Population distributions of time to collision at brake application during car following from naturalistic driving data

Application of naturalistic driving data: A systematic review and bibliometric analysis

The application of naturalistic driving data (NDD) has the potential to answer critical research questions in the area of driving behavior assessment, as well as the impact of exogenous and endogenous factors on driver safety. However, the presence of a large number of research domains and analysis foci makes a systematic review of NDD applications challenging in terms of information density and complexity. While previous research has focused on the execution of naturalistic driving studies and on specific analysis techniques, a multifaceted aggregation of NDD applications in Intelligent Transportation System (ITS) research is still unavailable. In spite of the current body of work being regularly updated with new findings, evolutionary nuances in this field remain relatively unknown. To address these deficits, the evolutionary trend of NDD applications was assessed using research performance analysis and science mapping. Subsequently, a systematic review was conducted using the keywords "naturalistic driving data" and "naturalistic driving study data". As a result, a set of 393 papers, Published between January 2002-March 2022, was thematically clustered based on the most common application areas utilizing NDD. the results highlighted the relationship between the most crucial research domains in ITS, where NDD had been incorporated, and application areas, modeling objectives, and analysis techniques involving naturalistic databases.

A novel traffic conflict risk measure considering the effect of vehicle weight

INTRODUCTION

Vehicle weight is deterministic to the impact force in collision, and thus the injury risk of vehicle occupants. In China, involvement of heavy vehicles in overall and fatal crashes are prevalent, even though heavy vehicles only constitute a small proportion of overall registered motor vehicles. However, vehicle weight is rarely considered in the existing traffic conflict risk prediction and assessment models because of the unavailability of required data.

METHOD

Novel risk indicators for the diagnosis of traffic conflict risk map, considering the effect of vehicle weight, are proposed, with the advantage of comprehensive traffic flow characteristics and vehicle weight data using Weigh-in-Motion (WIM) technique. Weight-incorporated risk level (WRL) and weight integrated risk level (WIRL) are established to quantify the traffic conflict risk, at an instant and over a specified time period, respectively, by extending the conventional traffic conflict risk measures including time-to-collision (TTC) and modified potential collision energy (PCE). Then, a microscopic traffic simulation model is adopted to estimate the traffic conflict risk map along a highway segment that has partial lane closure. The traffic conflict risk performances, between the risk indicators with and without considering the vehicle weight, are compared.

RESULTS

The traffic conflict risks estimated using conventional risk indicators without considering the vehicle weight are generally lower than that based on WRL and WIRL. The difference is more profound when the proportion of heavy vehicles in the traffic stream increases.

CONCLUSIONS

The finding is indicative to remedial engineering measures including variable message sign, speed limit, and ramp metering that can mitigate the real-time crash risks on highways, especially in adverse environmental and weather conditions, with due consideration of vehicle composition and crash worthiness of vehicles.

Characterization of the Driving Style by State–Action Semantic Plane Based on the Bayesian Nonparametric Approach

The quantification and estimation of the driving style are crucial to improve the safety on the road and the acceptance of drivers with level2–level3(L2–L3) intelligent vehicles. Previous studies have focused on identifying the difference in driving style between categories, without further consideration of the driving behavior frequency, duration proportion properties, and the transition properties between driving style and behaviors. In this paper, a novel methodology to characterize the driving style is proposed by using the State–Action semantic plane based on the Bayesian nonparametric approach, i.e., hierarchical Dirichlet process–hidden semi–Markov model (HDP–HSMM). This method segments the time series driving data into fragment clusters with similar characteristics and construct the State–Action semantic plane based on the statistical characteristics of the state and action layer to label and interpret the fragment clusters. This intuitively and simply visualizes the driving performance of individual drivers, while the risk index of the individual drivers can also be obtained through semantic plane. In addition, according to the joint mutual information maximization (JIMI) approach, seven transition probabilities of driving behaviors are extracted from the semantic plane and applied to identify driving styles of drivers. We found that the aggressive drivers prefer high–risk driving behaviors, and the total duration and frequency of high–risk behaviors are greater than those of cautious and normal drivers. The transition probabilities among high–risk driving behaviors are also greater compared with low–risk behaviors. Moreover, the transition probabilities can provide rich information about driving styles and can improve the classification accuracy of driving styles effectively. Our study has practical significance for the regulation of driving behavior and improvement of road safety and the development of advanced driver assistance systems (ADAS).

How Does Heterogeneity Affect Freeway Safety? A Simulation-Based Exploration Considering Sustainable Intelligent Connected Vehicles

Intelligent connected vehicles (ICVs) are recognized as a new sustainable transportation mode, which could be promising for reducing crashes. However, the mixed traffic consisting of manually driven vehicles and ICVs may negatively affect road safety due to individual heterogeneity. This study investigated heterogeneity effects on freeway safety-based simulation experiments. Two types of vehicle dynamic models were employed to depict dynamic behaviors of manually driven vehicles and adaptive cruise control (ACC) vehicles (a simplified version of ICVs), respectively. Real vehicle trajectories were utilized to calibrate model parameters based on genetic algorithms. Surrogate safety measures were applied to establish the relationship between vehicle behaviors and longitudinal collision risks. Simulation results indicate that the heterogeneity has negative effects on longitudinal safety. With the higher degree of heterogeneity, longitudinal collision risks are increased. Compared to traffic flow consisting of human drivers only, mixed traffic flow may be more dangerous when the market penetration rate of ACC is low, since the ACC system can be recognized as a new source of individual heterogeneity. Findings of this study show that necessary countermeasures should be developed to improve safety for mixed traffic flow from the perspective of transportation safety planning in the near future.

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.

Autonomous emergency braking systems adapted to snowy road conditions improve drivers' perceived safety and trust

OBJECTIVE

This study investigated drivers' evaluation of a conventional autonomous emergency braking (AEB) system on high and reduced tire-road friction and compared these results to those of an AEB system adaptive to the reduced tire-road friction by earlier braking. Current automated systems such as the AEB do not adapt the vehicle control strategy to the road friction; for example, on snowy roads. Because winter precipitation is associated with a 19% increase in traffic crashes and a 13% increase in injuries compared to dry conditions, the potential of conventional AEB to prevent collisions could be significantly improved by including friction in the control algorithm. Whereas adaption is not legally required for a conventional AEB system, higher automated functions will have to adapt to the current tire-road friction because human drivers will not be required to monitor the driving environment at all times. For automated driving functions to be used, high levels of perceived safety and trust of occupants have to be reached with new systems. The application case of an AEB is used to investigate drivers' evaluation depending on the road condition in order to gain knowledge for the design of future driving functions.

METHODS

In a driving simulator, the conventional, nonadaptive AEB was evaluated on dry roads with high friction (μ = 1) and on snowy roads with reduced friction (μ = 0.3). In addition, an AEB system adapted to road friction was designed for this study and compared with the conventional AEB on snowy roads with reduced friction. Ninety-six drivers (48 males, 48 females) assigned to 5 age groups (20-29, 30-39, 40-49, 50-59, and 60-75 years) drove with AEB in the simulator. The drivers observed and evaluated the AEB's braking actions in response to an imminent rear-end collision at an intersection.

RESULTS

The results show that drivers' safety and trust in the conventional AEB were significantly lower on snowy roads, and the nonadaptive autonomous braking strategy was considered less appropriate on snowy roads compared to dry roads. As expected, the adaptive AEB braking strategy was considered more appropriate for snowy roads than the nonadaptive strategy. In conditions of reduced friction, drivers' subjective safety and trust were significantly improved when driving with the adaptive AEB compared to the conventional AEB. Women felt less safe than men when AEB was braking. Differences between age groups were not of statistical significance.

CONCLUSIONS

Drivers notice the adaptation of the autonomous braking strategy on snowy roads with reduced friction. On snowy roads, they feel safer and trust the adaptive system more than the nonadaptive automation.

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 1.5s 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.