Driver Braking Research Articles

Risk Predictive Driver Assistance System for Collision Avoidance in Intersection Right Turns

Most traffic accidents that result in injuries or fatalities occur in intersections. In Japan, where cars drive on the left, most of such accidents involve cars that are turning right. This situation serves as the basis of the development of our Advanced Driver Assistance System (ADAS) for intersection right turns. This research focuses on the scenario in which an object darts out from the blind spot created by heavy oncoming traffic as a vehicle is making an intersection right turn. When this happens, even if the driver brakes as hard as possible or an active safety function such as the Autonomous Emergency Braking System (AEBS) applies the brakes, the natural limits of physical friction may make it impossible to avoid a collision. To improve traffic safety given the limited potential of physical friction, this research seeks to develop a risk-predictive right-turn assistance system. The system predicts potential oncoming objects and reduces the vehicle velocity in advance. Blind corners can be detected by on-board sensors without requiring information from surrounding infrastructure. This paper presents a right-turn assistance system that avoids conflict with the AEBS in emergencies by decelerating the ego vehicle to a safe velocity.

Hybrid-Learning-Based Classification and Quantitative Inference of Driver Braking Intensity of an Electrified Vehicle

The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian mixture model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on artificial neural networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios.

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.

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 quantitative driver model of pre-crash brake onset and control

An existing modelling framework is leveraged to create a driver braking model for use in simulations of critical longitudinal scenarios with a slower or braking lead vehicle. The model applies intermittent brake adjustments to minimize accumulated looming prediction error. It is here applied to the simulation of a set of lead vehicle scenarios. The simulation results in terms of brake initiation timing and brake jerk are demonstrated to capture well the specific types of kinematics-dependencies that have been recently reported from naturalistic near-crashes and crashes.

Driving style recognition method using braking characteristics based on hidden Markov model.

Since the advantage of hidden Markov model in dealing with time series data and for the sake of identifying driving style, three driving style (aggressive, moderate and mild) are modeled reasonably through hidden Markov model based on driver braking characteristics to achieve efficient driving style. Firstly, braking impulse and the maximum braking unit area of vacuum booster within a certain time are collected from braking operation, and then general braking and emergency braking characteristics are extracted to code the braking characteristics. Secondly, the braking behavior observation sequence is used to describe the initial parameters of hidden Markov model, and the generation of the hidden Markov model for differentiating and an observation sequence which is trained and judged by the driving style is introduced. Thirdly, the maximum likelihood logarithm could be implied from the observable parameters. The recognition accuracy of algorithm is verified through experiments and two common pattern recognition algorithms. The results showed that the driving style discrimination based on hidden Markov model algorithm could realize effective discriminant of driving style.

Crash probability estimation via quantifying driver hazard perception

Crash probability estimation is an important method to predict the potential reduction of crash probability contributed by forward collision avoidance technologies (FCATs). In this study, we propose a practical approach to estimate crash probability, which combines a field operational test and numerical simulations of a typical rear-end crash model. To consider driver hazard perception characteristics, we define a novel hazard perception measure, called as driver risk response time, by considering both time-to-collision (TTC) and driver braking response to impending collision risk in a near-crash scenario. Also, we establish a driving database under mixed Chinese traffic conditions based on a CMBS (Collision Mitigation Braking Systems)-equipped vehicle. Applying the crash probability estimation in this database, we estimate the potential decrease in crash probability owing to use of CMBS. A comparison of the results with CMBS on and off shows a 13.7% reduction of crash probability in a typical rear-end near-crash scenario with a one-second delay of driver’s braking response. These results indicate that CMBS is positive in collision prevention, especially in the case of inattentive drivers or ole drivers. The proposed crash probability estimation offers a practical way for evaluating the safety benefits in the design and testing of FCATs.

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.

ABS Dynamic Simulation Test Stand using Lab View

Anti-lock braking system (ABS) is an automobile safety system for vehicles which will allow controlling wheels of vehicle and providing safety contact on road surface according to driver braking input, avoiding uncontrolled skidding and also preventing the wheels from locking up. The main task of ABS is to press and release the brakes almost 15 times a second, so process of stopping the vehicle in a shorter time and distance. The ABS controller basically prevents the braking wheel from skidding on the road surface. Because of all these features, ABS has greatly improved the performance of automobile security. Currently the test stand is used to take measurements and analysis of ABS. The ABS system performs two main functions, automatic measurement and testing of high quality ABS. In this paper, the test stand can create a virtual environment so that various testing conditions can be applied for ABS system. The test stand can record all the parameters and compare with stored values, and simultaneously apply for Lab View software. This Software is used to calculate varies parameters of the test stand and also generate graph which shows running status test stand.

A Forward Collision Warning Algorithm With Adaptation to Driver Behaviors

Significant effort has been made on designing user-acceptable driver assistance systems. To adapt to driver characteristics, this paper proposes a forward collision warning (FCW) algorithm that can adjust its warning thresholds in a real-time manner according to driver behavior changes, including both behavioral fluctuation and individual difference. This adaptive FCW algorithm overcomes the limit of traditional FCW with fixed risk evaluation models and fixed triggering thresholds by continuously monitoring driver braking behaviors in multiple lanes. A real-time identification algorithm for the warning thresholds is designed by using the recursive least squares method. Based on naturalistic experimental data, offline simulations show that this algorithm can match driver behavioral fluctuation and individual difference in long-time driving condition, and as time goes on, the adaptability to driver behavior is gradually improved, thus decreasing the false-alarm rate of FCW.

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.

Risk Prediction for Curve Speed Warning by Considering Human, Vehicle, and Road Factors

Current curve speed warning systems take into account mostly vehicle and road factors but not driver behavior. The systems aim at detecting sideslips of small cars on curves without consideration of rollovers for vehicles with an elevated center of gravity. In this study, a curve speed model that considers human, vehicle, and road factors is built to prevent not only sideslips but also rollover accidents for vehicles with an elevated center of gravity. In addition, a risk prediction model is presented to judge accident risk levels and determine levels of warning. Finally, the effectiveness of the presented system is validated with one skilled driver who carries out one test through a simulator under different curve scenarios. To verify the system, data from simulator tests were collected for offline checking of the system. The data were used to calculate safe speeds by using the curve speed model and to determine the levels of risk based on the risk prediction model. The results show that the system is highly compatible with the skilled driver in terms of warning accuracy and timing. Specifically, the correct alarm rate (i.e., the driver brakes and the system’s alarm goes off) of the system is 83.57% and the error alarm rate (i.e., the driver does not brake but the system’s alarm goes off) is 9.79%. Moreover, more than 80% of the time the difference between the system warning time and the operating time of the skilled driver is less than 2 s.

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.

Drivers’ comfort boundaries in pedestrian crossings: A study in driver braking characteristics as a function of pedestrian walking speed

Systems informing or warning a driver of an imminent collision with a pedestrian or automatically initiating braking have been introduced to the car market. One of the major challenges is to balance system performance against the possibility of undesired system activation. The distinction between desired and undesired system activation can be based on driver discomfort. In this study driver discomfort can be inferred from brake onset, which refers to the start of brake pedal depression, as the most intuitive way for a driver to resolve a conflicting situation.The influences of pedestrian walking speed on brake onset and brake deceleration levels were investigated in Toyota Motor Corporation’s high-fidelity driving simulator. One hundred and eight naive volunteers drove at a target speed of 30km/h in an urban environment and were subjected to two animations of pedestrian crossing situations projected inside the driving simulator. The pedestrian speed was 1m/s and 2m/s. Time To Collision (TTC) at brake onset, one of the recorded quantities, might be a suitable measure for comfort zone boundaries as it is less affected by driving speed than other measures.Pedestrian speed was found to have a statistically significant influence on brake onset. For pedestrian speeds of 1m/s, 90% of drivers braked before 2.6s TTC. For 2m/s this value was 2.2s. These values can be used to differentiate between desired and undesired system activation in the design of an “unjustified system response” test in the assessment of pedestrian safety systems.

Effect of perception irregularity on chain-reaction crash in low visibility

We present the dynamic model of the chain-reaction crash to take into account the irregularity of the perception–reaction time. When a driver brakes according to taillights of the forward vehicle, the perception–reaction time varies from driver to driver. We study the effect of the perception irregularity on the chain-reaction crash (multiple-vehicle collision) in low-visibility condition. The first crash may induce more collisions. We investigate how the first collision induces the chain-reaction crash numerically. We derive, analytically, the transition points and the region maps for the chain-reaction crash in traffic flow of vehicles with irregular perception times. We clarify the effect of the perception irregularity on the multiple-vehicle collision.

Development of Brake System and Regenerative Braking Cooperative Control Algorithm for Automatic-Transmission-Based Hybrid Electric Vehicles

In this paper, a brake system for an automatic transmission(AT)-based hybrid electric vehicle (HEV) is developed, and a regenerative braking cooperative control algorithm is proposed, with consideration of the characteristics of the brake system. The brake system does not require a pedal simulator or a fail-safe device, because a hydraulic brake is equipped on the rear wheels, and an electronic wedge brake (EWB) is equipped on the front wheels of the vehicle. Dynamic models of the HEV equipped with the brake system developed in this study are obtained, and a performance simulator is developed. Furthermore, a regenerative braking cooperative control algorithm, which can increase the regenerative braking energy recovery, is suggested by considering the characteristics of the proposed hydraulic brake system. A simulation and a vehicle test show that the brake system and the regenerative braking cooperative control algorithm satisfy the demanded braking force by performing cooperative control between regenerative braking and friction braking. The regenerative braking cooperative control algorithm can increase energy recovery of the regenerative braking by increasing the gradient of the demanded braking force against the pedal stroke. The gradient of the demanded braking force needs to be determined with consideration of the driver's braking characteristics, regenerative braking energy, and the driving comfort.

Risk Simulation Analysis of Vehicle Moving on Curved Sections based on Driver Braking

Risk Simulation Analysis of Vehicle Moving on Curved Sections based on Driver Braking

The effect of combined horizontal curve and longitudinal grade on side friction factors

The relationship between side friction factor and longitudinal grade is investigated for horizontal curves considered in three dimensions, and new models for estimating side friction factors for different vehicle types are presented. The study consists of three parts: (1) a series of simulation tests using the CarSim and TruckSim multi-body simulation software packages; (2) multiplex regression analysis; and (3) presentation of recommended formulas for the side friction factor. Two different driving behaviors are considered in the simulation process: in one, the driver negotiates the curve at constant speed; in the other, the driver brakes while passing downgrades. Side friction factors are also studied separately for each axle of a vehicle. Greater side friction factors are found on downgrades for all vehicle types when cornering. From the viewpoint of skidding, the most critical situation is found for the rear axle of a sedan. In addition, braking is found to have a significant effect on side friction factors. The results and models from this study can be used in the geometric design phase by highway engineers to design safer roads.

A method to model anticipatory postural control in driver braking events

Human body models (HBMs) for vehicle occupant simulations have recently been extended with active muscles and postural control strategies. Feedback control has been used to model occupant responses to autonomous braking interventions. However, driver postural responses during driver initiated braking differ greatly from autonomous braking. In the present study, an anticipatory postural response was hypothesized, modelled in a whole-body HBM with feedback controlled muscles, and validated using existing volunteer data. The anticipatory response was modelled as a time dependent change in the reference value for the feedback controllers, which generates correcting moments to counteract the braking deceleration. The results showed that, in 11m/s2 driver braking simulations, including the anticipatory postural response reduced the peak forward displacement of the head by 100mm, of the shoulder by 30mm, while the peak head flexion rotation was reduced by 18°. The HBM kinematic response was within a one standard deviation corridor of corresponding test data from volunteers performing maximum braking. It was concluded that the hypothesized anticipatory responses can be modelled by changing the reference positions of the individual joint feedback controllers that regulate muscle activation levels. The addition of anticipatory postural control muscle activations appears to explain the difference in occupant kinematics between driver and autonomous braking. This method of modelling postural reactions can be applied to the simulation of other driver voluntary actions, such as emergency avoidance by steering.

A Study on Modeling of Driver's Braking Action to Avoid Rear-End Collision with Time Delay Neural Network

A Study on Modeling of Driver's Braking Action to Avoid Rear-End Collision with Time Delay Neural Network