Modeling collision avoidance maneuvers for micromobility vehicles.

INTRODUCTION: In recent years, as novel micromobility vehicles (MMVs) have hit the market and rapidly gained popularity, new challenges in road safety have also arisen. There is an urgent need for validated models that comprehensively describe the behavior of such novel MMVs. This study aims to compare the longitudinal and lateral control of bicycles and e-scooters in a collision-avoidance scenario from a top-down perspective, and to propose appropriate quantitative models for parameterizing and predicting the trajectories of the avoidance-braking and steering-maneuvers. METHOD: We compared a large e-scooter and a light e-scooter with a bicycle (in assisted and non-assisted modes) in field trials to determine whether these new vehicles have different maneuverability constraints when avoiding a rear-end collision by braking and/or steering. RESULTS: Braking performance in terms of deceleration and jerk varies among the different types of vehicles; specifically, e-scooters are not as effective at braking as bicycles, but the large e-scooter demonstrated better braking performance than the light one. No statistically significant difference was observed in the steering performance of the vehicles. Bicycles were perceived as more stable, maneuverable, and safe than e-scooters. The study also presents arctangent kinematic models for braking and steering, which demonstrate better accuracy and informativeness than linear models. CONCLUSIONS: This study demonstrates that the new micromobility solutions have some maneuverability characteristics that differ significantly from those of bicycles, and even within their own kind. Steering could be a more efficient collision-avoidance strategy for MMVs than braking under certain circumstances, such as in a rear-end collision. More complicated modeling for MMV kinematics can be beneficial but needs validation. PRACTICAL APPLICATIONS: The proposed arctangent models could be used in new advanced driving assistance systems to prevent crashes between cars and MMV users. Micromobility safety could be improved by educating MMV riders to adapt their behavior accordingly. Further, knowledge about the differences in maneuverability between e-scooters and bicycles could inform infrastructure design, and traffic regulations.

Drivers passing cyclists: How does sight distance affect safety? Results from a naturalistic study.

INTRODUCTION: Cycling is popular for its ecological, economic, and health benefits. However, especially in rural areas, cyclists may need to share the road with motorized traffic, which is often perceived as a threat. Overtaking a cyclist is a particularly critical maneuver for drivers as they need to control their lateral clearance and speed when passing the cyclist, possibly in the presence of oncoming vehicles or view-obstructing curves. An overtaking vehicle can destabilize the cyclist when passing with low clearance and high speed. At the same time, the cyclist may get scared and eventually stop cycling. In this work, we investigated how visibility regarding available sight distance-an important factor for infrastructure design and regulation-affects drivers' behavior when overtaking cyclists. METHOD: Using four roadside-based traffic sensors, we collected naturalistic data that contained kinematics of drivers overtaking cyclists on a rural road in Sweden. We modeled lateral clearance and speed at the passing moment in response to variables such as sight distance and oncoming traffic with a Bayesian multivariate approach. RESULTS: Fitted on 81 maneuvers, the model revealed that drivers reduced lateral clearance under reduced sight distance. Speed was similarly reduced, however, not as clearly. When an oncoming vehicle was present, it had a similar-yet stronger-effect than sight distance. While we found an overall correlation between clearance and speed, some maneuvers were recorded at critically low clearance. CONCLUSIONS: Cyclists' safety is endangered when passed by drivers under reduced visibility or close to oncoming traffic. PRACTICAL APPLICATIONS: Decision-making for infrastructure and policymaking should aim at prohibiting overtaking in areas with reduced visibility or close oncoming traffic. The model developed in this study may serve as a reference to vehicle active-safety systems and automated driving. The collected and processed data may support evaluating driver models fitted on less ecologically valid data and simulated active-safety systems.

How do cyclists interact with motorized vehicles at unsignalized intersections? Modeling cyclists' yielding behavior using naturalistic data.

When a cyclist's path intersects with that of a motorized vehicle at an unsignalized intersection, serious conflicts may happen. In recent years, the number of cyclist fatalities in this conflict scenario has held steady, while the number in many other traffic scenarios has been decreasing. There is, therefore, a need to further study this conflict scenario in order to make it safer. With the advent of automated vehicles, threat assessment algorithms able to predict cyclists' (other road users') behavior will be increasingly important to ensure safety. To date, the handful of studies that have modeled the vehicle-cyclist interaction at unsignalized intersections have used kinematics (speed and location) alone without using cyclists' behavioral cues, such as pedaling or gesturing. As a result, we do not know whether non-verbal communication (e.g., from behavioral cues) could improve model predictions. In this paper, we propose a quantitative model based on naturalistic data, which uses additional non-verbal information to predict cyclists' crossing intentions at unsignalized intersections. Interaction events were extracted from a trajectory dataset and enriched by adding cyclists' behavioral cues obtained from sensors. Both kinematics and cyclists' behavioral cues (e.g., pedaling and head movement), were found to be statistically significant for predicting the cyclist's yielding behavior. This research shows that adding information about the cyclists' behavioral cues to the threat assessment algorithms of active safety systems and automated vehicles will improve safety.

How do different micro-mobility vehicles affect longitudinal control? Results from a field experiment.

INTRODUCTION: While micromobility vehicles offer new transport opportunities and may decrease fuel emissions, the extent to which these benefits outweigh the safety costs is still uncertain. For instance, e-scooterists have been reported to experience a tenfold crash risk compared to ordinary cyclists. Today, we still do not know whether the real safety problem is the vehicle, the human, or the infrastructure. In other words, the new vehicles may not necessarily be unsafe; the behavior of their riders, in combination with an infrastructure that was not designed to accommodate micromobility, may be the real issue. METHOD: In this paper, we compared e-scooters and Segways with bicycles in field trials to determine whether these new vehicles create different constraints for longitudinal control (e.g., in braking avoidance maneuvers). RESULTS: The results show that acceleration and deceleration performance changes across vehicles; specifically, e-scooters and Segways that we tested cannot brake as efficiently as bicycles. Further, bicycles are experienced as more stable, maneuverable, and safe than Segways and e-scooters. We also derived kinematic models for acceleration and braking that can be used to predict rider trajectories in active safety systems. PRACTICAL APPLICATIONS: The results from this study suggest that, while new micromobility solutions may not be intrinsically unsafe, they may require some behavior and/or infrastructure adaptations to improve their safety. We also discuss how policy making, safety system design, and traffic education may use our results to support the safe integration of micromobility into the transport system.

On the evaluation of visual nudges to promote safe cycling: Can we encourage lower speeds at intersections?

OBJECTIVE: Crashes between cars and cyclists at urban intersections are common, and their consequences are often severe. Typical causes for this type of crashes included the excessive speed of the cyclist as well as car drivers failing to see the cyclist. Measures that decrease the cyclists' speed may lead to safer car-cyclist interactions. This study aimed to investigate the extent to which cyclists may approach intersections at a lower speed when nudged to do so. METHODS: Visual flat-stripe nudges were placed on bicycle lanes in the proximity of uncontrolled intersections (with a history of car-cyclist crashes) in two locations in Gothenburg, Sweden. This specific nudge was the one obtaining the best results from a previous study that tested different nudges in controlled experiments. Video data from the intersections were recorded with a site-based video recording system both before (baseline), and after (treatment), the nudge was installed. The video data was processed to extract trajectory and speed for cyclists. The baseline and treatment periods were equivalent in terms of day of the week, light, and weather conditions. Furthermore, two treatment periods were recorded to capture the effect of the nudge over time in one of the locations. RESULTS: Leisure cyclists showed lower speeds in treatment than in baseline for both locations. Commuters were less affected by the nudge than leisure cyclists. This study shows that visual nudges to decrease cyclist speed at intersections are hard to evaluate in the wild because of the many confounders. We also found that the effect of visual nudges may be smaller than the effect of environmental factors such as wind and demographics, making their evaluation even harder. CONCLUSIONS: The observed effect of speed might not be very high, but the advantage both in terms of cyclist acceptance and monetary cost makes an investment in the measure very low risk. This study informs policymakers and road authorities that want to promote countermeasures to intersection crashes and improve the safety of cyclists at urban intersections.

Driver Visual Attention Before and After Take-Over Requests During Automated Driving on Public Roads.

OBJECTIVE: This study aims to understand drivers' visual attention before and after take-over requests during automated driving (AD), when the vehicle is fully responsible for the driving task on public roads. BACKGROUND: Existing research on transitions of control from AD to manual driving has mainly focused on take-over times. Despite its relevance for vehicle safety, drivers' visual attention has received little consideration. METHOD: Thirty participants took part in a Wizard of Oz study on public roads. Drivers' visual attention was analyzed before and after four take-over requests. Visual attention during manual driving was also recorded to serve as a baseline for comparison. RESULTS: During AD, the participants showed reduced visual attention to the forward road and increased duration of single off-road glances compared to manual driving. In response to take-over requests, the participants looked away from the forward road toward the instrument cluster. Levels of visual attention towards the forward road did not return to the levels observed during manual driving until after 15 s had passed. CONCLUSION: During AD, drivers may look toward non-driving related task items (e.g., mobile phone) instead of forward. Further, when a transition of control is required, drivers may take over control before they are aware of the driving environment or potential threat(s). Thus, it cannot be assumed that drivers are ready to respond to events shortly after the take-over request. APPLICATION: It is important to consider the effect of the design of take-over requests on drivers' visual attention alongside take-over times.

A data-driven framework for the safe integration of micro-mobility into the transport system: Comparing bicycles and e-scooters in field trials.

INTRODUCTION: Recent advances in technology create new opportunities for micro-mobility solutions even as they pose new challenges to transport safety. For instance, in the last few years, e-scooters have become increasingly popular in several cities worldwide; however, in many cases, the municipalities were simply unprepared for the new competition for urban space between traditional road users and e-scooters, so that bans became a necessary, albeit drastic, solution. In many countries, traditional vehicles (such as bicycles) may not be intrinsically safer than e-scooters but are considered less of a safety threat, possibly because-for cyclists-social norms, traffic regulations, and access to infrastructure are established, reducing the number of negative stakeholders. Understanding e-scooter kinematics and e-scooterist behavior may help resolve conflicts among road users, by favoring a data-driven integration of these new e-vehicles into the transport system. In fact, regulations and solutions supported by data are more likely to be acceptable and effective for all stakeholders. As new personal-mobility solutions enter the market, e-scooters may just be the beginning of a micro-mobility revolution. METHOD: This paper introduces a framework (including planning, execution, analysis, and modeling) for a data-driven evaluation of micro-mobility vehicles. The framework leverages our experience assessing bicycle dynamics in real traffic to make objective and subjective comparisons across different micro-mobility solutions. In this paper, we use the framework to compare bicycles and e-scooters in field tests. RESULTS: The preliminary results show that e-scooters may be more maneuverable and comfortable than bicycles, although the former require longer braking distances. PRACTICAL APPLICATIONS: Data collected from e-scooters may, in the short term, facilitate policy making, geo-fencing solutions, and education; in the long run, the same data will promote the integration of e-scooters into a cooperative transport system in which connected automated vehicles share the urban space with micro-mobility vehicles. Finally, the framework and the models presented in this paper may serve as a reference for the future assessment of new micro-mobility vehicles and their users' behavior (although advances in technology and novel micro-mobility solutions will inevitably require some adjustments).

On the importance of driver models for the development and assessment of active safety: A new collision warning system to make overtaking cyclists safer.

The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. When cars and bicycles share the same lane, cars typically need to overtake them, creating dangerous conflicts-especially on rural roads, where cars travel much faster than cyclists. In order to protect cyclists, advanced driver assistance systems (ADAS) are being developed and introduced to the market. One of them is a forward collision warning (FCW) system that helps prevent rear-end crashes by identifying and alerting drivers of threats ahead. The objective of this study is to assess the relative safety benefit of a behaviour-based (BB) FCW system that protects cyclists in a car-to-cyclist overtaking scenario. Virtual safety assessments were performed on crashes derived from naturalistic driving data. A series of driver response models was used to simulate different driver reactions to the warning. Crash frequency in conjunction with an injury risk model was used to estimate the risk of cyclist injury and fatality. The virtual safety assessment estimated that, compared to no FCW, the BB FCW could reduce cyclists' fatalities by 53-96% and serious injuries by 43-94%, depending on the driver response model. The shorter the driver's reaction time and the larger the driver's deceleration, the greater the benefits of the FCW. The BB FCW also proved to be more effective than a reference FCW based on the Euro NCAP standard test protocol. The findings of this study demonstrate the BB FCW's great potential to avoid crashes and reduce injuries in car-to-cyclist overtaking scenarios, even when the driver response model did not exceed a comfortable rate of deceleration. The results suggest that a driver behaviour model integrated into ADAS collision threat algorithms can provide substantial safety benefits.

Modelling duration of car-bicycles overtaking manoeuvres on two-lane rural roads using naturalistic data.

Nowadays, Spanish two-lane rural roads frequently accommodate sport cyclists. They usually ride on the shoulder or on the right edge of the lane, sharing the infrastructure with motorised vehicles. Due to the speed difference between road users, the most frequent and dangerous interaction is in overtaking manoeuvres. One key factor from a safety and traffic operation point of view is the overtaking duration. The main aim of this paper is to analyse how factors related to the road, the cyclists, and the overtaking manoeuvre influence the duration of overtaking to cyclists on two-lane rural roads. Naturalistic field data were obtained using instrumented bicycles. Seven groups of cyclists, formed by different numbers of cyclists riding in-line and two-abreast, rode along five rural roads with different geometric and traffic characteristics. A total of 1592 flying manoeuvres, in which drivers did not reduce their speed, and 192 accelerative manoeuvres were analysed. The overtaking duration, considering each overtaking strategy, was modelled using Bayesian statistics. Results showed that flying manoeuvres were more prevalent than accelerative. They were performed with higher speeds and lower lateral clearances and, therefore, presented lower overtaking durations. For both overtaking strategies, duration increased on wider roads and with a larger size of the group. The presence of an oncoming vehicle decreased the overtaking duration. However, other factors presented opposite effects on the duration depending on the overtaking strategy. The developed predictive models allow obtaining overtaking durations varying road and cyclist grouping characteristics. Results can be used by road administration to manage and propose some specific countermeasures to integrate the cyclists in a safe and efficient way on two-lane rural roads.

Modelling discomfort: How do drivers feel when cyclists cross their path?

Many cyclist fatalities occur on roads when crossing a vehicle path. Active safety systems address these interactions. However, the driver behaviour models that these systems use may not be optimal in terms of driver acceptance. Incorporating explicit estimates of driver discomfort might improve acceptance. This study quantified the degree of discomfort experienced by drivers when cyclists crossed their travel path. Participants were instructed to drive through an intersection in a fixed-base simulator or on a test track, following the same experimental protocol. During the experiments, three variables were controlled: 1) the car speed (30, 50 km/h), 2) the bicycle speed (10, 20 km/h), and 3) the bicycle-car encroachment sequence (bicycle clears the intersection first, potential 50 %-overlap crash, and car clears the intersection first). For each trial, a covariate, the car's time-to-arrival at the intersection when the bicycle appears (TTAvis), was calculated. After each trial, the participants were asked to report their experienced discomfort on a 7-point Likert scale ranging from no discomfort (1) to maximum discomfort (7). The effect of the three controlled variables and the effect of TTAvis on drivers' discomfort were estimated using cumulative link mixed models (CLMM). Across both experimental environments, the controlled variables were shown to significantly influence discomfort. TTAvis was shown to have a significant effect on discomfort as well; the closer to zero TTAvis was (i.e., the more critical the situation), the more likely the driver reported great discomfort. The prediction accuracies of the CLMM with all three controlled variables and the CLMM with TTAvis were similar, with an average accuracy between 40 and 50 % for the exact discomfort level and between 80 and 85 % allowing deviations by one step. Our model quantifies driver discomfort. Such model may be included in the decision-making algorithms of active safety systems to improve driver acceptance. In fact, by tuning system activation times depending on the expected level of discomfort that a driver would experience in such situation, a system is not likely to annoy a driver.

Modelling cyclists' comfort zones from obstacle avoidance manoeuvres.

This paper introduces a framework for modelling the cyclist's comfort zone. Unlike the driver's comfort zone, little is known about the cyclist's. The framework draws on existing literature in cognitive science about driver behaviour to explain experimental results from cycling field trials, and the modelling of these results. We modelled braking and steering manoeuvres from field data of cyclists' obstacle avoidance within their comfort zone. Results show that when cyclists avoided obstacles by braking, they kept a constant deceleration; as speed increased, they started to brake earlier, farther from the obstacle, maintaining an almost constant time to collision. When cyclists avoided obstacles by steering, they maintained a constant distance from the object, independent of speed. Overall, the higher the speed, the more the steering manoeuvres were temporally delayed compared to braking manoeuvres. We discuss these results and other similarities between cyclist and driver behaviour during obstacle avoidance. Implications for the design of acceptable active safety and infrastructure design are also addressed.

How do drivers negotiate intersections with pedestrians? The importance of pedestrian time-to-arrival and visibility.

Forward collision warning (FCW) and autonomous emergency braking (AEB) systems are increasingly available and prevent or mitigate collisions by alerting the driver or autonomously braking the vehicle. Threat-assessment and decision-making algorithms for FCW and AEB aim to find the best compromise for safety by intervening at the "right" time: neither too early, potentially upsetting the driver, nor too late, possibly missing opportunities to avoid the collision. Today, the extent to which activation times for FCW and AEB should depend on factors such as pedestrian speed and lane width is unknown. To guide the design of FCW and AEB intervention time, we employed a fractional factorial design, and determined how seven factors (crossing side, car speed, pedestrian speed, crossing angle, pedestrian size, zebra-crossing presence, and lane width) affect the driver's response process and comfort zone when negotiating an intersection with a pedestrian. Ninety-four volunteers drove through an intersection in a fixed-base driving simulator, which was based on open-source software (OpenDS). Several parameters, including pedestrian time-to-arrival and driver response time, were calculated to describe the driver response process and define driver comfort boundaries. Linear mixed-effect models showed that driver responses depended mainly on pedestrian time-to-arrival and visibility, whereas factors such as pedestrian size, zebra-crossing presence, and lane width did not significantly influence the driver response process. Drivers released the accelerator pedal in 99.8 % of the trials and braked in 89 % of the trials. Forty-six percent of the drivers changed their negotiation strategy (proportion of pedal braking to engine braking) to minimize driving effort over the course of the experiment. In fact, 51 % of the of the inexperienced drivers changed their response strategy whereas only 40 % of the experienced drivers did; nevertheless, all drivers behaved similarly, independent of driving experience. The flexible and customizable driving environment provided by OpenDS may be a viable platform for behavioural experiments in driving simulators. Results from this study suggest that visibility and pedestrian time-to-arrival are the most important variables for defining the earliest acceptable FCW and AEB activations. Fractional factorial design effectively compared the influence of seven factors on driver behaviour within a single experiment; however, this design did not allow in-depth data analysis. In the future, OpenDS might become a standard platform, enabling crowdsourcing and favouring repeatability across studies in traffic safety. Finally, this study advises future design and evaluation procedures (e.g. new car assessment programs) for FCW and AEB by highlighting which factors deserve further investigation and which ones do not.

How do oncoming traffic and cyclist lane position influence cyclist overtaking by drivers?

Overtaking cyclists is challenging for drivers because it requires a well-timed, safe interaction between the driver, the cyclist, and the oncoming traffic. Previous research has investigated this manoeuvre in different experimental environments, including naturalistic driving, naturalistic cycling, and simulator studies. These studies highlight the significance of oncoming traffic-but did not extensively examine the influence of the cyclist's position within the lane. In this study, we performed a test-track experiment to investigate how oncoming traffic and position of the cyclist within the lane influence overtaking. Participants overtook a robot cyclist, which was controlled to ride in two different lateral positions within the lane. At the same time, an oncoming robot vehicle was controlled to meet the participant's vehicle with either 6 or 9 s time-to-collision. The order of scenarios was randomised over participants. We analysed safety metrics for the four different overtaking phases, reflecting drivers' safety margins to rear-end, head-on, and side-swipe collisions, in order to investigate the two binary factors: 1) time gap between ego vehicle and oncoming vehicle, and 2) cyclist lateral position. Finally, the effects of these two factors on the safety metrics and the overtaking strategy (either flying or accelerative depending on whether the overtaking happened before or after the oncoming vehicle had passed) were analysed. The results showed that, both when the cyclist rode closer to the centre of the lane and when the time gap to the oncoming vehicle was shorter, safety margins for all potential collisions decreased. Under these conditions, drivers-particularly female drivers-preferred accelerative over flying manoeuvres. Bayesian statistics modelled these results to inform the development of active safety systems that can support drivers in safely overtaking cyclists.

How do drivers overtake pedestrians? Evidence from field test and naturalistic driving data.

For pedestrians, the risk of dying in a traffic accident is highest on rural roads, which are often characterized by a lack of sidewalks and high traffic speed. In fact, hitting the pedestrian during an overtaking attempt is a common crash scenario. To develop active safety systems that avoid such crashes, it is necessary to understand and model driver behavior during the overtaking maneuvers, so that system interventions are acceptable because they happen outside drivers' comfort zone. Previous modeling of driver behavior in interactions with pedestrians primarily focused on road crossing scenarios. The aim of this study was, instead, to address pedestrian-overtaking maneuvers on rural roads. We focused our analysis on how drivers adjust their behavior with respect to three safety metrics (in order of importance): 1) minimum lateral clearance when passing the pedestrian, 2) overtaking speed at that moment, and 3) the time-to-collision at the moment of steering away to start the overtaking maneuver. The influence of three factors on the safety metrics was investigated: 1) walking direction (same as the overtaking vehicle or opposite), 2) walking position (on the edge of the vehicle lane or 0.5 m away from the edge on the paved shoulder), and 3) oncoming traffic (absent or present). Seventy-seven overtaking maneuvers in France from the naturalistic driving study UDRIVE and 297 maneuvers in Sweden from field tests were analyzed. Bayesian regression was used to model how minimum lateral clearance and overtaking speed depended on the three factors. Results showed that drivers maintained smaller minimum lateral clearance and lower overtaking speed when the pedestrian was walking in the opposite direction, on the lane edge, or when oncoming traffic was present. Minimum lateral clearance and time-to-collision were only weakly correlated with overtaking speed. The regression models predicted distributions similar to those actually observed in the data. The time-to-collision at the moment of steering away was comparable in value to the time-to-collision used by Euro NCAP for testing active safety systems in car-to-pedestrian longitudinal scenarios since 2018. This study is the first to analyze driver behavior when overtaking pedestrians, based on field test and naturalistic driving data. Results suggest that pedestrian safety is particularly endangered in situations when the pedestrian is walking opposite to traffic, close to the lane, and when oncoming traffic is present. The Bayesian regression models from this study can be used in active safety systems to model drivers' comfort in overtaking maneuvers.

Multitasking additional-to-driving: Prevalence, structure, and associated risk in SHRP2 naturalistic driving data.

OBJECTIVE: This paper 1) analyzes the extent to which drivers engage in multitasking additional-to-driving (MAD) under various conditions, 2) specifies odds ratios (ORs) of crashing associated with MAD, and 3) explores the structure of MAD. METHODS: Data from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) was analyzed to quantify the prevalence of MAD in normal driving as well as in safety-critical events of various severity level and compute point estimates and confidence intervals for the corresponding odds ratios estimating the risk associated with MAD compared to no task engagement. Sensitivity analysis in which secondary tasks were re-defined by grouping similar tasks was performed to investigate the extent to which ORs are affected by the specific task definitions in SHRP2. A novel visual representation of multitasking was developed to show which secondary tasks co-occur frequently and which ones do not. RESULTS: MAD occurs in 11 % of control driving segments, 22 % of crashes and near-crashes (CNC), 26 % of Level 1-3 crashes and 39 % of rear-end striking crashes, and 9 %, 16 %, 17 % and 28 % respectively for the same event types if MAD is defined in terms of general task groups. The most common co-occurrences of secondary tasks vary substantially among event types; for example, "Passenger in adjacent seat - interaction" and "Other non-specific internal eye glance" tend to co-occur in CNC but tend not to co-occur in control driving segments. The odds ratios of MAD using SHRP2 task definitions compared to driving without any secondary task and the corresponding 95 % confidence intervals are 2.38 (2.17-2.61) for CNC, 3.72 (3.11-4.45) for Level 1-3 crashes and 8.48 (5.11-14.07) for rear-end striking crashes. The corresponding ORs using general task groups to define MAD are slightly lower at 2.00 (1.80-2.21) for CNC, 3.03 (2.48-3.69) for Level 1-3 crashes and 6.94 (4.04-11.94) for rear-end striking crashes. CONCLUSIONS: The number of secondary tasks that the drivers were engaged in differs substantially for different event types. A graphical representation was presented that allows mapping task prevalence and co-occurrence within an event type as well as a comparison between different event types. The ORs of MAD indicate an elevated risk for all safety-critical events, with the greatest increase in the risk of rear-end striking crashes. The results are similar independently of whether secondary tasks are defined according to SHRP2 or general task groups. The results confirm that the reduction of driving performance from MAD observed in simulator studies is manifested in real-world crashes as well.

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

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.

Definition of run-off-road crash clusters-For safety benefit estimation and driver assistance development.

Single-vehicle run-off-road crashes are a major traffic safety concern, as they are associated with a high proportion of fatal outcomes. In addressing run-off-road crashes, the development and evaluation of advanced driver assistance systems requires test scenarios that are representative of the variability found in real-world crashes. We apply hierarchical agglomerative cluster analysis to define similarities in a set of crash data variables, these clusters can then be used as the basis in test scenario development. Out of 13 clusters, nine test scenarios are derived, corresponding to crashes characterised by: drivers drifting off the road in daytime and night-time, high speed departures, high-angle departures on narrow roads, highways, snowy roads, loss-of-control on wet roadways, sharp curves, and high speeds on roads with severe road surface conditions. In addition, each cluster was analysed with respect to crash variables related to the crash cause and reason for the unintended lane departure. The study shows that cluster analysis of representative data provides a statistically based method to identify relevant properties for run-off-road test scenarios. This was done to support development of vehicle-based run-off-road countermeasures and driver behaviour models used in virtual testing. Future studies should use driver behaviour from naturalistic driving data to further define how test-scenarios and behavioural causation mechanisms should be included.

Modelling how drivers respond to a bicyclist crossing their path at an intersection: How do test track and driving simulator compare?

Bicyclist fatalities are a great concern in the European Union. Most of them are due to crashes between motorized vehicles and bicyclists at unsignalised intersections. Different countermeasures are currently being developed and implemented in order to save lives. One type of countermeasure, active safety systems, requires a deep understanding of driver behaviour to be effective without being annoying. The current study provides new knowledge about driver behaviour which can inform assessment programmes for active safety systems such as Euro NCAP. This study investigated how drivers responded to bicyclists crossing their path at an intersection. The influences of car speed and cyclist speed on the driver response process were assessed for three different crossing configurations. The same experimental protocol was tested in a fixed-base driving simulator and on a test track. A virtual model of the test track was used in the driving simulator to keep the protocol as consistent as possible across testing environments. Results show that neither car speed nor bicycle speed directly influenced the response process. The crossing configuration did not directly influence the braking response process either, but it did influence the strategy chosen by the drivers to approach the intersection. The point in time when the bicycle became visible (which depended on the car speed, the bicycle speed, and the crossing configuration) and the crossing configuration alone had the largest effects on the driver response process. Dissimilarities between test-track and driving-simulator studies were found; however, there were also interesting similarities, especially in relation to the driver braking behaviour. Drivers followed the same strategy to initiate braking, independent of the test environment. On the other hand, the test environment affected participants' strategies for releasing the gas pedal and regulating deceleration. Finally, a mathematical model, based on both experiments, is proposed to characterize driver braking behaviour in response to bicyclists crossing at intersections. This model has direct implications on what variables an in-vehicle safety system should consider and how tests in evaluation programs should be designed.

Counterfactual simulations applied to SHRP2 crashes: The effect of driver behavior models on safety benefit estimations of intelligent safety systems.

As the development and deployment of in-vehicle intelligent safety systems (ISS) for crash avoidance and mitigation have rapidly increased in the last decades, the need to evaluate their prospective safety benefits before introduction has never been higher. Counterfactual simulations using relevant mathematical models (for vehicle dynamics, sensors, the environment, ISS algorithms, and models of driver behavior) have been identified as having high potential. However, although most of these models are relatively mature, models of driver behavior in the critical seconds before a crash are still relatively immature. There are also large conceptual differences between different driver models. The objective of this paper is, firstly, to demonstrate the importance of the choice of driver model when counterfactual simulations are used to evaluate two ISS: Forward collision warning (FCW), and autonomous emergency braking (AEB). Secondly, the paper demonstrates how counterfactual simulations can be used to perform sensitivity analyses on parameter settings, both for driver behavior and ISS algorithms. Finally, the paper evaluates the effect of the choice of glance distribution in the driver behavior model on the safety benefit estimation. The paper uses pre-crash kinematics and driver behavior from 34 rear-end crashes from the SHRP2 naturalistic driving study for the demonstrations. The results for FCW show a large difference in the percent of avoided crashes between conceptually different models of driver behavior, while differences were small for conceptually similar models. As expected, the choice of model of driver behavior did not affect AEB benefit much. Based on our results, researchers and others who aim to evaluate ISS with the driver in the loop through counterfactual simulations should be sure to make deliberate and well-grounded choices of driver models: the choice of model matters.