Driver Braking 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.

Comparison between traditional vacuum booster and electro-mechanical braking system

The Electro-Mechanical Braking System (EMB) is an innovation in the energy supply and transmission of the brake drive mechanism. It provides braking energy with a motor and transmits the driver's braking intention with an electrical signal, and the actuator is still a conventional brake. The automotive electronic mechanical system is in line with the three major themes of safety, energy saving, and environmental protection, and is the development direction of the future brake system. Its research is still in the experimental stage, and there is still some distance from the product launch. Therefore, by comparing the structure and principle of the traditional vacuum booster and the Electro-Mechanical brake system, this paper analyzes their respective advantages and disadvantages, and finally points out that under the current background, the research and development of the Electro-Mechanical brake system is still immature, because its structure is more compact and complex, it is more difficult to conduct simulation, and it still needs to increase research and development investment.

“Braking bad”: The influence of haptic feedback and tram driver experience on emergency braking performance

Trams are experiencing a resurgence with worldwide network expansion driven by the need for sustainable and efficient cities. Trams often operate in shared or mixed-traffic environments, which raise safety concerns, particularly in hazardous situations. This paper adopts an international, mixed-methods approach, conducted through two interconnected studies in Melbourne (Australia) and Birmingham (UK). The first study involved qualitative interviews, while the second was an experimental study involving a virtual reality (VR) simulator and haptic master controller (i.e., speed lever). In tram operations, master controllers play a critical role in ensuring a smooth ride, which directly influences passenger safety and comfort. The objective was to understand how a master control system, enhanced with additional haptic feedback, could improve tram driver braking performance and perceptions in safety-critical scenarios. Interview results indicate that the use of the emergency brake is considered the final or ultimate choice by drivers, and their driving experience is a moderating factor in limiting its application. Combined with the experimental results, this paper highlights how implementing haptic feedback within a master controller can reduce the performance disparity between novice and experienced tram drivers.

LaTAS-F: Locality-aware transformer architecture search with multi-source fusion for driver continuous braking intention inference

Precise inference of driver braking intention is highly correlated with traffic safety, energy consumption, and driving comfort of electrified vehicles (EVs). Until recently, gratifying results have been achieved in accurately inferring braking intention as discrete states, such as classify brake or no brake, and judge normal or emergency brake. However, accurately and quantitively predicting driver brake intensity is significant for enhancing vehicle safety, its related research is especially rarely seen. To mitigate this deficiency, a novel framework, locality-aware transformer architecture search with multi-source fusion (LaTAS-F), is put forward for continuous braking intention (CBI) inference in this paper. To consider the comprehensively complicated 'driver-vehicle' interactions, data composed by driver physical state and vehicle state are collected with multi-source sensors. A kernel principal component analysis (KPCA) method is proposed to fuse multi-source data while removing redundancies. In addition, an improved transformer with enhanced locality-aware attention mechanism (ELAM) is elaborately designed to alleviate the incapability of self-attention mechanism (SAM) in canonical transformer when capturing local context. In particular, due to the inefficiency of designing the transformer entirely by hand, a tree-structured parzen estimator (TPE) strategy is implemented to automatically design the most effective and practical model architecture. The performance of LaTAS-F framework is evaluated in various of historical and predicted horizons. Data is collected from 21 subjects, and experimental results demonstrate that our proposed framework can accurately predict CBI of 200 ms in the future, outperforming baselines with RMSE of 0.428Mpa and R2 of 0.963.

Recovery and Control Strategy of Electro-Hydraulic Composite Braking Energy for Electric Loader with Braking Intention Recognition

The loader has a lot of recoverable braking energy due to its larger mass and frequent starts/stops. For a 5-ton pure electric drive loader, an emergency braking intention recognition strategy based on hydraulic braking pressure was proposed. The braking intention recognition strategy of an acceleration pedal and brake pedal was used to distinguish different braking intentions, and the hydraulic braking system pressure was used as a feedback parameter for emergency braking intention recognition to improve braking safety. Aiming at electro-hydraulic composite braking mode switching, a collaborative control strategy of walking regenerative braking and mechanical braking is proposed. Simulation analysis by AMESim and vehicle test results show that the proposed control strategy can realize driver braking intention recognition and electro-hydraulic braking force distribution under different working conditions and improve braking smoothness. According to the calculation of the energy recovery effect evaluation index, the energy recovery efficiency is up to 71.64%, the braking recovery rate is above 42.50%, and the maximum energy saving for the whole vehicle is 7.58% under one cycle condition. The proposed strategy has a good energy-saving effect.

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.

Relationship between braking intensity and driver heart rate as a function of the size of the measurement window and its position

Heart Rate (HR) is often used to capture the driver's mental workload and stress. Usually, during a predefined time window (e.g., 10 s, 30 s, or longer). In addition, the center of that time window is commonly placed at the peak of events of interest (e.g., safety events such as hard braking). However, the choices of window size and window position relative to driving events are often not explained in the literature, and conceivably, other choices could have been made for estimating changes in HR. This research tested the effect of window size and position on HR estimates. Participants executed 12 braking events on a closed driving course and were monitored for HR and skin conductivity. We fitted a regression model on HR as a function of driver braking intensity, multiple times (N = 210), each time, we calculated the HR with different window sizes (1–70 s) and three window positions (onset of braking, peak deceleration, and end of braking). We found that the coefficients in the model on HR by braking intensity changed as a function of the window size and its position. Our sample's optimal time window size was different from conventional settings: it was 4-second long and centered at the end of the braking events. Our findings showcase the analysis procedure that we employed and can contribute to research on physiological markers (HR and others) and to the design of driver assistance systems.

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.

Pre-braking behaviors analysis based on Hilbert–Huang transform

Previous studies have shown that about 90% of traffic accidents are due to human error, which means that human factors may affect a driver's braking behaviors and thus their driving safety, especially when the driver makes a braking motion. However, most studies have mounted sensors on the brake pad, ignoring to some extent an analysis of the driver's behavior before the brake pad is pressed (pre-braking). Therefore, to determine the effect of different human factors on drivers' pre-braking behaviors, this study focused on analyzing drivers' local joints (knee, ankle, and toe) by a motion capture device. A Hilbert–Huang Transform (HHT)-based local human body movement analysis method was used to decompose the realistic complex pre-braking actions into sub-actions such as intrinsic mode functions (IMF1, IMF2, etc.). Analysis of the results showed that IMF1 is a common and necessary action when pre-braking for all drivers, and IMF2 may be the safety assurance action that allows right-foot transverse movement at the beginning part of the pre-braking process. We also found that the experienced, male, and Phys.50 groups may have consistent characteristics in the HHT scheme, which could mean that such drivers would have better performance and efficiency during the pre-braking process. The results of this study will be useful in decomposing and discerning the specific actions that lead to accidents, providing insights into driver training for novice drivers, and guiding the construction of daily automated driver assistance or accident prevention systems (advanced driver assistance systems, ADASs).

The Impact of Physical Motion Cues on Driver Braking Performance: A Clinical Study Using Driving Simulator and Eye Tracker.

Driving simulators are increasingly being incorporated by driving schools into a training process for a variety of vehicles. The motion platform is a major component integrated into simulators to enhance the sense of presence and fidelity of the driving simulator. However, less effort has been devoted to assessing the motion cues feedback on trainee performance in simulators. To address this gap, we thoroughly study the impact of motion cues on braking at a target point as an elementary behavior that reflects the overall driver's performance. In this paper, we use an eye-tracking device to evaluate driver behavior in addition to evaluating data from a driving simulator and considering participants' feedback. Furthermore, we compare the effect of different motion levels ("No motion", "Mild motion", and "Full motion") in two road scenarios: with and without the pre-braking warning signs with the speed feedback given by the speedometer. The results showed that a full level of motion cues had a positive effect on braking smoothness and gaze fixation on the track. In particular, the presence of full motion cues helped the participants to gradually decelerate from 5 to 0 ms-1 in the last 240 m before the stop line in both scenarios, without and with warning signs, compared to the hardest braking from 25 to 0 ms-1 produced under the no motion cues conditions. Moreover, the results showed that a combination of the mild motion conditions and warning signs led to an underestimation of the actual speed and a greater fixation of the gaze on the speedometer. Questionnaire data revealed that 95% of the participants did not suffer from motion sickness symptoms, yet participants' preferences did not indicate that they were aware of the impact of simulator conditions on their driving behavior.

A Novel Braking Control Strategy for Hybrid Electric Buses Based on Vehicle Mass and Road Slope Estimation

Proper braking force distribution strategies can improve both stability and economy performance of hybrid electric vehicles, which is prominently proved by many studies. To achieve better dynamic stable performance and higher energy recovery efficiency, an effective braking control strategy for hybrid electric buses (HEB) based on vehicle mass and road slope estimation is proposed in this paper. Firstly, the road slope and the vehicle mass are estimated by a hybrid algorithm of extended Kalman filter (EKF) and recursive least square (RLS). Secondly, the total braking torque of HEB is calculated by the sliding mode controller (SMC), which uses the information of brake intensity, whole vehicle mass, and road slope. Finally, comprehensively considering driver's braking intention and regulations of the Economic Commission for Europe (ECE), the optimal proportional relationship between regenerative braking and pneumatic braking is obtained. Furthermore, related simulations and experiments are carried out on the hardware-in-the-loop test bench. Results show that the proposed strategy can effectively improve the braking performance and increase the recovered energy through precise control of the braking torque.

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.

A Simulation-Based Study of the Effect of Brake Light Flashing Frequency on Driver Brake Behavior from the Perspective of Response Time.

To prevent vehicle crashes, studies have proposed the use of flashing signals (brake lights or other light indicators) to improve the driver’s response time when the leading vehicle is braking. However, there are no consistent results on the ideal flashing frequency of the brake lights. This study aimed to investigate different brake light flashing frequencies to assess their impact on braking response time. Twenty-four participants aged 25 to 30 were recruited. Two driving speed environments (50 and 80 km/h), three deceleration rates (0.25, 0.6, and 1 g), and four brake light flashing frequencies (0, 2, 4, and 7 Hz) were examined. Braking response time, average braking force, and braking response time ratio were used to evaluate the driving behavior. The results showed that the braking response time and average braking force were affected by the deceleration rate in the 50 km/h driving environment. In the 50 and 80 km/h driving environments, although there were no significant differences among the three deceleration rates, the braking response time decreased by 3–7% under the flashing brake light condition. These findings can be used as a reference for safety designs as well as future studies on driving behavior.

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.

A methodology for assessing driver perception-response time during unanticipated cross-centerline events

Objective The purpose of this study was to present a methodology that utilizes naturalistic driving data to measure the driver response to an unanticipated driving scenario, a cross-centerline event. Methods Forward-facing video from naturalistic driving was used to determine when the cross-centerline event occurred. Then, the recorded acceleration and yaw rate data were used to identify the start of braking and steering evasive actions, respectively. A deceleration threshold of −0.1 g was defined as the braking onset, and a yaw rate of 2 deg/s was defined as the steering onset. Perception-response times (PRTs) were derived using these inputs. Results 17 cross-centerline events were identified from the naturalistic driving database. The drivers in all analyzed events applied the brakes, and 11 of the 17 drivers performed a steering maneuver. However, the average steering PRT (0.39 s) was faster than the average braking PRT (0.84 s). Conclusions Based naturalistic data from cross-centerline encroachment scenarios, the average driver steering PRT was faster than the average driver braking PRT. Both the driver’s median braking and steering PRT was faster in these real-world scenarios than in similar test track or simulator studies. Future analyses should investigate which action is attempted first and the effect of time to contact on driver response.

Accurate Pressure Control Based on Driver Braking Intention Identification for a Novel Integrated Braking System

<div class="section abstract"><div class="htmlview paragraph">With the development of intelligent and electric vehicles, higher requirements are put forward for the active braking and regenerative braking ability of the braking system. The traditional braking system equipped with vacuum booster has difficulty meeting the demand, therefore it has gradually been replaced by the integrated braking system. In this paper, a novel Integrated Braking System (IBS) is presented, which mainly contains a pedal feel simulator, a permanent magnet synchronous motor (PMSM), a series of transmission mechanisms, and the hydraulic control unit. As an integrative system of mechanics-electronics-hydraulics, the IBS has complex nonlinear characteristics, which challenge the accurate pressure control. Furthermore, it is a completely decoupled braking system, the pedal force doesn’t participate in pressure-building, so it is necessary to precisely identify driver’s braking intention. To improve the control accuracy of the system, this paper proposed a novel pressure control strategy based on driver braking intention identification. Firstly, the structure and working principle of the novel integrated braking system was introduced. Secondly, the driver's braking intention identification strategy was designed. Thirdly, Considering the nonlinear and dynamic characteristics of the system, a cascade closed-loop control strategy including a pressure loop by the feedforward-feedback method, a position loop by the sliding-mode control method, and current loop with friction compensation was proposed. Finally, based on dSPACE products, a hardware-in-the-loop (HiL) experimental bench was built for algorithm verification. The HiL experiment results show that the pressure control strategy has the advantages of accurate response, the braking system pressure follows the driver's expected pressure well.</div></div>

Development of driver braking control model based on ride comfort index

We propose a new driver braking control model. The driver braking control model is developed based on a ride comfort index. First, a brake model of the experimental vehicle is shown. Next, a driver braking control model is developed by using LQ optimal control. The driver braking control model is designed so that a ride comfort index can become small and ride comfort performance can be improved. Finally, numerical simulations are carried out to show effectiveness of the driver braking control model.

Analysis of dangers in the operation of city buses at the intersections

Summary Operation of motor vehicles should take into account the elimination of the possibility of accidents. The article considers selected problems occurring in the operation of public transport buses at intersections. Compliance with the requirements of the safety system during signal change on the signaling device controlling traffic at the intersection was analyzed. The human factor in ergonomics is important. Ending the green signal display (in special situations) causes a dilemma of decisions. The driver brakes abruptly (resulting in potentially harming passengers on the bus) or passes through a signal light displaying a red signal. The need to introduce changes in the regulations that would allow the introduction of adequate solutions was pointed out. These are activities in the area of the active safety system in the operation of vehicles in road traffic. The need to provide advance information and change the provisions in applicable law has been demonstrated.

Driver conflict response during supervised automation: Do hands on wheel matter?

Securing appropriate driver responses to conflicts is essential in automation that is not perfect (because the driver is needed as a fall-back for system limitations and failures). However, this is recognized as a major challenge in the human factors literature. Moreover, in-depth knowledge is lacking regarding mechanisms affecting the driver response process. The first aim of this study was to investigate how driver conflict response while using highly reliable (but not perfect) supervised automation differ for drivers that (a) crash or avoid a conflict object and (b) report high trust or low trust in automation to avoid the conflict object. The second aim was to understand the influence on the driver conflict response of two specific factors: a hands-on-wheel requirement (with vs. without), and the conflict object type (garbage bag vs. stationary vehicle). Seventy-six participants drove with highly reliable but supervised automation for 30 min on a test track. Thereafter they needed to avoid a static object that was revealed by a lead-vehicle cut-out. The driver conflict response was assessed through the response process: timepoints for driver surprise reaction, hands-on-wheel, driver steering, and driver braking. Crashers generally responded later in all actions of the response process compared to non-crashers. In fact, some crashers collided with the conflict object without even putting their hands on the wheel. Driver conflict response was independent of the hands-on-wheel requirement. High-trust drivers generally responded later than the low-trust drivers or not at all, and only high trust drivers crashed. The larger stationary vehicle triggered an earlier surprise reaction compared to the garbage bag, while hands-on-wheel and steering response were similar for the two conflict object types. To conclude, crashing is associated with a delay in all actions of the response process. In addition, driver conflict response does not change with a hands-on-wheel requirement but changes with trust-level and conflict object type. Simply holding the hands on the wheel is not sufficient to prevent collisions or elicit earlier responses. High trust in automation is associated with late response and crashing, whereas low trust is associated with appropriate driver response. A larger conflict object trigger earlier surprise reactions.

Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic

Overtaking a cyclist on a two-lane rural road with oncoming traffic is a challenging task for any driver. Failing this task can lead to severe injuries or even death because of the potentially high impact speed in a possible collision. To avoid a rear-end collision with the cyclist, drivers need to make a timely and accurate decision about whether to steer and overtake the cyclist, or brake and let the oncoming traffic pass first. If this decision is delayed, for instance because the driver is distracted, neither braking nor steering may eventually keep the driver from crashing—at that point, rear-ending a cyclist may be the safest alternative for the driver. Active safety systems such as forward collision warning that help drivers being alert and avoiding collisions may be enhanced with driver models to reduce activations perceived as false positive. In this study, we developed a driver model based on logistic regression using data from a test-track experiment. The model can predict the probability and confidence of drivers braking and steering while approaching a cyclist during an overtaking, and therefore this model may improve collision warning systems. In both an in-sample and out-of-sample evaluation, the model identified drivers’ intent to overtake with high accuracy (0.99 and 0.90, respectively). The model can be integrated into a warning system that leverages the deviance of the actual driver behavior from the behavior predicted by the model to allow timely warnings without compromising driver acceptance.