crashes and injuries that could be prevented in the United States if vehicles were equipped with an intersection advanced driver assistance system (I-ADAS).

Methods: This study reconstructed 501 vehicle-to-vehicle LTAP/OD crashes in the United States that were investigated in the NHTSA National Motor Vehicle Crash Causation Survey (NMVCCS). The performance of 30 different I-ADAS system variations was evaluated for each crash. These variations were the combinations of 5 time-to-collision (TTC) activation thresholds, 3 latency times, and 2 different response types (automated braking and driver warning). In addition, 2 sightline assumptions were modeled for each crash: One where the turning vehicle was visible long before the intersection and one where the turning vehicle was only visible within the intersection. For resimulated crashes that were not avoided by I-ADAS, a new crash delta-V was computed for each vehicle. The probability of Abbreviated Injury Scale 2 or higher injury in any body region (Maximum Abbreviated Injury Scale [MAIS] 2+F) to each front-row occupant was computed.

Results: Depending on the system design, sightline assumption, I-ADAS variation, and fleet penetration, an I-ADAS system that automatically applies emergency braking could avoid 18–84% of all LTAP/OD crashes. Only 0–32% of all LTAP/OD crashes could have been avoided using an I-ADAS system that only warns the driver. An I-ADAS system that applies emergency braking could prevent 47–93% of front-row occupants from receiving MAIS 2?+?F injuries. A system that warns the driver in LTAP/OD crashes was able to prevent 0–37% of front-row occupants from receiving MAIS 2?+?F injuries. The effectiveness of I-ADAS in reducing crashes and number of injured persons was higher when both vehicles were equipped with I-ADAS.

Conclusions: This study presents the simulated effectiveness of a hypothetical intersection active safety system on real crashes that occurred in the United States. This work shows that there is a strong potential to reduce crashes and injuries in the United States.     

Risk factors for being the at-fault driver: A case–control study

Objective: It is estimated that road traffic accidents are globally responsible for approximately 1.2 million deaths and 20 to 50 million injuries. About 70% of traffic incidences (TIs) occur in developing countries and among countries with high TI rates; Iran is the first. The aim of this study was to measure the association between being responsible for a traffic accident and some vehicle, human; and environmental related factors in Yasuj, a city with a high incidence of road traffic injuries and deaths in Iran.

Methods: This is a time-, date-, and place-matched case–control study conducted in 2015 using all traffic accidents registered and investigated by police during 2012. In total, 194 drivers were considered the at-fault driver in a traffic accident and the 194 drivers in the same collisions were included in the analysis.

Results: Based on the results from multivariate conditional logistic regression, significant associations between vehicle maneuver (OR_{Turn to right or left/Moving forward} = 11.10, 95% confidence interval [CI], 1.77–69.58, P = .01) and age (odds ratio [OR] = 1.11, 95% CI, 1.004–1.22, P = .04) and the chance of being an at-fault driver were found.

Conclusion: Driver behavior–related interventions including training and law enforcement seem to be more effective in reducing road traffic accidents in Iran.     

Acute injuries resulting from accidents involving powered mobility devices (PMDs)—Development and outcomes of PMD-related accidents in Sweden

Objective: Powered mobility devices (PMDs) are commonly used as aids for older people and people with disabilities, subgroups of vulnarable road users (VRUs) who are rarely noted in traffic safety contexts. However, the problem of accidents involving PMD drivers has been reported in many countries where these vehicles have become increasingly popular.

The aim of this study is to extract and analyze national PMD-related accident and injury data reported to the Swedish Traffic Accident Data Acquisition (STRADA) database. The results will provide valuable insight into the risks and obstacles that PMD drivers are exposed to in the traffic environment and may contribute to improving the mobility of this group in the long term.

Methods: The current study is based on data from 743 accidents and 998 persons. An analysis was performed on a subset of data (N?=?301) in order to investigate the development of accidents over a period of 10 years. Thereafter, each accident in the whole data set was registered as either single (N?=?427) or collision (N?=?315).

Results: The results show that there was a 3-fold increase in the number of PMD-related accidents reported to STRADA during the period 2007–2016.

With regard to single accidents, collisions, as well as fatalities, the injury statistics were dominated by males. Single accidents were more common than collisions (N?=?427 and N?=?316, respectively) and the level of injury sustained in each type of accident is on par.

The vast majority of single accidents resulted in the PMD driver impacting the ground (87%), due to either PMD turnover (71%) or the driver falling out of the PMD (16%). The reason for many of the single accidents was a difference in ground level (34%, typically a curb).

Cars, trucks, or buses were involved in 67% of collision events; these occured predominantly at junctions or intersections (70%).

Abbreviated Injury Scale (AIS) 3+ injuries were dominated by hip and head injuries in both single accidents and collision events.

Conclusions: The present study shows that further research on PMD accidents is required, with regard to both single accidents and collision events. To ensure that appropriate decisions are made, future work should follow up on injury trends and further improve the quality of PDM-related accident data. Improved vehicle stability and design, increased usage of safety equipment, proper training programs, effective maintenance services, and development of a supporting infrastructure would contribute to increased safety for PMD drivers.     

Measurement method for objective cyclist behavior parameters

Abstract

Objective: The objective of this research is to study the feasibility of measuring behavioral indicators that reflect effects of infrastructure and interaction with other road users.

Methods: An observation study was performed using 6 cameras above a separated cycle path next to a road which included a crossing with both cyclists and cars. A learning method based on Single Shot MultiBox Detector was applied to automatically detect the cyclists, and cyclist tracks were determined. Next, kinematic parameters were calculated from the cyclists’ tracks. Amongst others, the cyclists’ intensity, speed, position on the cycle path, and the distance to each other were analyzed for a busy period as well as for a quiet period of the day.

Results: With the measurement method developed in this study it is possible to analyze the cyclists’ intensity, the space they use at the cycle path, their average velocity, waiting times, the space and velocity amongst each other, and red light negation. However, collisions were not seen in the dataset analyzed, and the data is not sufficiently accurate to analyze sudden braking actions.

Conclusion: It can be concluded that the developed measurement method provides insight of the cyclists’ behavior in such a way that it can already be used for obtaining information to make changes to the infrastructure that will improve the comfort and safety of cyclists. The method could be further developed for doing qualitative comfort and safety analyses, and for doing analyses of the interaction between various types of road participants.     

Can a unique appearance of e-bikes,coupled with information on their characteristics,influence drivers’ gap acceptance?

Abstract

Objective: Car drivers tend to underestimate the speed of e-bikes and accept smaller gaps for crossing in front of them compared to conventional bicycles. As an explanation, it has been suggested that car drivers rely on their previous experience with conventional bicycles, which tells them that those mostly travel at low speeds. E-bikes, which look just like regular bicycles, do not conform to this expectation, resulting in potentially dangerous interactions. Based on this assumption, researchers have suggested to increase other road users’ awareness of e-bikes’ higher speeds by giving them a distinct appearance. The goal of our experiment was to investigate the effects of such a unique appearance, aided by clear instructions about the higher speeds of e-bikes, on gap acceptance.

Method: In order to investigate the effect of appearance independent of the effect of bicycle type, we used video sequences of conventional bicycles and e-bikes approaching at different levels of speed. The riders (regardless of what type of bike they were actually riding) either wore an orange helmet as an indicator for an e-bike, or a gray helmet indicating a conventional bicycle. Fifty participants were asked to indicate the smallest acceptable gap for a left turn in front of the cyclist or e-bike rider.

Results: The results showed significantly smaller acceptable gaps when confronted with the gray helmet (signal for bicycle) compared to the orange helmet (signal for e-bike), whereas there was no difference between the actual bicycle types.

Conclusions: Overall, the results indicate that informing about e-bikes characteristics in combination with a unique appearance can lead to a more cautious behavior among car drivers.     

Analysis of Driver Evasive Maneuvering Prior to Intersection Crashes Using Event Data Recorders

Objective: Intersection crashes account for over 4,500 fatalities in the United States each year. Intersection Advanced Driver Assistance Systems (I-ADAS) are emerging vehicle-based active safety systems that have the potential to help drivers safely navigate across intersections and prevent intersection crashes and injuries. The performance of an I-ADAS is expected to be highly dependent upon driver evasive maneuvering prior to an intersection crash. Little has been published, however, on the detailed evasive kinematics followed by drivers prior to real-world intersection crashes. The objective of this study was to characterize the frequency, timing, and kinematics of driver evasive maneuvers prior to intersection crashes.

Methods: Event data recorders (EDRs) downloaded from vehicles involved in intersection crashes were investigated as part of NASS-CDS years 2001 to 2013. A total of 135 EDRs with precrash vehicle speed and braking application were downloaded to investigate evasive braking. A smaller subset of 59 EDRs that collected vehicle yaw rate was additionally analyzed to investigate evasive steering. Each vehicle was assigned to one of 3 precrash movement classifiers (traveling through the intersection, completely stopped, or rolling stop) based on the vehicle's calculated acceleration and observed velocity profile. To ensure that any significant steering input observed was an attempted evasive maneuver, the analysis excluded vehicles at intersections that were turning, driving on a curved road, or performing a lane change. Braking application at the last EDR-recorded time point was assumed to indicate evasive braking. A vehicle yaw rate greater than 4° per second was assumed to indicate an evasive steering maneuver.

Results: Drivers executed crash avoidance maneuvers in four-fifths of intersection crashes. A more detailed analysis of evasive braking frequency by precrash maneuver revealed that drivers performing complete or rolling stops (61.3%) braked less often than drivers traveling through the intersection without yielding (79.0%). After accounting for uncertainty in the timing of braking and steering data, the median evasive braking time was found to be between 0.5 to 1.5 s prior to impact, and the median initial evasive steering time was found to occur between 0.5 and 0.9 s prior to impact. The median average evasive braking deceleration for all cases was found to be 0.58 g. The median of the maximum evasive vehicle yaw rates was found to be 8.2° per second. Evasive steering direction was found to be most frequently in the direction of travel of the approaching vehicle.

Conclusions: The majority of drivers involved in intersection crashes were alert enough to perform an evasive action. Most drivers used a combination of steering and braking to avoid a crash. The average driver attempted to steer and brake at approximately the same time prior to the crash.     

Driving safety improves after individualized training: An RCT involving older drivers in an urban area

Objective: This study aimed to reproduce the results of a previous investigation on the safety benefits of individualized training for older drivers. We modified our method to address validity and generalizability issues.

Methods: Older drivers were randomly assigned to one of the 3 arms: (1) education alone, (2) education?+?on road training, and (3) education?+?on road?+?simulator training. Older drivers were recruited from a larger urban community. At the pre- and posttests (separated by 4 to 8 weeks) participants followed driving directions using a Global Positioning System (GPS) navigation system.

Results: Our findings support the positive influence of individualized on-road training for urban-dwelling older drivers. Overall, driving safety improved among drivers who received on-road training over those who were only exposed to an education session, F(1, 40) = 11.66, P = .001 (26% reduction in total unsafe driving actions [UDAs]). Statistically significant improvements were observed on observation UDAs (e.g., scanning at intersections, etc.), compliance UDAs (e.g., incomplete stop), and procedural UDAs (e.g., position in lane).

Conclusion: This study adds to the growing evidence base in support of individualized older driver training to optimize older drivers’ safety and promote continued safe driving.     

E-bikers’ braking behavior: Results from a naturalistic cycling study

Abstract

Objective: The number of e-bike users has increased significantly over the past few years and with it the associated safety concerns. Because e-bikes are faster than conventional bicycles and more prone to be in conflict with road users, e-bikers may need to perform avoidance maneuvers more frequently. Braking is the most common avoidance maneuver but is also a complex and critical task in emergency situations, because cyclists must reduce speed quickly without losing balance. The aim of this study is to understand the braking strategies of e-bikers in real-world traffic environments and to assess their road safety implications. This article investigates (1) how cyclists on e-bikes use front and rear brakes during routine cycling and (2) whether this behavior changes during unexpected conflicts with other road users.

Methods: Naturalistic data were collected from 6 regular bicycle riders who each rode e-bikes during a period of 2 weeks, for a total of 32.5?h of data. Braking events were identified and characterized through a combined analysis of brake pressure at each wheel, velocity, and longitudinal acceleration. Furthermore, the braking patterns obtained during unexpected events were compared with braking patterns during routine cycling.

Results: In the majority of braking events during routine cycling, cyclists used only one brake at a time, favoring one of the 2 brakes according to a personal pre-established pattern. However, the favored brake varied among cyclists: 66% favored the rear brake and 16% the front brake. Only 16% of the cyclists showed no clear preference, variously using rear brake, front brake, or combined braking (both brakes at the same time), suggesting that the selection of which brake to use depended on the characteristics of the specific scenario experienced by the cyclist rather than on a personal preference. In unexpected conflicts, generally requiring a larger deceleration, combined braking became more prevalent for most of the cyclists; still, when combined braking was not applied, cyclists continued to use the favored brake of routine cycling. Kinematic analysis revealed that, when larger decelerations were required, cyclists more frequently used combined braking instead of single braking.

Conclusions: The results provide new insights into the behavior of cyclists on e-bikes and may provide support in the development of safety measures including guidelines and best practices for optimal brake use. The results may also inform the design of braking systems intended to reduce the complexity of the braking operation.     

Situational characteristics of fatal pedestrian accidents involving vehicles traveling at low speeds in Japan

Abstract

Objective: This study aimed to investigate the situational characteristics of fatal pedestrian accidents involving vehicles traveling at low speeds in Japan. We focused on vehicles with 4 or more wheels. Such characteristics included daytime or nighttime conditions, road type, vehicle behaviors preceding the accident, and vehicle impact locations.

Methods: Pedestrian fatality data on vehicle–pedestrian accidents were obtained from the Institute for Traffic Accident Research and Data Analysis of Japan (ITARDA) from 2005 to 2014. Nine vehicle classifications were considered: Trucks with gross vehicle weight (GVW) ≥7.5 tons and <7.5 tons, buses, box vans, minivans, sport utility vehicles (SUVs), sedans, light passenger cars (LPCs), and light cargo vans (LCVs). We compared the situational daytime or nighttime conditions, road type, vehicle behaviors preceding the accident, and vehicle impact locations for accident-involved vehicles traveling at low and higher speeds across all vehicle types.

Results: The results indicate that pedestrian fatalities involving vehicles traveling at low speeds occurred more often under daytime conditions across all vehicle types. At signalized intersections, the relative proportions of pedestrian fatalities were significantly higher when vehicles were traveling at low speed, except when the accidents involved box vans or SUVs. Similarly, when vehicles turned right, the relative proportions of pedestrian fatalities were significantly higher when vehicles traveling at low speed were involved across all vehicle types. In terms of the frontal right vehicle impact location, the relative proportions of pedestrian fatalities were significantly higher when trucks with GVW ≥7.5 tons or <7.5 tons, sedans, or LCVs traveling at low speed were involved.

Conclusions: The situational characteristics of fatal pedestrian accidents involving vehicles traveling at low speeds identified in this study can guide targeted development of new traffic safety regulations or technologies specific to vehicle–pedestrian interactions at low vehicle travel speeds (i.e., driver alert devices or automated emergency braking systems). Ultimately, these developments can improve pedestrian safety by reducing the frequency or severity of vehicle–pedestrian accidents for vehicles turning right at intersections and/or reducing the number of resultant pedestrian fatalities.     

Difference in dummy responses in matched side impact tests of vehicles with and without side airbags

Objective: Insurance Institute for Highway Safety (IIHS) high-hooded side impacts were analyzed for matched vehicle tests with and without side airbags. The comparison provides a measure of the effectiveness of side airbags in reducing biomechanical responses for near-side occupants struck by trucks, SUVs, and vans at 50 km/h.

Method: The IIHS moving deformable barrier (MDB) uses a high-hooded barrier face. It weighs 1,500 kg and impacts the driver side perpendicular to the vehicle at 50 km/h. SID IIs dummies are placed in the driver and left second-row seats. They represent fifth percentile female occupants.

IIHS tests were reviewed for matches with one test with a side airbag and another without it in 2003–2007 model year (MY) vehicles. Four side airbag systems were evaluated: (1) curtain and torso side airbags, (2) head and torso side airbag, (3) curtain side airbag, and (4) torso side airbag.

There were 24 matched IIHS vehicle tests: 13 with and without a curtain and torso side airbags, 4 with and without a head and torso side airbag, 5 with and without a side curtain airbag, and 2 with and without a torso airbag. The head, chest, and pelvis responses were compared for each match and the average difference was determined across all matches for a type of side airbag.

Results: The average reduction in head injury criterion (HIC) was 68 ± 16% (P < .001) with curtain and torso side airbags compared to the HIC without side airbags. The average HIC was 296 with curtain and torso side airbags and 1,199 without them. The viscous response (VC) was reduced 54 ± 19% (P < .005) with curtain and torso side airbags. The combined acetabulum and ilium force (7 ± 15%) and pelvic acceleration (?2 ± 17%) were essentially similar in the matched tests.

The head and torso side airbag reduced HIC by 42 ± 30% (P < .1) and VC by 32 ± 26% compared to vehicles without a side airbag. The average HIC was 397 with the side head and torso airbag compared to 729 without it. The curtain airbag and torso airbag only showed lower head responses but essentially no difference in the chest and pelvis responses.

Conclusion: The curtain and torso side airbags effectively reduced biomechanical responses for the head and chest in 50 km/h side impacts with a high-hooded deformable barrier. The reductions in the IIHS tests are directionally the same as estimated fatality reductions in field crashes reported by NHTSA for side airbags.     

AEB effectiveness evaluation based on car-to-cyclist accident reconstructions using video of drive recorder

Objective: Though autonomous emergency braking (AEB) systems for car-to-cyclist collisions have been under development, an estimate of the benefit of AEB systems based on an analysis of accident data is needed for further enhancing their development. Compared to the data available from in-depth accident data files, data provided by drive recorders can be used to reconstruct car-to-cyclist collisions with greater accuracy because the position of cyclists can be observed from the videos. In this study, using data from drive recorders, the performance and limitations of AEB systems were investigated.

Method: Data of drive recorders involving taxi-to-cyclist collisions were collected. Using the images collected from the drive recorders of those taxis, 40 cases of 90° car-to-cyclist intersection collisions were reconstructed using PC-Crash. Then, the collisions were reconstructed again utilizing car models with AEB systems installed while changing the sensor’s field of view (FOV) and the delay time of initiating vehicle deceleration.

Results: The angle of FOV has a significant influence on avoiding car-to-cyclist collisions. Using a 50° FOV with a braking delay time of 0.5?s resulted in avoiding 6 collisions, and using a 90° FOV resulted in avoiding an additional 14 collisions. Even when installing an ideal AEB system providing 360° FOV and no delay time for braking, 8 collisions were not avoided, though the impact velocities were reduced for all of these remaining collisions. These collisions were caused by the cyclists’ sudden appearance in front of cars, and the time-to-collision (TTC) when the cyclists appeared was less than 0.9?s.

Conclusion: The AEB systems were effective for mitigating collisions that occurred due to driver perception delay. Because cyclists have a traveling velocity, a wide-angle FOV is effective for reduction of car-to-cyclist intersection collisions. The reduction of delay time in braking can reduce the number of collisions that are close to the braking performance limit. The collisions that remained even with an ideal AEB system in the PC-Crash simulation indicate that such collisions could still occur for autonomous cars if the traffic environment does not change.