Wet Road Surfaces Research Articles

Wheel Slip Equilibrium Point Model Reference Adaptive Control Based PID Controller for Antilock Braking System: A New Approach

This paper presents a new approach to wheel slip control in Antilock Braking System (ABS) using an Approximated First Order Wheel Slip (AFOWS) Model Reference Adaptive Control (MRAC) based PID (AFOWS-MRAC-PID) controller. An ABS was modeled in a MATLAB/Simulink environment using a quarter car model with the proposed controller. Simulations were conducted with a wide range of adaptation gains (50, 100, 150, 200, and 250) to study the effectiveness of the proposed control system. The results revealed that the proposed system could track and maintain 10% wheel slip and eliminate oscillation (instability) in terms of overshoot associated with conventional PID controllers, particularly on wet and snowy road surfaces, using adaptation gains of 150, 200, and 250. Overall, the proposed system provided the best performance in terms of stopping distance, vehicle braking velocity, and braking torque on all road surfaces with an adaptation gain of 250, although braking on dry road surfaces was the most effective.

An algorithm for estimating the ultimate speed of vehicles on wet roads

Real-time estimation of a vehicle’s ultimate hydroplaning speed is crucial for ensuring its safety and stability, especially in rainy weather conditions. This paper proposes an algorithm for estimating the ultimate hydroplaning speed of vehicles on wet and slippery road surfaces based on finite element analysis of tire. Using the ABAQUS simulation software, a finite element model of a 205/55 R16 radial tire is established. Subsequently, a fluid-structure coupling model between the tire, water layer, and road surface is developed. The finite element method is employed to simulate and analyze the effects of tire inflation pressure, water layer thickness, vertical load, and tread wear on the vehicle’s ultimate hydroplaning speed. Simulation results indicate that an increase in tire inflation pressure and vertical load leads to an increase in the ultimate hydroplaning speed, while tread wear and an increase in water layer thickness result in a decrease. Finally, based on the simulation analysis data, a BP neural network-based estimation algorithm for the vehicle’s ultimate hydroplaning speed is proposed by combining the relationship between tire inflation pressure, tire vertical load, tread wear, water layer thickness, and ultimate hydroplaning speed. The results show that the estimated ultimate hydroplaning velocity profile has a high degree of overlap with the actual ultimate hydroplaning velocity profile, with a maximum error of no more than 5 km/h and an average percentage error of 2.31416%.

An adaptive model predictive control for coupled yaw and rollover stability of vehicle during corner maneuvers

This paper presents a control strategy to achieve yaw and roll stability by taking into account the physical interaction between the yaw and roll dynamics to prevent vehicle collisions in hilly or curved terrain. The mathematical model is formulated utilizing a roll dynamic model with a small tyre slip angle and a bicycle model with two degrees of freedom considering coupling of yaw and roll dynamics. An adaptive model predictive controller and a PID controller are included in the proposed control methodology so that a real-time scenario of variation in the longitudinal velocity and friction coefficient is considered. Stability limits are established based on the yaw rate, sideslip, and roll motions of the vehicle, taking into account the effects of the road angle. The friction coefficients of 0.4 and 0.8 are chosen for wet and dry road surfaces to show manoeuvrability and force the vehicle to avoid rollover condition. Using numerical simulations in Matlab R2022a, the effectiveness of the designed controller is assessed. A root mean square error (RMSE) is calculated for the proposed methodology for the evaluation of the performance and the values are obtained as 3.032 and 3.912 for friction coefficient of 0.8 for yaw rate and roll angle respectively. On comparing with the other methodology, it is found that the performance of the proposed method is better based on RMSE. Also, the fluctuations at the corners are removed and the variables are bound inside the stability limit, thus avoiding the vehicle from accidents in hilly areas. The robustness of the controller towards increasing the mass of the vehicle by 5% and 10% is found to be good.

Urban flood extent segmentation and evaluation from real-world surveillance camera images using deep convolutional neural network

This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. High-definition web cameras can be an alternative tool with the models trained on the data it collected. In conclusion, DCNN-based models can extract flood extent from camera images of urban flooding. The challenges with using these models on real-world data identified through this research present opportunities for future research.

A Machine Learning Approach for Detection and Suppression of Shadow or Wet Road Surfaces

In advanced driver assistance system detection of road surfaces is an important task. Few algorithms have been proposed in past to detect the road surfaces based on intensities. However, problem arises in detection process is due to the presence of shadows or wet road surfaces. Here we have proposed a novel algorithm for detection of shadows with the help of machine learning approaches. Initially shadow is being detected with the help of a threshold-based approach followed by windowing-based method. The detected shadow region gets confirmed with the help of a set of features and classifier. The detected shadow or wet pixels are in painted to obtain set of pixels without shadow for road classification problems. The simplicity and accuracy of the algorithm makes it robust and can be used as a part of road surface detection algorithm.

Lane Change Trajectory Planning Based on Quadratic Programming in Rainy Weather

To enhance the safety and stability of lane change maneuvers for autonomous vehicles in adverse weather conditions, this paper proposes a quadratic programming−based trajectory planning algorithm for lane changing in rainy weather. Initially, in order to mitigate the risk of potential collisions on wet and slippery road surfaces, we incorporate the concept of road adhesion coefficients and delayed reaction time to refine the establishment of the minimum safety distance. This augmentation establishes constraints on lane change safety distances and delineates the boundaries of viable lane change domains within inclement weather contexts. Subsequently, adopting a hierarchical trajectory planning framework, we incorporate visibility cost functions and safety distance constraints during dynamic programming sampling to ensure the safety of vehicle operation. Furthermore, the vehicle lane change sideslip phenomenon is considered, and the optimal lane change trajectory is obtained based on the quadratic programming algorithm by introducing the maneuverability objective function. In conclusion, to verify the effectiveness of the algorithm, lateral linear quadratic regulator (LQR) and longitudinal double proportional−integral−derivative (DPID) controllers are designed for trajectory tracking. The results demonstrate the algorithm’s capability to produce continuous, stable, and collision−free trajectories. Moreover, the lateral acceleration varies within the range of ±1.5 m/s2, the center of mass lateral deflection angle varies within the range of ±0.15°, and the yaw rate remains within the ±0.1°/s range.

Study on the Influence of Environmental Conditions on Road Friction Characteristics

This study focuses on changes in the friction characteristics of paved roads under various conditions from the viewpoint of traffic safety. In general, the braking characteristics of road vehicles are examined using the μ–s characteristics of the tires. Therefore, in our research, we used three types of limited datasets and identified them using the Magic Formula proposed by Prof. Pacejka. Based on various experiments, it was shown that changes in the road surface environment, such as dry and wet conditions, significantly affect the μ–s characteristics, and this influence varies significantly depending on the pavement conditions. In this study, as the first stage of the analysis of these influences, it was clarified that the difference in the friction characteristics of wet and dry road surfaces varies significantly depending on the pavement surface, which is based on experimental results obtained using actual roads. As this variation is closely related to the safety of actual road traffic, we used a brush model, which is a dynamic model of the road surface and tires, to clarify the differences in this characteristic.

Optimized Performance Design of Low Freezing Point Asphalt Mixture for Northern Mountainous Areas

Due to the lack of research on the applicability and optimization design of low freezing point asphalt mixtures in specific environments, especially for the harsh environments of low freezing point asphalt mixtures in mountainous areas during cold winter and high humidity in other seasons, there is a significant engineering demand. This paper carries out a study on the performance optimization design of low freezing point asphalt mixtures in northern mountainous areas. Through comparative analysis of low freezing point asphalt mixture technologies and analysis of the road performance of low freezing point asphalt mixtures, combined with the climate and driving characteristics of northern mountainous areas, the low freezing point asphalt mixture suitable for northern mountainous areas is selected. The research shows that the hygroscopicity of M low freezing point filler is significantly stronger than that of Z low freezing point filler, and since the humidity in northern mountainous areas is relatively high, selecting low freezing point fillers with lower hygroscopicity to prepare low freezing point asphalt mixtures can avoid safety accidents caused by excessively wet road surfaces. SBS modified asphalt and waste tire rubber powder modified asphalt can both produce low freezing point asphalt mixtures that meet the requirements of standard road performance, and both have good high and low temperature performance. When conditions permit, waste tire rubber powder modified asphalt low freezing point asphalt mixture is recommended as the first choice.

Comparison of Water Discharge Performance of Motorcycle Tires with Different Tread Patterns by Applying CFD (Computational Fluid Dynamics) Technique

The tire is one of the most important vehicle parts that provide contact between the vehicle and the road surface together with the air it carries in it. Some traffic accidents are due to driving performance on the wet road surface. Aquaplaning on water occurs when depressions and sloping areas on the asphalt become filled with water, causing it to accumulate. When a vehicle passes over a puddle, its tires lose contact with the ground, causing it to slide instead of tire pattern channels helping roll on the water. This can cause problems with water drainage. When a vehicle passes over a puddle, its tires lose contact with the ground, causing it to slide instead of roll on the water. This can cause problems with water drainage. For this reason, it is extremely important to design the pattern on the tires properly. The pattern channels on the tire help to evacuate water by passing it through the channels formed on the asphalt surface. However, in cases where the amount of water is high, the pattern channels will be forced after a while and lose contact with the ground, as they cannot discharge all the water bodies. For this reason, it is extremely important to design the pattern on the tires properly. The study analyzed the water evacuation effects of motorcycle tires with three different tread patterns on wet surfaces, taking into account different factors such as contact angles, driving direction, and driving speeds. The data obtained from the study indicate that the tread pattern design has a significant impact on the aquaplaning performance of tires on water surfaces.

Estimation of Water Depth on Road Surfaces Using Accelerometric Signals.

The paper presents an experimental study conducted to evaluate the feasibility of using accelerometers as an indirect means to estimate water depths on road surfaces. It makes use of the vibration of the vehicle's wheel arch due to water droplets projected by a tire rolling on a wet road surface. A trailer equipped with a wheel and towed by a van was used. The test setups to spread water on the road surface and before the test wheel, measure the water depth and visualize the water spray are described. The test program, conducted on a test track closed to the traffic, includes three surfaces and two speeds. Visualization of water flows by means of high-speed cameras makes it possible to choose a suitable location for the accelerometers. It turns out that signals provided by the accelerometers are affected by the trailer's movement; a filtering method has been successfully developed to remove noises. Results show a tight relationship between the mean amplitude of accelerometric signals and actual water depths. Discussions are made in terms of effects of the vehicle speed and the road surface texture. Perspectives for using the developed system to improve passenger safety under autonomous driving conditions are presented.

Derin Öğrenme Yöntemi Kullanılarak Meteorolojik Parametrelere Dayalı Karayolu Kaplama Yüzey Durumunun Tahmini

The condition of the pavement surface on highways is an important factor in ensuring traffic safety. The condition of the road pavements varies according to the climatic conditions of the road. To record the variability of road pavements according to meteorological factors, both sensors placed in the pavement and road meteorology information stations are installed on the roadsides. Within the scope of intelligent transportation systems, the establishment of road management information systems and the status of the road pavement in real-time can be observed with the data obtained from the sensors. With these sensor data, the road surface condition can be estimated with different artificial intelligence methods. Thus, important information is provided for decision-makers in taking precautions according to the dry, wet, and icy road surface condition. In this study, it is purposed to estimate the road surface condition based on meteorological parameters. For this purpose, deep learning models have been developed. Air temperature (tmp), dew point temperature (dwp), wind speed (sknt), wind direction (drct), wind gust (gust), pavement sensor temperature (tfs), and pavement sensor condition (cond) parameters were used in 65966 datasets. Accuracy was used in the evaluation of deep learning models. Consequently, the evaluation, the accuracy value of the best model was determined as 0.88. In addition, accuracy, recall, precision, and F1-score values of each class were calculated for the test set of the best model.

Using Vision Transformers for Spatial-Context-Aware Rain and Road Surface Condition Detection on Freeways

Inclement weather conditions, particularly heavy rain and the consequent wet road surface, have an unfavorable effect on driving conditions, traffic infrastructure, and operational plans. To mitigate the potentially detrimental ramifications of turbulent weather, it must be continuously monitored in real time and with high geospatial granularity. Traditionally, road weather conditions are monitored using weather forecasts or Roadside Weather Information Systems (RWIS). However, these methods are either ill-equipped or too expensive to provide the required fine-grained observations. Alternatively, roadside traffic CCTV cameras are ubiquitously deployed on US freeways and can serve as inexpensive sensors to surveil weather. In this paper, a novel vision-based methodology is proposed to detect rain and road surface conditions from roadside traffic cameras. Vision Transformers were utilized for image-based classification. They demonstrated superior results compared to convolution-based approaches. Furthermore, the geographical distribution of roadside cameras was leveraged to add spatial context awareness to the detection model. A Spatial Self-Attention network was proposed to model the relationship between the detection results of adjacent images as a sequence-to-sequence detection task. The results indicate that the addition of the sequential detection module improved the accuracy of the stand-alone Vision Transformer as measured by the F1-score. The boost in performance enhanced the F1-scores of the stand-alone Vision Transformer by 5.61% and 5.97% for the rain and road surface condition detection tasks, respectively, raising the total F1-score to 96.71% and 98.07%.

Evaluation of direction judgements based on turn signal guide lamp and behavioural responses

Traffic crashes frequently occur at intersections because drivers or pedestrians cannot properly anticipate the direction towards which other vehicles are turning. A turn signal guide lamp was developed to improve the observer’s direction judgement of turn signals, and experiments were conducted to test the effect of the guide lamp based on behavioural responses. Our experimental results demonstrated that the turning direction judgement response time was reduced when both the turn signal indicators and turn signal guide lamps were used together compared with the case when only the turn signal indicators were used. Supplementary eye-tracking data also demonstrated that the gaze point frequently stayed on the light patterns on the ground marked by the turn signal guide lamps. Additional glare evaluation using the de Boer scale showed no significant difference between the turn signal indicator with a guide lamp and the turn signal indicator alone on dry or wet road surfaces. Therefore, it is expected that the newly developed turn signal guide lamp will increase the observer’s performance of direction judgement on the turn signalling function without causing an additional glare when the observer knows the function of the guide lamp.

Near-infrared LED system to recognize road surface conditions for autonomous vehicles

Abstract. The driving safety of autonomous vehicles will strongly depend on their ability to recognize road surface conditions such as dry, wet, snowy and icy road. Currently, the existing investigations to detect road surface conditions still have limitations in daytime and nighttime conditions. The objective of this paper is to propose and develop a new system with three near-infrared (NIR) LED sources. This choice is based on the advantages of LED sources over laser diodes. They are less sensitive to temperature and have lower costs. Considering these advantages, the feasibility of the LED system to recognize road surface conditions is investigated. For this, the appropriate wavelengths of the LED tri-wavelength source are first computed from experimental data taking into account the specific LED spectral shape. In addition, the effect of the spectral bandwidth of the LED sources on the system performance is theoretically studied. Finally, the NIR LED system with the LED sources at 970, 1450 and 1550 nm is experimentally tested and validated with an incident angle from 78.7 to 86.2∘. According to the results of the experiments, the accuracy of the classification of snow, wet and water can reach 97 %, while the accuracy of the dry and wet road surface conditions is respectively 73 % and 68 %.

Application and Comparison of Deep Learning Methods to Detect Night-Time Road Surface Conditions for Autonomous Vehicles

Currently, road surface conditions ahead of autonomous vehicles are not well detected by the existing sensors on those autonomous vehicles. However, driving safety should be ensured for the weather-induced road conditions for day and night. An investigation into deep learning to recognize the road surface conditions in the day is conducted using the collected data from an embedded camera on the front of the vehicles. Deep learning models have only been proven to be successful in the day, but they have not been assessed for night conditions to date. The objective of this work is to propose deep learning models to detect on-line road surface conditions caused by weather ahead of the autonomous vehicles at night with a high accuracy. For this study, different deep learning models, namely traditional CNN, SqueezeNet, VGG, ResNet, and DenseNet models, are applied with performance comparison. Considering the current limitation of existing night-time detection, reflection features of different road surfaces are investigated in this paper. According to the features, night-time databases are collected with and without ambient illumination. These databases are collected from several public videos in order to make the selected models more applicable to more scenes. In addition, selected models are trained based on a collected database. Finally, in the validation, the accuracy of these models to classify dry, wet, and snowy road surface conditions at night can be up to 94%.

A Study on the Ultimate Velocity for Vehicles Going through Roadway Curves to Avert Sideslip

The part results of a study on the factors influencing safety as driving a vehicle at road curve are reported in this paper. A motivation doing this research is to reduce casualties in traffic road accidents since many accidents happened at road curve sections. An improved mathematical model is constructed based on the mechanical analysis of vehicle motion. In the model, excepting the factor of road surface superelevation angle, the effects of longitudinal gradient of road curve and wind force are also taken into consideration. Two typical road curves respectively in two existing roadways in Nagano Prefecture, Japan are selected as the investigated road curves, which include the road curve section where a bus traffic accident well known has occurred. Four types of vehicles including bus are taken as the studied objects. The relationship between the ultimate velocity for vehicle to travel bending a road curve and to safely avert sideslip and relative parameters is built. The predicted results are given and discussed. The results show that the ultimate velocity as one of the important parameters to avert bus accident decreases with wind speed increasing. With the curve radius increasing, the ultimate velocity increases in all road surface conditions, in which the ultimate velocity for bus traveling on dry road surface increases most quickly, then on wet road surface, snow accumulation road surface and frozen road surface successively for ranges of velocity limited to 110 km/h and a curve radius to 350 m. For same wind speed, the bigger the longitudinal gradient ?, the smaller the ultimate velocity.

Obsolete or Viable? Revision of Lane-Change Manoeuvre Duration Empirical Calculation

This study presents a calculation of the time required to execute a lane-change manoeuvre. Compared with other (and older) calculation methods, an analysis was conducted to determine which approach could yield the most reliable results. This study aimed to present a universal calculation method for different road surfaces, surface conditions (dry and wet road surface), and vehicle types (i.e., from small vehicles to SUVs). A total of 108 comparable manoeuvres with modern vehicles were used as a basis for statistical analysis. A new mathematical constant was found based on a regression analysis, adjusting one of the older calculation methods (so-called Kovařík equation), providing the best match between real and calculated manoeuvre duration.

Youth Resilience Education and 13-Year Motor Vehicle Crash Risk.

DRIVE is a prospective cohort study of a large (≥20 000) nonrepresentative sample of newly licensed young drivers in New South Wales, Australia.1 Its objective is to identify risk and protective factors for motor vehicle crashes (MVCs) (or crashes) by observing subgroup differences over time. A previously observed protective factor was participation in the New South Wales Health Reduce Risk Increase Student Knowledge (RRISK) resilience program.2 RRISK brings together grade 11 students from diverse schools for a 1-day, whole-of-community seminar that is preceded and followed by multiple peer-led teacher and parent support activities. It addresses common youth risks, including alcohol and drug use, partying, and driver and passenger risks. The focus is on empowering students to personalize learning into awareness of their own and their friends’ risk propensities, contributing factors, and how to minimize these, including forward planning and back-up strategies. The current research aims were to explore whether crash differences by program participation persisted over 13 years and to explore any differences by crash type, including severity.Detailed DRIVE methods are reported elsewhere.1,3 Briefly, all drivers aged 17 to 24 in New South Wales, Australia, on their first independent (postlearner) license during 2003–2004 were invited to complete a detailed online questionnaire, which included RRISK participation, and gave permission to access their police-recorded MVC records in future years. Survey and crash data to 2016 were linked by an independent body and were provided deidentified (ethics approvals: Aboriginal Health & Medical Research Council, University of New South Wales, and New South Wales Population & Health Services; reference: HREC/16/CIPHS/9).Univariate and multivariate negative binomial regression models were analyzed, with the number of MVCs of legally permitted vehicles as the primary outcome and MVC police codes for wet road surface, dark lighting, and severity (no injury versus minor injury to fatal) as secondary outcomes (other MVC types were too infrequent to include). We imputed missing values in the survey data using chained equations with 30 imputation cycles. Table 1 includes baseline survey variables considered as potential confounders, with time in study (from survey completion to either December 31, 2016, or [all cause] date of death) included as an offset. Analyses were performed by using Stata 15 (Stata Corp, College Station, TX).The cohort comprised 20 806 participants; 54.1% of the cohort was female (Table 1). Other demographics differed by group, with more program participants aged 17 and living in a regional and disadvantaged area, reflecting program delivery locations. Overall, 20.4% of the cohort had at least 1 crash as a driver, including 4.7% crashes on wet roads, 6.1% crashes in dark lighting, and 9.5% crashes resulting in injury.Table 2 summarizes resulting rate ratios. Unadjusted models revealed lower risk for program participants than nonparticipants for all crashes, crashes in dark lighting, and injury crashes but not crashes on wet roads. When adjusted for confounders, injury crashes were no longer statistically different; however, program participants were 24% less likely to have had any crash and 42% less likely to crash in darkness than nonparticipants.In this prospective study, rich baseline information on known confounders and access to statewide records were available for a large cohort of drivers for up to 13 years. Even when known confounders were accounted for, a lower risk of crash persisted for resilience program participants, including crashes in darkness. Injury crashes indicated similar positive findings but were not statistically different after adjustment for confounders.Limitations of the observational study design, including potential selection bias (more by schools than individuals) and complex changes in baseline measures over time, such as crash risk exposures and lifestyle changes (including cessation of driving), albeit unlikely influenced by program participation, have been reported previously.2,3 At-fault status was unknown; however, driver errors contribute to the majority of young novice crashes.4 Regardless, positive outcomes persisted, strongly indicating that setting up youth to be safer, responsible drivers early has real potential to reduce their lifetime risk of a crash that is significant enough to warrant police attendance. Research increasingly identifies that road risks cluster with other youth risks and that addressing these collectively and early (including at predriving age) is likely to have better outcomes than addressing them in isolation.5–7High-quality evaluations of school-based road safety initiatives are limited or treat any driver education program as equal.8,9 This contributes to overriding beliefs that driver education does not work rather than that good programs need to be identified from the proliferation of traditional programs. Positive outcomes are evident for more nuanced initiatives.10With road crashes persisting as a leading cause of preventable deaths and serious injuries, the current research warrants increased efforts to improve the evidence base and thereby the quality of youth road safety initiatives to set them up for a lifetime of safer driving.

Investigation of Contributing Factors to Traffic Crash Severity in Southeast Texas Using Multiple Correspondence Analysis

Driving is the essential means of travel in Southeast Texas, a highly urbanized and populous area that serves as an economic powerhouse of the whole state. However, driving in Southeast Texas is subject to many risks as this region features a typical humid subtropical climate with long hot summers and short mild winters. Local drivers would encounter intense precipitation, heavy fog, strong sunlight, standing water, slick road surface, and even frequent extreme weather such as tropical storms, hurricanes and flood during their year-around travels. Meanwhile, research has revealed that the fatality rate per 100 million vehicle miles driven in urban Texas became considerably higher than national average since 2010, and no conclusive study has elucidated the association between Southeast Texas crash severity and potential contributing factors. This study used multiple correspondence analysis (MCA) to examine a group of contributing factors on how their combinatorial influences determine crash severity by creating combination clouds on a factor map. Results revealed numerous significant combinatorial effects. For example, driving in rain and extreme weather on a wet road surface has a higher chance in causing crashes that incur severe or deadly injuries. Besides, other contributing factors involving risky behavioral factors, road designs, and vehicle factors were well discussed. The research outcomes could inspire local traffic administration to take more effective countermeasures to systematically mitigate road crash severity.

Adjustable Gain Enhanced Fuzzy Logic Controller for Optimal Wheel Slip Ratio Tracking in Hard Braking Control System

This paper has presented hard braking control system based on Adjustable Gain Enhanced Fuzzy Logic Controller (AGE-FLC) for optimal wheel slip ratio tracking performance. The purpose of the study was to improve slip ratio tracking and eliminate cycling while achieving very much shortened distance during emergency braking. The model of a braking vehicle at speed of 30 m.s^-1 subject to wheel locking was developed and implemented in MATLAB/Simulink environment. Simulation was conducted without a controller to study the slip ratio performance of the system on different road surfaces. The simulation results showed that stopping distance was 135.2 m in 5 seconds. A Fuzzy Logic Controller (FLC) whose control signal was enhanced by adding an adjustable gain mechanism to its output was designed. Simulation results showed that the AGE-FLC controller offered optimal tracking of desired wheel slip ratio of 0.1 as fast as possible on all road surface scenarios, while improving the stopping distance by 70.4% on dry road surface, 63.3% on wet road surface, 57.5% on cobblestone road surface and 48.8% on snow road surface in 2.651seconds.