Driver Braking Behavior Research Articles

Effects of driver’s braking behavior by the real-time pedestrian scale warning system

A driver warning system can improve pedestrian safety by providing drivers with alerts about potential hazards. Most driver warning systems have primarily focused on detecting the presence of pedestrians, without considering other factors, such as the pedestrian’s gender and speed, and whether pedestrians are carrying luggage, that can affect driver braking behavior. Therefore, this study aims to investigate how driver braking behavior changes based on the information about the number of pedestrians in a crowd and examine if a developed warning system based on this information can induce safe braking behavior. For this purpose, an experiment scenario was conducted using a virtual reality-based driving simulator and an eye tracker. The collected driver data were analyzed using mixed ANOVA to derive meaningful conclusions. The research findings indicate that providing information about the number of pedestrians in a crowd has a positive impact on driver braking behavior, including deceleration, yielding intention, and attention. Particularly, It was found that in scenarios with a larger number of pedestrians, the Time to Collision (TTC) and distance to the crosswalk were increased by 12%, and the pupil diameter was increased by 9%. This research also verified the applicability of the proposed warning system in complex road environments, especially under conditions with poor visibility such as nighttime. The system was able to induce safe braking behavior even at night and exhibited consistent performance regardless of gender. In conclusion, considering various factors that influence driver behavior, this research provides a comprehensive understanding of the potential and effectiveness of a driver warning system based on information about the number of pedestrians in a crowd in complex road environments.

Impact on driver behavior from ERTMS speed-filtering

In the signal planning process, the ERTMS speed profiles based on track nature needs to be complemented with additional constraints. The base profile resolving the allowed track speed, often includes several speed changes which can be difficult for the train driver to follow. One approach to deal with this problem is to filter the base profile, reducing the number of changes and giving the driver more time for attention.This paper presents the effects of a speed profile filtering principle, based on possible time usage during a speed increase, on train energy consumption, train driver braking behavior, running time, and driver workload. In an Electrical Multiple Unit train driver simulator, 40 drivers tested three different speed profiles of a 19 km railway line. It can be concluded that differences in running time are small, that these small time-gains implies a high energy consumption cost, and that drivers tend to drive close to the indication braking curve in the beginning of the braking phase. Further, the drivers rated the driver task workload low for all filter conditions. Accordingly, a certain filter level is required to get capacity and energy effects.

A Study on Human-like Deceleration considering Static Objectives for One-pedal Driving of Electric Vehicles

One-pedal driving is a user-friendly feature, particularly in electric vehicles, allowing for simple, energy-efficient, comfortable, and safe driving. In this study, we propose a simple and effective method to generate a human-like braking speed profile in the presence of a red traffic light or a stop sign. This human-like braking profile can serve as a reference profile of a human driver at a given initial speed and distance-to-stop. The proposed method is developed based on four-phase braking and vehicle longitudinal dynamics. The method utilizes parameters that describe the braking behavior of individual drivers, such as the braking time for a given initial speed and distance-to-stop, the minimum acceleration, the minimum jerk, and the maximum jerk during the braking. These parameters are identified from real-world driving data. The results show that the proposed method successfully captures a human driver's braking behavior when coming to a full stop without a preceding vehicle. The median RMSEs of the speed profile and median distance errors from all vehicles are 0.0075 m and 0.789m/s on average.

Personalized Driver Braking Behavior Modeling in the Car-Following Scenario: An Importance-Weight-Based Transfer Learning Approach

Accurately recognizing braking intensity levels (BIL) of drivers is important for guaranteeing the safety and avoiding traffic accidents in intelligent transportation systems. In this article, an instance-level transfer learning framework is proposed to recognize BIL for a new driver with insufficient driving data by combining the Gaussian mixture model (GMM) and the importance-weighted least-squares probabilistic classifier (IWLSPC). By considering the statistic distribution, GMM is applied to cluster the data of braking behaviors into three levels with different intensities. With the density ratio calculated by unconstrained least-squares importance fitting, the least-squares probabilistic classifier is modified as IWLSPC to transfer the knowledge from one driver to another and recognize BIL for a new driver with insufficient driving data. Comparative experiments with nontransfer methods indicate that the proposed framework obtains a higher accuracy in recognizing BIL in the car-following scenario, especially when sufficient data are not available.

Limitations and Improvements of the Intelligent Driver Model (IDM)

This contribution analyzes the widely used and well-known "intelligent driver model (briefly IDM), which is a second order car-following model governed by a system of ordinary differential equations. Although this model was intensively studied in recent years for properly capturing traffic phenomena and driver braking behavior, a rigorous study of the well-posedness has, to our knowledge, never been performed. First it is shown that, for a specific class of initial data, the vehicles' velocities become negative or even diverge to $-\infty$ in finite time, both undesirable properties for a car-following model. Various modifications of the IDM are then proposed in order to avoid such ill-posedness. The theoretical remediation of the model, rather than post facto by ad-hoc modification of code implementations, allows a more sound numerical implementation and preservation of the model features. Indeed, to avoid inconsistencies and ensure dynamics close to the one of the original model, one may need to inspect and clean large input data, which may result in practically impossible scenarios for large-scale simulations. Although well-posedness issues occur only for specific initial data, this may happen frequently when different traffic scenarios are analyzed, and especially in presence of lane-changing, on ramps and other network components as it is the case for most commonly used micro-simulators. On the other side, it is shown that well-posedness can be guaranteed by straight-forward improvements, such as those obtained by slightly changing the acceleration to prevent the velocity from becoming negative.

Influence of red-light violation warning systems on driver behavior – a driving simulator study

Objective: With the advent of connected and automated vehicle (CAV) technology, there is an increasing need to evaluate driver behavior with the advent of connected and automated vehicle (CAV) technology. This study introduced a red-light violation warning (RLVW) system using CAV technology in a driving simulator environment, to evaluate driver braking behavior when the light changes suddenly from green to yellow.Methods: Researchers recruited a total of 93 participants from diverse socio-economic backgrounds for this study and created a virtual network of downtown Baltimore. An eye tracking device was used to observe distractions and head movements. A Lognormal accelerated failure time (AFT) distribution model was used for this analysis, to calculate speed reduction times from the moment the traffic light changes from green to yellow, to the point where a minimum speed was reached.Results: It was observed that speed reduction times were significantly higher in the presence of a RLVW system, requiring a longer period of time to come to a complete stop at the red light. Inferences can be drawn from the jerk analysis that, the RLVW system results in a highly unsafe jerk at the onset of the warning. Without the RLVW system though, a highly uncomfortable positive jerk occurs closer to the signal, which is due to sudden acceleration, as the participants possibly slowed down a lot initially. Gaze analysis showed that the system was able to attract the attention of the drivers, as the majority of the drivers noticed the displayed warning.Conclusions: The findings suggest that the presence of an RLVW system sends a clear message to the driver about the change in traffic light and gives the driver ample time to adapt their initial approach speed to stop at the signal, avoiding potential intersection crashes.

A Study on the Acceptability of the Elderly Driver according to the Assistant Method

According to traffic accident reports, many of the accidents that occur at intersections are caused by elderly drivers, and development of an assistant system that can prevent non-stopping accidents (i.e., accidents that occur by not stopping when required) is currently a pressing need. To develop an anti-stopping alert, it has been assumed that an alarm that informs the driver about the impending approach of an intersection is effective. However, a uniform alarm system cannot be expected to influence the individual differences among elderly drivers. Then, audio-visual assistance systems that consist of a nudge that informs drivers about the existence of an intersection and of an approach alarm that follows the nudge, as well as of a display and a warning with sound and voice, were designed based on the timing when braking occurs and on a given elderly driver's braking behavior when approaching an intersection. In this research, considering an elderly driver's cognition, judgment capability, and individual driving abilities, and while investigating individual elderly drivers’ vision capabilities and judgment functions, a questionnaire survey on the posture and concerns about driving was conducted in advance. Subsequently, using the driving simulator of a city road, the driving behavior of an elderly driver from braking behavior to stopping performance was observed; furthermore, driving behavioral changes following the proposed driving assistance alarm were analyzed. The result of our analysis demonstrated that elderly drivers differed in their response to the assistance alarm according to their cognition and judgment capabilities, their experience, and driving style. The proposed system’s effectiveness was apparent when the alarm was adapted to each individual’s capabilities.

A forward collision avoidance algorithm based on driver braking behavior

Measuring risk is critical for collision avoidance. The paper aims to develop an online risk level classification algorithm for forward collision avoidance systems. Assuming risk levels are reflected by braking profiles, deceleration curves from critical evasive braking events from the Virginia “100-car” database were first extracted. The curves are then clustered into different risk levels based on spectrum clustering, using curve distance and curve changing rate as dissimilarity metrics among deceleration curves. Fuzzy logic rules of safety indicators at critical braking onset for risk classification were then extracted according to the clustered risk levels. The safety indicators include time to collision, time headway, and final relative distance under emergency braking, which characterizes three kinds of uncertain critical conditions respectively. Finally, the obtained fuzzy risk level classification algorithm was tested and compared with other Automatic Emergency Braking (AEB) algorithms under Euro-NCAP testing scenarios in simulation. Results show the proposed algorithm is promising in balancing the objectives of avoiding collision and reducing interference with driver’s normal driving compared with other algorithms.

Learning Driver Braking Behavior Using Smartphones, Neural Networks and the Sliding Correlation Coefficient: Road Anomaly Case Study

This paper focuses on the automated learning of driver braking “signature” in the presence of road anomalies. Our motivation is to improve driver experience using preview information from navigation maps. Smartphones facilitate, due to their unprecedented market penetration, the large-scale deployment of advanced driver assistance systems. On the other hand, it is challenging to exploit smartphone sensor data because of the fewer and lower quality signals, compared to the ones on board. Methods for detecting braking behavior using smartphones exist, however, most of them focus only on harsh events. Additionally, only a few studies correlate longitudinal driving behavior with the road condition. In this paper, a new method, based on deep neural networks and the sliding correlation coefficient, is proposed for the spatio-temporal correlation of road anomalies and driver behavior. A unique deep neural network structure, that requires minimum tuning, is proposed. Extensive field trials were conducted and vehicle motion was recorded using smartphones and a data acquisition system, comprising an inertial measurement unit and differential GPS. The proposed method was validated using the probabilistic Receiver Operating Characteristics method. The method proves to be a robust and flexible tool for self-learning driver behavior.

Driver braking behavior analysis to improve autonomous emergency braking systems in typical Chinese vehicle-bicycle conflicts

Bicycling is one of the fundamental modes of transportation especially in developing countries. Because of the lack of effective protection for bicyclists, vehicle-bicycle (V-B) accident has become a primary contributor to traffic fatalities. Although AEB (Autonomous Emergency Braking) systems have been developed to avoid or mitigate collisions, they need to be further adapted in various conflict situations. This paper analyzes the driver’s braking behavior in typical V-B conflicts of China to improve the performance of Bicyclist-AEB systems. Naturalistic driving data were collected, from which the top three scenarios of V-B accidents in China were extracted, including SCR (a bicycle crossing the road from right while a car is driving straight), SCL (a bicycle crossing the road from left while a car is driving straight) and SSR (a bicycle swerving in front of the car from right while a car is driving straight). For safety and data reliability, a driving simulator was employed to reconstruct these three scenarios and some 25 licensed drivers were recruited for braking behavior analysis. Results revealed that driver’s braking behavior was significantly influenced by V-B conflict types. Pre-decelerating behaviors were found in SCL and SSR conflicts, whereas in SCR the subjects were less vigilant. The brake reaction time and brake severity in lateral V-B conflicts (SCR and SCL) was shorter and higher than that in longitudinal conflicts (SSR). The findings improve their applications in the Bicyclist-AEB and test protocol enactment to enhance the performance of Bicyclist-AEB systems in mixed traffic situations especially for developing countries.

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.

Driver's braking behavior approaching pedestrian crossings: a parametric duration model of the speed reduction times

SummaryThe driver's braking behavior while approaching zebra crossings under different safety measures (curb extensions, parking restrictions, and advance yield markings) and without treatment (baseline condition) was examined. The speed reduction time was the variable used to describe the driver's behavior. Forty‐two drivers drove a driving simulator on an urban scenario in which the baseline condition and the safety measures were implemented. The speed reduction time was modeled with a parametric duration model to compare the effects on driver's braking behavior of vehicle dynamic variables and different countermeasures. The parametric accelerated failure time duration model with a Weibull distribution identified that the vehicle dynamic variables and only the countermeasure curb extensions affected, in a statistically significant way, the driver's speed reduction time in response to a pedestrian crossing.This result shows that the driver, because of the improved visibility of the pedestrian allowed by the curb extensions, was able to receive a clear information and better to adapt his approaching speed to yield to the pedestrian, avoiding abrupt maneuvers. This also means a reduction of likelihood of rear‐end collision due to less aggressive braking. Copyright © 2016 John Wiley & Sons, Ltd.

Operational Effects of Transverse Rumble Strips on Approaches to High-Speed Intersections

The objective of this study was to determine whether the application of transverse rumble strips was an effective warning device for drivers approaching high-speed intersections. Performance was evaluated for attention-getting characteristics, driver speed choice, and braking behavior. Five intersection approaches on US-280 in Alabama were selected and monitored during both daytime and nighttime conditions. Acoustic and tactile signatures were measured by a specially equipped passenger car under different speed ranges. To examine effectiveness on speed changes, vehicle mean speeds were recorded by radar measurements along the approach to the intersections: 400 ft (122 m) upstream of the transverse rumble strips, right before the transverse rumble strips, and right after the transverse rumble strips. Video cameras were also in place to survey the brake lights of vehicles driving over the transverse rumble strips to quantify driver behavior. An analysis of variance F-test and Tukey’s honest significance test revealed that the strips created a recognizable amount of interior noise and vibration when an automobile crossed over the transverse rumble strips. The installation of transverse rumble strips generally produced statistically significant ( p ≤ .05) reductions in approach speeds. During daytime and at night, drivers drove at lower speeds through the intersections compared with the initial upstream speed. The transverse rumble strips also exerted certain effects on driver braking behavior.

Driver-Related Delay in Emergency Braking Response to a Laterally Incurring Hazard

Collision analysis often assumes emergency deceleration begins immediately upon completion of the vehicle’s mechanical brake lag. The goal of this study is to determine the driver-related delay from initial brake application to various degrees of deceleration in a simulated emergency and to test variables contributing to the modulation of driver braking. Using the data of Mazzae et al (2003), in which drivers respond to a lateral vehicle incursion, we have assessed the contribution of Time-to-Intersection (TTI), road condition, gender and crash outcome on driver emergency brake response. In the first 0.3 second phase after initial brake application, vehicle behavior was similar across all variables as drivers reached only moderate levels of deceleration. In the second phase, drivers often took more than one second to reach emergency decelerations, especially with a longer TTI. Pavement condition, gender and crash outcome were not significant factors. We discuss the consequences of driver braking behavior in the context of driver feedback and accident reconstruction analyses.

Driver Braking Performance to Surprise and Expected Events

Meta-analyses have shown that the driver braking characteristics reported in the literature often vary due to differences in study methodologies (Green, 2000; Muttart, 2005; Summala, 2000). This paper provides additional insight on driver braking performance by 1) characterizing driver behavior during the execution of surprise and expected braking maneuvers, and 2) investigating the effect of gender, age, and vehicle driven on this behavior. Sixty-four drivers performed surprise and expected braking maneuvers from 45 mph (72.4 km/h) at an inflatable barricade, and subsequently performed expected braking maneuvers in response to an auditory alarm. Participants drove one of two instrumented vehicles: 1) a 2006 Mercedes-Benz R350, or 2) a 2007 Volvo S80. Drivers' braking inputs and corrected stopping distance were measured. Results indicate that drivers' braking performance varied by gender, age, and vehicle driven. These results and their relationship to the extant braking literature are discussed. The findings are expected to benefit those involved in crash reconstruction, researchers modeling driver braking behavior, as well as designers of brake-by-wire systems.

The Influence of Lead Vehicle Behavior and Vehicle Rates of Closure on a Driver's Braking Behavior

Given the great prevalence of vehicle accidents, collision prevention should be a priority for ground transportation research. The understanding of such incidents is critical to the safety of the driver. This study involved simulations of multiple driving situations variant on Rate of Closure (the relative speed between two vehicles) and Lead Vehicle Behavior. Useful Field of View and Test Anxiety measures were used to analyze additional factors related to driving with little success. The findings show that brake onset times of younger drivers are significantly related to the behavior of a lead vehicle and to the rate of closure. As such, any system that only uses time to contact or distance to contact is fundamentally flawed. These results aid in the general understanding of driver behavior and could fundamentally change automatic braking systems.

An Analysis of Driver's Behaviour during the System Limits (A Study Regarding the Warning Timing of System Limits for the Designing of Low Speed Adaptive Cruise Control Systems)

We analyzed the driver's braking behavior during the system-limit-warning, meaning the system is inevitable to avoid the collision on the preceding vehicle, when the driver was using Low Speed Adaptive Cruise Control. It was observed that the drivers are judging the condition of system-limits, not only by themselves but also by the system-limit-warning. Driver's braking behavior was delayed when the assumed delay time was set at less than 1.2 s in the Stopping Distance Algorithm. We also analyzed the driver's braking behavior in the miss warning condition. Driver's braking behavior during the miss warning was extremely delayed when the assumed delay time was set at less than 1.0 s. In the designing of warning timing by use of Stopping Distance Algorithm, one of the optimal warning timing is 1.2s, when we focus on not only the normal condition that the warning system operate accurately but also the miss warning condition.