Snowy Road Research Articles

Data-driven risk analysis of nonlinear factor interactions in road safety using Bayesian networks

This paper aims to demonstrate nonlinear risk factor interactions based on a data-driven approach using a Bayesian network model, providing a road safety use case. Road safety is a critical issue worldwide, with approximately 1.3 million road traffic deaths each year (WHO). Traditional road safety risk assessment methods often analyze individual factors separately; however, these assessments fail to capture the complex dynamics of real-world analysis, in which multiple factors interact through nonlinear relationships. In this study, a novel road safety risk assessment approach that uses a Bayesian network model to explore the nonlinear relationships among road safety risk factors is developed. Through the analysis of extensive crash reports from the state of Maryland, the complex interdependencies among various risk factors and their cumulative impact on road safety are investigated. Our findings show that two combined risk factors have different effects on risk level when considered individually. Two case studies related to human state risk factors and environmental risk factors, such as driving under the influence and snowy roads, as well as fatigue and snowy roads, have an amplified effect on the risk level. The findings highlight the importance of considering nonlinear interactions among risk factors when developing effective and targeted strategies for accident prevention and road safety improvement. This research contributes to the field of road safety by presenting a new methodology for understanding and mitigating road safety hazards.

Acceleration Slip Regulation Control Method for Distributed Electric Drive Vehicles under Icy and Snowy Road Conditions

To achieve a rapid and stable dynamic response of the drive anti-slip system for distributed electric vehicles on low-friction surfaces, this paper proposes an adaptive acceleration slip regulation control strategy based on wheel slip rate. An attachment coefficient fusion estimation algorithm based on an improved singular value decomposition unscented Kalman filter is designed. This algorithm combines Sage–Husa with the unscented Kalman filter for adaptive improvement, allowing for the quick and accurate determination of the road friction coefficient and, subsequently, the optimal slip rate. Additionally, a slip rate control strategy based on dynamic adaptive compensation sliding mode control is designed, which introduces a dynamic weight integral function into the control rate to adaptively adjust the integral effect based on errors, with its stability proven. To verify the performance of the road estimator and slip rate controller, a model is built with vehicle simulation software, and simulations are conducted. The results show that under icy and snowy road conditions, the designed estimator can reduce estimation errors and respond rapidly to sudden changes. Compared to traditional equivalent controllers, the designed controller can effectively reduce chattering, decrease overshoot, and shorten response time. Especially during road transitions, the designed controller demonstrates better dynamic performance and stability.

Fatal crashes involving vehicles with driver warning systems: Identifying risk factors using a correlated random parameters ordered logit modeling approach

Recent advancements in vehicular technology are expected to enhance traffic safety by either warning the drivers or by automating the tasks related to driving to reduce the human driver's involvement. The driver warning systems (DWSs) are designed to warn drivers in unsafe situations such as forward collision, lane departure, or when changing lanes with vehicles in blind spot areas. Although these features are designed to enhance safety, recent crash data shows vehicles with these features are still getting involved in crashes, making it necessary to identify the contributing factors. Further, it also requires research to quantify the benefits of vehicles with one or multiple DWS in terms of safety during multivehicle crashes. This study presents a methodological framework to compare factors affecting fatal crashes involving vehicles with no, one and two DWSs. A three-step method is proposed to incorporate unobserved heterogeneity while modeling. Fixed parameter and correlated random parameter order logit models are employed. The results shows that correlated random parameters ordered logit model outperforms traditional fixed parameter ordered logit model. Vehicles equipped with DWSs are safer than vehicles without DWSs during wet or snowy road conditions, when the vehicle skids laterally or longitudinally, and at intersections. Vehicles with one or both DWSs can reduce drink-and-drive and speeding-related crash involvement than vehicles without DWSs. Female and elderly drivers are at a higher risk while driving a vehicle with one or both DWSs compared to driving a vehicle without DWSs, demanding vehicular modifications.

Enhanced Vehicle Dynamics and Safety through Tire–Road Friction Estimation for Predictive ELSD Control under Various Conditions of General Racing Tracks

This study focuses on the tire–road friction estimation for the predictive control strategy of electronically limited slip differential (ELSD) to improve the handling and acceleration performance of front-wheel drive cars, which typically suffer from excessive understeer and inner drive wheel spin during acceleration while turning due to reduced vertical load on the wheel. To mitigate this, we propose a control logic for ELSD that enhances course followability and acceleration by pre-transferring the driving torque from the inside to the outside wheel, considering the estimated traction potential for rapid response. It is essential to improve the control accuracy of wheel spin prediction by predicting the friction coefficient of the road surface. Furthermore, this study extends to the analysis of vehicle dynamics during lane-change maneuvers on low-friction surfaces, emphasizing the role of accurate tire–road friction estimation in vehicle safety. A CarSim 2023-based simulation study was conducted to investigate the vehicle response on snowy roads with low friction coefficients (μ = 0.2) and low temperatures (−5 °C). The results demonstrated that even minimal steering input could result in significant side-slip angles, highlighting the nonlinear vehicle behavior and the critical need for robust traction estimation in such challenging conditions of general racing tracks. The proposed friction-estimation method was evaluated through vehicle testing and has been substantiated by patents for its originality in control and friction-estimation approaches. The outcomes of these combined methodologies underline the critical importance of tire–road friction coefficient estimation in both the effectiveness of the ELSD system and the broader context of active safety systems.

The Dynamic Path Planning of Autonomous Vehicles on Icy and Snowy Roads Based on an Improved Artificial Potential Field

The crucial dynamic path planning of autonomous vehicles is achieved via obstacle avoidance path planning technology. The reduction of the tire adhesion coefficient on icy and snowy roads (ISRs) increases the difficulty of autonomous vehicles’ control. In this paper, the driving characteristics of vehicles on ISRs are established, and the artificial potential field function is introduced to avoid collision risk when planning a path. A dynamic path planning algorithm for autonomous vehicles based on the artificial potential field (APF) is established. The adjustment factor is added to the gravitational potential field, and a judgment coefficient is added to the repulsive potential field to improve the artificial potential field function, based on the low adhesion of vehicles on ISRs. Moreover, a path with a continuous curvature is generated to achieve the driving comfort and driving safety of the planned path via trajectory smoothing. By establishing the Carsim/Simulink co-simulation platform, the effectiveness of dynamic path planning for autonomous vehicles under different algorithms and different obstacle models is compared. The results show that the improved APF algorithm has an obvious effect on the smoothness of the path and the reduction of the curvature mutation and can generate a safe and efficient path on icy and snowy roads. The dynamic obstacle avoidance of the improved APF algorithm improves the pre-judgment accuracy of the collision risk assessment of autonomous vehicles and shows the superiority of the improved algorithm.

Study on the Effect and Mechanism of Different Snowmelt Agents on Asphalt

Abstract This article explores the effects of different types of snow-melting agents on the high-temperature performance, low-temperature performance, rheological properties, and creep properties of asphalt in order to better understand the mechanism of different types of snow-melting agents on asphalt. Chlorine salt snow-melting agent solution, nonchlorine salt snow-melting agent solution, and mixed snow-melting agent solution were prepared, respectively, and the erosion of asphalt was carried out by periodic immersion test method. Then, through the penetration test, ductility test, softening point test, dynamic shear rheological test, and multi-stress recovery creep test, the effects of different types of snow-melting agents on the high-temperature performance, low-temperature performance, rheological properties, and creep properties of asphalt were investigated. Finally, the mechanism of action of different types of snow-melting agents on asphalt was analyzed by scanning electron microscopy, infrared spectroscopy, and differential scanning calorimetry. The results show that the chloride snow-melting agent has the greatest influence on the high-temperature performance, low-temperature performance, rheological properties, and creep properties of asphalt, followed by the mixed snow-melting agent, and the nonchloride snow-melting agent has the least influence on the high-temperature performance, low-temperature performance, rheological properties, and creep properties of asphalt. From the macroscopic experiment, microscopic experiment, and mechanism analysis of this article, it is suggested that nonchlorine salt snow-melting agent should be used in snow removal on snowy road surface.

젠더화된 트라우마의 전위된 스크린 기억

This study compares Snowy Road(2017) in Korea and Grbavica(2005) in Bosnia and Herzegovina and examines then the cinematic dismemberment of 'gendered trauma' related to women as wartime rapists. The following research process was carried out to examine these comparative films and visual culture research. Grbavica and Snowy Road were compared and discussed in turn as the study subjects. Speculation of the comfort women movie Snowy Road is about how the victims of the Japanese military comfort station during World War II are being reproduced as 'dislocated screen memories' by the present and the past. A speculation of the post-Yugoslavian cinema Grbavica is about the ‘postmemory’ related to the victim woman and daughter systematically perpetrated in the rape camp for the purpose of ethnic cleansing during the Bosnian Civil War. The studies of the two works, which focus on the aspects of women's harm caused by war, have been compared and reviewed, and the ethics of 'cosmopolitanism' is enhanced through the consideration of 'dislocated screen memory' of 'gendered trauma'.

Do Hoosiers Forget How to Drive in the Snow Each Year?

Abstract The winter season in many U.S. states includes snowfall, and with it comes comments about how drivers always seem to “forget” how to drive in snow when the first snowfall of the season occurs. This study assesses the accuracy of this popular sentiment during Indiana winters from 2007 to 2020. The number of motor vehicle crashes, injuries, and fatalities during the first snowfall of the season was compared with those during subsequent snow events. A grid of 46 cells was constructed to subdivide the state, and instances of snowfall and crashes were aggregated within each cell each day during the study period. Daily crash, injury, and fatality totals in each cell were normalized by their respective means and standard deviations, allowing for data from all cells to be combined into a single dataset. Four snow accumulation thresholds were examined: 1, 13, 25, and 51 mm. Distributions at each threshold show that more crashes occur on average on days with the first snowfall of the winter season than on other days with snowfall, regardless of the accumulation threshold used. Statistical tests support this result, showing significant differences between the mean numbers of crashes at each of the four snowfall thresholds. There were also significantly more injuries on the first snowfall day and more fatalities, although fatalities were only significant for the 13-mm snowfall threshold. Significance Statement The purpose of my research is to answer the question: are there more motor vehicle crashes on the first day with snow each winter when compared with the number of crashes on other days with snowfall in the state of Indiana? Using four snowfall thresholds of increasing amounts, statistical tests comparing daily crashes on first snowfall and other snowfall days showed that there were significantly more crashes on average on the first day with snowfall each winter, regardless of the amount of snow accumulation. This supports the popular notion that crashes occur more frequently the first time it snows each year, although it is more likely attributed to drivers reacclimating to snowy road conditions than to forgetfulness.

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%.

Analysis of Spatial Concentrations of Large-Truck Crashes Using Data Mining Methods

In recent years, the number of studies on crashes involving large-trucks has increased due to its importance to the economy and the higher chance of fatalities. However, none of the previous studies has given attention to the spatial concentrations of large-truck crashes. Moreover, the literature lacks exploration of granular level land use and urban design factors. The current study used the DBSCAN (Density-Based Spatial Clustering of Application with Noise) method to identify the spatial concentrations of crashes involving large-trucks. Additionally, the study explored housing, population, employment, and road network density attributes along with the crash characteristics, roadway attributes, location type, traffic conditions, driver’s action and behavior, and environmental factors. The association rule analysis was employed to discover the contributory factors that lead to no injury, non-severe and severe injuries at the spatial concentrations of crashes involving large-trucks. The findings indicated that the rear-end collisions involving drunk drivers often lead to severe injuries in large-truck crashes. Non-interstate roads, speed limit from 40 to 80 kilometers per hour, high road network density, medium and high population density are frequent conditions of non-severe injuries. Lastly, collisions between large-trucks and fixed objects, sideswipe same direction collisions, snowy roads, clear weather, medium road network and employment density are likely to facilitate no injury crashes involving large-trucks. Road traffic authorities can use these insights to reduce the frequency and severity of crashes involving large-trucks at their spatial concentrations.

System Dynamics Modeling for Estimating the Locations of Road Icing Using GIS

Road icing can cause large traffic accidents on highways because, unlike snowy roads, its location is difficult to identify and it can occur rapidly, even during rainy weather. In this study, the amount and location of road icing were modeled and simulated over time based on the system dynamics theory. The simulation is expressed on the geographic information system (GIS) and facilitates advance detection of the location and amount of road icing that occurs unexpectedly unlike previous studies. Modeling was designed to process spatial and meteorological data after combining them. The spatial data used for modeling were Hillshade, Water System, Bridge, and Road (Highway). Air temperature, cloudiness, vapor pressure, wind speed, and precipitation were used as meteorological data. The amount of road icing was estimated by scientifically designing the parameters related to its occurrence between spatial and meteorological data. Based on this, the amount of road icing by location was simulated per 1m2 using the GIS. The simulation results showed that the amount of road icing that began to increase from AM 08:00 reached its peak (an average of 213.62 g/m2) at noon and then slowly decreased. Additionally, when simulated with GIS, the sum amount of road icing between AM 12:00 and PM 13:00 was a maximum of 1707.292 (g/14 h) and a minimum of 360.082 (g/14 h) for each location. Hypothesis testing was conducted on whether road icing significantly occurs at actual points vulnerable to traffic accidents. Based on the results, the average significance level was calculated to be less than 0.05. Therefore, the alternative hypothesis that the model can estimate road icing in vulnerable areas was adopted. The verified simulation can be useful data to government agencies (e.g., road traffic authority) in their programs to prevent traffic accidents caused by road icing.

이청준의 「눈길」에 나타나는 카이로스의 시공간 연구

본 논문은 이청준의 단편소설 ｢눈길｣에 나타나는 카이로스 시공간 인식과 영적 카이로스 세계에 관한 연구이다. 이청준의 ｢눈길｣은 유한적인 인간이 직면하는 시간과 공간의 한계를 영적인 카이로스의 시공간으로 확장시켜 서정적 감동과 미학을 우리에게 안겨준 소설이다. 그의 ｢눈길｣ 속에 내재한 카이로스의 시공간에 대한 연구를 통해서, 정신적으로 귀향하지 못하고 실존적 상처를 안고 사는 우리가 어떻게 영적인 소통 과정을 거쳐 진정한 화해와 사랑에 이르는지를 묻고 찾았다.<BR> 시간의 측면에서 볼 때 소설 ｢눈길｣은 물리적인 시간인 크로노스 시간에 대하여 화해의 시간인 카이로스 시간에로의 소통을 하게 된다. 카이로스의 시간은 우리가 가진 실존적 상처를 치유케 하는 ‘영적 카이로스’의 시간이다. 공간 이미지와 관련하여 ｢눈길｣은 시간과 공간에 대한 전통적인 인식을 정지시키고, 크로노스 공간의 한계에서 자유로워지게 되고 카이로스 공간에 진입하여 순간적으로 초월을 맛보면서 화해와 사랑을 교감하게 된다.<BR> 이러한 분석을 통해, ｢눈길｣은 카이로스의 시공간에서 과거의 아픔과 상처를 ‘영혼의 눈’으로 소환하고 소통함으로써, 크로노스의 시공간에서의 원죄, 아픔, 부끄러움이 화해와 사랑의 문으로 이르게 되는 것을 살펴볼 수 있다.

Characteristics of Traffic Accidents on Highways: An Analysis Based on Patients Treated at a Regional Trauma Center

Purpose There have been increasing concerns about serious traffic accidents on highways. The purpose of this study was to analyze factors affecting traffic accidents on highways and the severity of the resulting injuries. Methods This retrospective study was conducted at a regional trauma center. We reviewed 594 patients who had been in 114 traffic accidents on highways from January 2018 to June 2020. We collected demographic data, clinical data, accident-related factors, and meteorological data (weather and temperature). Results Environmental risk factors were found to be significantly associated with the incidence of traffic accidents on highways. Injury severity and the death rate were higher in sedans than in any other type of vehicle. Tunnels were the most common location of accidents, accounting for 47 accidents (41.2%) and 269 injured patients (45.3%). The injury severity of individuals riding in the driver’s seat (front seat) was high, regardless of vehicle type. Three meteorological risk factors were found to be significantly associated with traffic accidents: rainy roads (odds ratio [OR] 2.08; 95% confidence interval [CI] 1.84–3.29; p=0.01), icy or snowy roads (OR 5.12; 95% CI 2.88–7.33; p&lt;0.01), and foggy conditions (OR 2.94; 95% CI 2.15–4.03; p&lt;0.05). Conclusions The injury severity of patients was affected by seat position and type of vehicle, and the frequency of accident was affected by the location. The incidence of traffic accidents was strongly influenced by meteorological conditions (rain, snow/ice, and fog).

Do Driver Characteristics and Crash Conditions Modify the Effectiveness of Automatic Emergency Braking?

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Studies of automatic emergency braking (AEB) find that AEB-equipped vehicles are around half as likely to crash. This study examines whether driver characteristics and road and weather conditions modify this preventive effect of AEB.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Toyota production data were merged with police reported crash files from eight U.S. states for crash years 2015 up to 2019 by 17-digit vehicle identification number (VIN). Using a case-control design, this study investigated the relationship of AEB presence with being a case vehicle in a system-relevant crash (the striking vehicle in front-to-rear crash; n=30,056) versus an AEB non-relevant control vehicle (the struck vehicle in a front-to-rear crash; n=62,820). The analysis was stratified by driver characteristics and by weather and road conditions. Logistic regression modeled the relationship, controlling for exposure (vehicle-days) and possible confounding factors. The resulting odds ratios for AEB equipment from the separate models were compared to determine if the effect of AEB presence was modified by the characteristic or condition of interest.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Overall, AEB-equipped vehicles were 43% (p&amp;lt;0.001) less likely to be the striking (case) vehicle compared to non-equipped vehicles. However, the preventive effect of AEB was significantly lower among older drivers (over 65 years) compared to younger drivers; 29% less likely to be a striking vehicle (OR=0.71) versus 46% (OR=0.54), respectively. The effect of AEB was also lower in adverse weather conditions (rain, fog, snow) (OR=0.66) and on wet or snowy roads (OR=0.65), though these differences were not significant compared to clear weather and dry roads. The AEB effect was also lower among risk-taking drivers (alcohol-involved, speeding, or unrestrained) compared to non-risk-taking (OR=0.72 versus OR=0.59, respectively).&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;AEB prevents crashes, regardless of driver characteristics and environmental conditions. This study suggests, however, that the size of the effect is smaller among older and risk-taking drivers, and in adverse weather and road conditions.&lt;/div&gt;&lt;/div&gt;

A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions

This article proposes a novel longitudinal vehicle speed estimator for snowy roads in extreme conditions (four-wheel slip) based on low-cost wheel speed encoders and a longitudinal acceleration sensor. The tire rotation factor, η, is introduced to reduce the deviation between the rotation tire radius and the manufacturer’s marked tire radius. The Local Vehicle Speed Estimator is defined to eliminate longitudinal vehicle speed estimation error. It improves the tire slip accuracy of four-wheel slip, even with a high slip rate. The final vehicle speed is estimated using two fuzzy control strategies that use vehicle speed estimates from speed encoders and a longitudinal acceleration sensor. Experimental and simulation results confirm the algorithm’s validity for estimating longitudinal vehicle speed for four-wheel slip in snowy road conditions.

Saturation Flow Rate of Urban At-Grade Signalized Intersection Under Different Climatic Conditions (Case Study: Sattari-Mokhberi Intersection)

This research aimed to compare the saturation flow rates of an urban at-grade signalized intersection under different climatic conditions in order to optimize the traffic light control system of this intersection. Data collection was performed with a video camera. The case studied was the intersection of Sattari-Mokhberi Intersection, in Tehran. The road surface conditions occurring in this intersection were divided into three classes: dry, wet, and snowy. Modeling and simulations were performed with the help of the software Vissim and Synchro. Model calibration was performed using the parameters “desired speed” and “acceleration/deceleration at zero speed”. Data analysis was performed using descriptive statistics and inferential statistics, including analysis of variance (ANOVA), Kolmogorov–Smirnov test, and Tukey and Scheffe tests. The saturation flow for dry, wet, and snowy road conditions was estimated to 1967, 1722, and 1607, respectively. Also, the relationship between saturation headway and sight distance in meters was modeled linearly and logarithmically. The analysis of videos showed that the mean desired speed in wet and snowy road conditions was, respectively, 23% and 36% lower than in normal conditions. The mean desired acceleration at zero speed in wet and snowy road conditions was, respectively, 24% and 30% lower than normal. The simulation results showed a 15% reduction in the total delay of the intersection after the implementation of weather-responsive schedules. The results of this research show that the benefits of adjusting the schedule of traffic lights based on weather are more pronounced in snowy conditions than in rainy conditions.

System for monitoring road slippery based on CCTV cameras and convolutional neural networks

The slipperiness of the surface is essential for road safety. The growing number of CCTV cameras opens the possibility of using them to automatically detect the slippery surface and inform road users about it. This paper presents a system of developed intelligent road signs, including a detector based on convolutional neural networks (CNNs) and the transfer-learning method employed to the processing of images acquired with video cameras. Based on photos taken in different light conditions by CCTV cameras located at the roadsides in Poland, four network topologies have been trained and tested: Resnet50 v2, Resnet152 v2, Vgg19, and Densenet201. The last-mentioned network has proved to give the best result with 98.34% accuracy of classification dry, wet, and snowy roads.

Five Total Strangers by Natalie D. Richards

Reviewed by: Five Total Strangers by Natalie D. Richards Natalie Berglind Richards, Natalie D. Five Total Strangers. Sourcebooks Fire, 2020 [320p] Paper ed. ISBN 9781492657217 $10.99 Reviewed from digital galleys R Gr. 9-12 It's Christmas Eve, and all eighteen-year-old Mira wants to do is get home to her grief-wrought mother for the anniversary of Aunt Phoebe's death. When a blizzard interrupts her flight itinerary, she accepts a rental car ride with a college girl from the plane and three of that girl's friends—or are they? As if driving through a blizzard weren't scary enough, it becomes evident that the passengers are strangers to one another, someone is stealing crucial items for navigation, charging ports and snow chains have been tampered with, and one of the passengers is lying. Richards is a master of tension, playing on worst-case scenarios of winter driving and racking up the paranoia of the car's occupants as each engages in suspicious behavior to the others. Each of the five are fleshed-out, realistic characters that equally induce sympathy and doubt—plausibly, any of them could be the culprit, and the terror is only amplified by interspersed letters sent to Mira (but never received) by a stalker. Suspense fans will get all the ups-and-downs of a well-paced narrative, but they may never want to drive on a snowy road again. [End Page 101] Copyright © 2020 The Board of Trustees of the University of Illinois

Analysis of cornering response and stability of electrified heavy commercial road vehicles with regenerative braking

Additional powertrain components and regenerative braking are two important factors that may affect the performance and stability of electrified vehicle cornering. The location of additional components affects the vehicle’s center of gravity (CG) position and thereby the stability of the vehicle. As regenerative braking is possible only on driven wheels, the brake force distribution between front and rear wheels may not follow the ideal brake force distribution curve. Hence, applying maximum regenerative braking during cornering may affect vehicle stability, and this has motivated the analysis presented in this paper. The scope of this research work includes obtaining a model for the regenerative brake system, which was then used to analyze the heavy commercial road vehicle lateral dynamic response during combined cornering and regenerative braking. A sensitivity study was carried out regarding variations in center of gravity, longitudinal speed, and tire–road traction coefficient [Formula: see text]. The IPG TruckMaker® vehicle simulation software running in a hardware-in-loop experimental system was used to study the heavy road vehicle cornering performance. The results showed that applying braking on a constant radius path required correction in the steering input to follow the desired path. However, the amount of steering correction required during regenerative braking was higher than that with conventional friction braking. Moreover, applying maximum regenerative braking at higher longitudinal speeds on snowy roads and split- µ roads has a higher impact on vehicle cornering performance compared with that on dry roads. Furthermore, a co-operative braking strategy with an optimum brake force sharing between regenerative braking and friction braking was developed to improve the electrified heavy commercial road vehicle’s cornering stability and handling performance during cornering and braking.

Modelling and Validation of An Electronic Wedge Brake System with Realistic Quarter Car Model for Anti-Lock Braking System Design

With the advancement in battery and electronics technologies, soon Electric Vehicles (EV) will replace traditional vehicles as they are more efficient and environment friendly. This will require replacement of all mechanical systems in vehicles with their electrical counterparts. This study focuses on electromechanical brakes (EMB) as replacement of hydraulics brakes. Particularly a type of EMB known as Electronic Wedge Brake (EWB) which uses wedges to create self reinforcing braking force and consume less power than other EMBs. Detailed mathematical model of an EWB system is presented which provides braking force and torque to the disk brake. A Quarter Car Model (QCM) with realistic parameter values and aerodynamic deceleration is modelled to validate the EWB system. The system is validated for different road conditions and anti-lock braking system (ABS) is demonstrated for snowy road using a single PID controller. The results validate the brake and car model and a need for cascaded control strategy to implement ABS is established.