Modeling Lead-vehicle Kinematics For Rear-end Crash Scenario Generation

The use of virtual safety assessment as the primary method for evaluating vehicle safety technologies has emphasized the importance of crash scenario generation. One of the most common crash types is the rear-end crash, which involves a lead vehicle and a following vehicle. Most studies have focused on the following vehicle, assuming that the lead vehicle maintains a constant acceleration/deceleration before the crash. However, there is no evidence for this premise in the literature. This study aims to address this knowledge gap by thoroughly analyzing and modeling the lead vehicle's behavior as a first step in generating rear-end crash scenarios. Accordingly, the study employed a piecewise linear model to parameterize the speed profiles of lead vehicles, utilizing two rear-end pre-crash/near-crash datasets. These datasets were merged and categorized into multiple sub-datasets; for each one, a multivariate distribution was constructed to represent the corresponding parameters. Subsequently, a synthetic dataset was generated using these distribution models and validated by comparison with the original combined dataset. The results highlight diverse lead-vehicle speed patterns, indicating that a more accurate model, such as the proposed piecewise linear model, is required instead of the conventional constant acceleration/deceleration model. Crashes generated with the proposed models accurately match crash data across the full severity range, surpassing existing lead-vehicle kinematics models in both severity range and accuracy. By providing more realistic speed profiles for the lead vehicle, the model developed in the study contributes to creating realistic rear-end crash scenarios and reconstructing real-life crashes

Exploring Turn Signal Usage Patterns in Lane Changes: A Bayesian Hierarchical Modelling Analysis of Realistic Driving Data

Using turn signals to convey a driver's intention to change lanes provides a direct and unambiguous way of communicating with nearby drivers. Nonetheless, past research has indicated that drivers may not always use their turn signals prior to starting a lane change. In this study, we analyze realistic driving data to investigate turn signal usage during lane changes on highways in and around Gothenburg, Sweden. We examine turn signal usage and identify factors that have an influence on it by employing Bayesian hierarchical modelling (BHM). The results showed that a turn signal was used in approximately 60% of cases before starting a lane change, while it was only used after the start of a lane change in 33% of cases. In 7% of cases, a turn signal was not used at all. Additionally, the BHM results reveal that various factors influence turn signal usage. The study concludes that understanding the factors that affect turn signal usage is crucial for improving traffic safety through policy-making and designing algorithms for autonomous vehicles for future mixed traffic

Driver Performance and Workload Using a Night Vision System

Night vision systems (NVS) have the potential to improve the visibility of critical objects at night beyond the levels achievable with low-beam headlamps. This could be especially valuable for older drivers, who have difficulty seeing at night and who are sensitive to glare. However, this benefit may also be accompanied by ancillary costs, such as the additional workload involved with monitoring and interpreting the forward view depicted by the NVS. In this study, we asked young and old subjects to drive at night on a test track while we measured distance and accuracy of target detection, subjective workload, and longitudinal control of the vehicle. In some conditions, direct view of the road was supplemented by a far-infrared NVS with two display configurations: a head-up display mounted above the dashboard, and a head-down display mounted near the vehicle midline. Night vision systems increased target detection distance for both young and old drivers, with noticeably more benefit for younger drivers. Although workload measures did not differ between the unassisted visual detection task and the NVS-assisted tasks, they were greater when driving with a detection task than without

Using Manual Measurements on Event Recorder Video and Image Processing Algorithms to Extract Optical Parameters and Range

Vehicle kinematics and optical parameters such as optical angle, optical expansion rate, and tau are thought to underlie drivers’ ability to avoid and handle critical traffic situations. Analyses of these parameters in naturalistic driving data with video, such as commercial event recordings of near-crashes and crashes, can provide insight into driver behavior in critical traffic situations. This paper describes a pair of methods, one for the range to a lead vehicle and one for its optical angle, that are derived from image processing mathematics and that provide driver behavior researchers with a relatively simple way to extract optical parameters from video-based naturalistic data when automatic image processing is not possible. The methods begin with manual measurements of the size of other road users on a video on a screen. To develop the methods, 20 participants manually measured the width of a lead vehicle on 14 images where the lead vehicle was placed at different distances from the camera. An on-market DriveCam Event Recorder was used to capture these images. A linear model that corrects distortion and modeling optics was developed to transform the on-screen measurements distance (range) to and optical angle of the vehicle. The width of the confidence interval for predicted range is less than 0.1m when the actual distance is less than 10m and the lead-vehicle width estimate is correct. The methods enable driver behavior researchers to easily and accurately estimate useful kinematic and optical parameters from videos (e.g., of crashes and nearcrashes) in event-based naturalistic driving data

A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies

Driver braking behavior was analyzed using time-series recordings from naturalistic rear-end conflicts (116 crashes and 241 near-crashes), including events with and without visual distraction among drivers of cars, heavy trucks, and buses. A simple piecewise linear model could be successfully fitted, per event, to the observed driver decelerations, allowing a detailed elucidation of when drivers initiated braking and how they controlled it. Most notably, it was found that, across vehicle types, driver braking behavior was strongly dependent on the urgency of the given rear-end scenario’s kinematics, quantified in terms of visual looming of the lead vehicle on the driver’s retina. In contrast with previous suggestions of brake reaction times (BRTs) of 1.5 s or more after onset of an unexpected hazard (e.g., brake light onset), it was found here that braking could be described as typically starting less than a second after the kinematic urgency reached certain threshold levels, with even faster reactions at higher urgencies. The rate at which drivers then increased their deceleration (towards a maximum) was also highly dependent on urgency. Probability distributions are provided that quantitatively capture these various patterns of kinematics-dependent behavioral response. Possible underlying mechanisms are suggested, including looming response thresholds and neural evidence accumulation. These accounts argue that a naturalistic braking response should not be thought of as a slow reaction to some single, researcher-defined “hazard onset”, but instead as a relatively fast response to the visual looming cues that build up later on in the evolving traffic scenario

An invariant may drive the decision to encroach at unsignalized intersections

This paper introduces a novel approach to understanding when and where drivers make the Go / No Go decision (not) to turn left and encroach upon an approaching car that has the right-of-way in an unsignalized intersection. The source of data is approximately 2,400 hours of video recordings at two intersections near Göteborg, Sweden. Automated image processing software extracted the trajectories of the pairs of cars involved in more than 14,000 left turns across traffic at the first intersection and 2,400 at the second. We subdivided the data into four different left-turn scenarios - where the approaching car arrives from the opposite direction, from the lateral direction, from the intended direction (merging), and while making its own left turn. For each scenario, we found the distances between the turning car and the approaching car at the time when we can assume the decision (not) to turn is made and conducted logistic regressions to identify the distances associated with the 50/50 acceptance probabilities for the decision (not) to turn. We also calculated the resulting encroachment distances (‘trailing buffers’) for every decision to turn. We expected to find wide variability in these buffers. Instead, we observed separations that were virtually the same across scenarios at each intersection but differed across intersections. Tacit, intersection-dependent knowledge of this invariant may drive the decision of whether or not to turn and encroach. We discuss the implications this finding has for the design of in-vehicle active safety systems

Quantitative Driver Behavior Modelling forActive Safety Assessment Expansion (QUADRAE)

In-vehicle technologies are essential for vehicle safety. This project, Quantitative Driver Behavior Modeling for Active Safety Assessment Expansion (QUADRAE), addresses two crucial components of the technology development process: driver models and simulation methodology. Together, they have provided the industrial partners with state-of-the-art tools for system development and testing, facilitating the development of innovative technologies to improve traffic safety. The main objectives of the project were to: develop and validate models of driver behavior needed in current and future simulation tools for virtual testing of active safety and automation carry out prioritized virtual tests to estimate the safety benefit of a system, tune system parameters, and explore potential outcomes in scenarios when the system is active learn more about the best methods for performing virtual testing using driver models As a result of the project, the partners now have an established virtual simulation framework using Predictive Processing (PP) as a general paradigm for modeling driver behavior. The modeling, based on the latest knowledge and ideas about human behavior in driving, draws on extensive research using volunteer drivers as study participants. Data from both controlled experiments and naturalistic driving were used to develop and validate the models. These models are already being used by the industry partners as part of their virtual safety assessment toolchain, to develop advanced driver support systems. The data will continue to be used by the project partners in industry and academia to develop future driver models (which will, in turn, foster improved driver support systems)

Driver Distraction and Inattention

In UDRIVE, the major focus of the work on driver inattention and distraction has been focused on obtaining a better understanding of whether and how drivers manage their secondary task activities — when they choose to engage, what tasks they select, whether they adjust their activity to different situations and whether they are willing to surrender secondary task activities as the primary task of driving becomes more demanding. In other words, the focus is on self-regulation, on how drivers manage their secondary task activity in the context of the dynamics of the traffic and road situation. That management includes the determination not to engage in such tasks in the first place or only to engage in some particular activities. NDS are particularly suited to such an investigation, since experimental studies in driving simulators and even on test tracks tend to suffer from an instruction effect, in that participants are typically instructed to carry out an activity at a given moment. Thus such experimental studies provide insight into how driver attention, driver information processing and driving performance are affected by secondary tasks, but are less useful when research is focused on driver management of task activity