Braking Patterns Research Articles

Heavy Braking Patterns and their Applicability to Assess Traffic Conflicts

Heavy Braking Patterns and their Applicability to Assess Traffic Conflicts

A Spatial-Temporal Analysis of Brain Activation to Explain the Take-Over Failure in Conditionally Automated Driving.

This study aims to investigate the causes of take-over failures in conditional automated driving with spatial-temporal analysis of brain zone activation. Take-over requires a human driver to resume the control of the vehicle when its automation system disengages. Existing studies have found that take-over failures occur frequently on some drivers, but the causes have not been thoroughly studied. In a driving simulator experiment, 40 drivers took over in critical freeway cut-in situations. Functional near-infrared spectroscopy (fNIRS) data were collected before and during the take-over process to evaluate brain zone activation. Successful and failed take-overs were compared with changes in fNIRS data based on spatial-temporal comparisons and cluster analysis. The results suggested a significant difference in temporal brain activation between take-over failure and success conditions. Take-over failure conditions are mostly related to earlier and longer brain activation in most brain zones and repeated activation of the cognition brain zones. Drivers' attention switches, steering, and braking patterns are also related to different brain zone activation orders. The results indicate the need to reduce the mental workload caused by the sudden system disengagement to prevent take-over failure. Future research and implementation should focus on earlier warnings of upcoming hazards and driver education in dealing with sudden system disengagement.

A Sustainable Free-Standing Triboelectric Nanogenerator Made of Flexible Composite Film forBrake Pattern Recognition in Automobiles.

In recent years, the automotive industry has made significant progress in integrating multifunctional sensors to improve vehicle performance, safety, and efficiency. As the number of integrated sensors keeps increasing, there is a growing interest in alternative energy sources. Specifically, self-powered sensor systems based on energy harvesting are drawing much attention, with a main focus on sustainability and reducing reliance on typical batteries. This paper demonstrates the use of triboelectric nanogenerators (TENGs) in a computer mouse for efficient energy harvesting and in automobile braking systems for safety applications using SrBi2Ta2O9 (SBTO) perovskite, blended PDMS composite operating in free-standing mode with an interdigitated patterned aluminum electrode. This self-powered sensor is capable of distinguishing between normal and abnormal braking patterns using digital signal processing techniques. It is noteworthy that the addition of 15%wt. of the SBTO in PDMS composite-based TENG delivered 13.5V, 45nA, and an output power of 0.98µW. This new combination of energy harvesting and safety applications enables real-time monitoring and predictive maintenance in the automotive industry.

Facilitate Driving with Geographical Proximity

Abstract: This research paper describes the development of a website based on remote computing with the aim of improving the connection between driving schools and automobile students. The primary purpose of this web application is to provide administrators, parents, and students with actual vehicle information, including clutch usage, speed, braking patterns, and vehicle location. This is achieved through the integration of remote data processing installed in the vehicle and sending the relevant data to web applications. This article discusses the importance of these systems in improving driver training and safety by allowing participants to remotely monitor vehicle operation, driving behavior and actions. We completed the literature review to examine existing telematics systems and their applications in the automotive industry. The work includes the selection and installation of remote data processing equipment, the development of user- friendly web interfaces using modern programming languages and methods, and scrupulous compliance with technical procedures. The main features of the web application include real-time data tracking, GPS tracking, important event alerts and notifications, and reporting and analysis capability. The implementation process is quite detailed, including hardware integration, front-end and back-end development, data storage and security measures.

A distributed simulation study to investigate pedestrians’ road-crossing decisions and head movements in response to different vehicle kinematics in mixed traffic

The impending deployment of automated vehicles (AVs) will lead to mixed traffic conditions, where pedestrians will be required to interact with both AVs and human-driven vehicles. For traffic flow to be safe and efficient, AVs’ understanding of pedestrians’ behaviour and intention is as important as pedestrians’ perception of AVs’ status and intent. To investigate pedestrians’ road-crossing decisions and interactive behaviour in mixed traffic, a distributed simulation study was developed by linking a CAVE-based pedestrian simulator and a desktop driving simulator. Twenty-five pairs of pedestrians and drivers were recruited, and each pair experienced 32 trials, where pedestrians decided to cross (or not) before an approaching vehicle at an un-signalised, single-lane, road. The driving pattern of the approaching vehicle (controlled by either a predefined program or human driver) and braking mode (braking/non-braking) were manipulated. For the predefined vehicles, the braking pattern was subdivided into hard braking and soft braking to provide more kinematic variability. Human drivers were also instructed to yield, or not, in different trials. Pedestrians’ road-crossing decisions and head movements were recorded and analysed. Results revealed a significant difference in head-turning patterns between crossing and non-crossing manoeuvres, demonstrating pedestrians’ head movements as a valid indicator of their road-crossing intentions. Moreover, results identified a ‘last moment check’ behaviour before pedestrians’ crossing initiation, with a significant increase in head-turning during the last 2 s. Finally, pedestrians made a similar percentage of road crossings and displayed a similar pattern of head movements, in response to human-driven and predefined vehicles, suggesting that the difference of implicit cues in the current mixed traffic setup does not impact their behaviour prior to road crossings. The findings from this study extend our knowledge of how pedestrians behave when crossing the road in mixed traffic, particularly in terms of their head-turning behaviour. We hope this information can be used by future AVs to better predict pedestrians’ road-crossing intentions in urban settings.

Emergency Braking Patterns of Trained and Untrained Riders

The complexity of motorcycle braking systems remains in the different applications of brakes using both hands and legs. In comparison to passenger vehicle systems that use brake pedals to slow down, motorcycles usually have independent front and rear braking. Furthermore, motorcycles have a high center of mass with a short wheelbase. This may result in loss of control during harsh braking. Thus, the motorcyclist's braking technique is important in producing safe braking. The objective of this project is to understand the difference between the braking technique of people who have attended defensive riding training (trained) with those people who did not attend (untrained). Both trained and untrained riders used higher rear braking force than front braking force at all test speeds. The trained riders improved the front braking force by 26.8% and reduced the rear braking force by 18.2% when the test speed changed from 30 km/h to 50 km/h. Thus, the deceleration produced by trained riders linearly increased with the increment of speed. On the other hand, untrained riders improved front and rear braking force by 15.6% and 3.4% respectively from 30 km/h to 40km/h. No change in braking force was found when the speed increased from 40 to 50 km/h, thus producing similar deceleration at both speeds respectively. This may increase the risk to the rider because, at higher speeds, the motorcycle produces a higher braking distance and time if a similar force is applied. Further analysis shows that trained riders used deceleration in the range of 0.40-0.49 at the speed of 30 km/h, and adjusted the motorcycle deceleration to 0.60-0.69 at the test speed of 40 and 50 km/h. Nevertheless, the untrained riders produced deceleration in the range of 0.5-0.59 in all test speeds.

Research on Regenerative Braking Control Strategy for Single-Pedal Pure Electric Commercial Vehicles

In recent years, with the increasing severity of energy and environmental issues, countries have vigorously developed the new energy automotive industry. To reduce the difficulty of driver operation and increase endurance mileage, this article proposes a regenerative braking control strategy for a single-pedal pure electric commercial vehicle. Firstly, the single-pedal control system’s hierarchical approach was designed to contain the driver’s intention analysis and torque calculation layers. After identifying the driver’s intention, a logic threshold method was used to determine the braking pattern. Then, a fuzzy theory was used, with road gradient, braking strength, and speed as input parameters, and the ratio coefficient of braking force as the output parameter. A hybrid regenerative braking strategy was formulated based on the ideal distribution curve. Finally, the proposed strategy was verified through simulation and a constant-speed car-following experiment. The constant-speed car-following experiment results show that the maximum optimization rate of energy consumption provided by the proposed single-pedal regenerative braking control strategy is 5.81%, and the average optimization rate is 4.33%. This strategy can effectively reduce energy consumption and improve the economic performance of single-pedal pure electric commercial vehicles.

Driving behavior analysis and classification by vehicle OBD data using machine learning.

The transportation industry's focus on improving performance and reducing costs has driven the integration of IoT and machine learning technologies. The correlation between driving style and behavior with fuel consumption and emissions has highlighted the need to classify different driver's driving patterns. In response, vehicles now come equipped with sensors that gather a wide range of operational data. The proposed technique collects critical vehicle performance data, including speed, motor RPM, paddle position, determined motor load, and over 50 other parameters through the OBD interface. The OBD-II diagnostics protocol, the primary diagnostic process used by technicians, can acquire this information via the car's communication port. OBD-II protocol is used to acquire real-time data linked to the vehicle's operation. This data are used to collect engine operation-related characteristics and assist with fault detection. The proposed method uses machine learning techniques, such as SVM, AdaBoost, and Random Forest, to classify driver's behavior based on ten categories that include fuel consumption, steering stability, velocity stability, and braking patterns. The solution offers an effective means to study driving behavior and recommend corrective actions for efficient and safe driving. The proposed model offers a classification of ten driver classes based on fuel consumption, steering stability, velocity stability, and braking patterns. This research work uses data extracted from the engine's internal sensors via the OBD-II protocol, eliminating the need for additional sensors. The collected data are used to build a model that classifies driver's behavior and can be used to provide feedback to improve driving habits. Key driving events, such as high-speed braking, rapid acceleration, deceleration, and turning, are used to characterize individual drivers. Visualization techniques, such as line plots and correlation matrices, are used to compare drivers' performance. Time-series values of the sensor data are considered in the model. The supervised learning methods are employed to compare all driver classes. SVM, AdaBoost, and Random Forest algorithms are implemented with 99%, 99%, and 100% accuracy, respectively. The suggested model offers a practical approach to examining driving behavior and suggesting necessary measures to enhance driving safety and efficiency.

Smart Metering and Machine Learning for the Quantification of Regenerative Braking Energy on the d.c. Railway Systems

Nowadays, there is a great interest in improving the energy efficiency in DC railway systems. The recovery of braking energy is one of the most promising solutions. However, this solution requires to engage modifications on the railway system such as installing braking energy storage or transforming the existing DC substations in reversible substations. Thus, before engaging such costly modifications, it is necessary to have a reliable quantification of the energy that can be saved. In this paper, we are concerned with this problem. For this purpose, a novel approach based on smart metering and machine learning is proposed to detect the regenerative braking patterns from collected data. The performed experiments show the efficiency of the considered method.

A Recognition Method of Truck Drivers’ Braking Patterns Based on FCM-LDA2vec

Taking truck drivers' braking patterns as the research objects, this study used a large amount of truck running data. A recognition method of truck drivers' braking patterns was proposed to determine the distribution of braking patterns during the operation of trucks. First, the segmented data of braking behaviors were collected in order to extract 25 characteristic parameters. Additionally, seven main correlation factors were obtained by dimensionality reduction. The FCM clustering algorithm and CH scores were used to identify nine categories of truck drivers' braking behaviors. Then the LDA2vec model was used to identify the distribution of different braking behavior words in braking patterns, and three categories of truck drivers' braking patterns were identified. The test results showed that the accuracy of the truck drivers' braking pattern recognition model based on LDA2vec was higher than 85%, and braking patterns of drivers in the daily operation process could be mined from vehicle operation data. Furthermore, through the monitoring and pre-warning of the braking patterns and targeted training of drivers, traffic accidents could be avoided. At the same time, this paper's results can be used to protect human life and health and reduce environmental pollution caused by traffic congestion or traffic accidents.

Effects of Payload on Non-exhaust PM Emissions from Hybrid Electric Vehicle during Braking Sequences

Abstract Vehicles equipped with internal combustion engines are known as one of the most important Particulate Matters (PM) emissions sources. Many countries are aware of this issue and interested in employing more electric vehicles to reduce this emission. However, various past research claims that electric vehicles also emitted PM as conventional vehicles due to their non-exhaust emissions such as brake wear, tyre wear, road surface wear, and road dust resuspension. In addition, substantial evidence showed that there was indeed a positive correlation between the weight of vehicles and amounts of non-exhaust PM emissions. This study aims to measure on-road non-exhaust PM emissions from a hybrid electric vehicle during braking sequences. An onboard PM measuring device is attached to the side of the tested hybrid electric vehicle. PM measurements are monitored during the braking sequence in the electrified vehicle mode. Studies of increasing payloads that might affect the tendency of non-exhaust PM emissions are observed. The PM emission pattern during the braking sequence is captured by the current PM measuring setup as seen in the literature. The braking pattern (hard vs. soft brake) shows distinct amounts in PM emissions by a factor of two. Based on experimental data, it is found out that the additional payload of approximately 70 kg increases the amount of non-exhaust PM emissions by almost 20%.

Making a few talk for the many – Modeling driver behavior using synthetic populations generated from experimental data

Understanding driver behavior is the basis for the development of many advanced driver assistance systems, and experimental studies are indispensable tools for constructing appropriate driver models. However, the high cost associated with testing is a serious obstacle in collecting large amounts of experimental data. This paper presents a methodology that can improve the reliability of results from experimental studies with a limited number of participants by creating a virtual population. Specifically, a methodology based on Bayesian inference has been developed, that generates synthetic cases that adhere to various real-world constraints and represent possible variations of the observed experimental data. The application of the framework is illustrated using data collected during a test-track experiment where truck drivers performed a right turn maneuver, with and without a cyclist crossing the intersection. The results show that, based on the speed profiles of the dataset and physical constraints, the methodology can produce synthetic speed profiles during braking that mimic the original curves but extend to other realistic braking patterns that were not directly observed. The models obtained from the proposed methodology have applications for the design of active safety systems and automated driving, demonstrating thereby that the developed framework has great promise for the automotive industry.

Toward Human-Centered Design of Automated Vehicles: A Naturalistic Brake Policy

While safety is the ultimate goal in designing Connected and Automated Vehicles (CAVs), current automotive safety standards fail to explicitly define rules and regulations that ensure the safety of CAVs or those interacting with such vehicles. This study investigates CAV safety in mixed traffic environments with both human-driven and automated vehicles, focusing particularly on rear-end collisions at intersections. The central hypothesis is that the primary reason behind these crashes is the potential mismatch between CAVs’ braking decisions and human drivers’ expectations. To test this hypothesis, various Artificial Intelligence (AI) techniques, along with specialized statistical methods are adopted to learn and model the braking behavior of human drivers at intersections and compare the results to that of CAVs. Findings suggest systematical differences in CAVs’ and humans’ braking trajectories, revealing a mismatch between their braking patterns. Accordingly, a Markovian decision modeling framework is adopted to design a novel CAV braking profile that ensures 1) compatibility with human expectation, and 2) safe and comfortable maneuvers by CAVs in mixed driving environments. The findings of this study are expected to facilitate the development of higher levels of vehicle automation by providing guidelines to prevent rear-end collisions caused by existing differences in CAVs’ and humans’ braking strategies.

Analysis of Driving Dynamics Considering Driving Resistances in On-Road Driving

Internal combustion engine emissions are a serious worldwide problem. To combat this, emission regulations have become stricter with the goal of reducing the proportion of transportation emissions in global air pollution. In addition, the European Commission passed the real driving emissions–light-duty vehicles (RDE-LDV) regulation that evaluates vehicle emissions by driving on real roads. The RDE test is significantly dependent on driving conditions such as traffic or drivers. Thus, the RDE regulation has the means to evaluate driving dynamics such as the vehicle speed per acceleration (v·apos) and the relative positive acceleration (RPA) to determine whether the driving during these tests is normal or abnormal. However, this is not an appropriate way to assess the driving dynamics because the v⋅apos and the RPA do not represent engine load, which is directly related to exhaust emissions. Therefore, in the present study, new driving dynamic variables are proposed. These variables use engine acceleration calculated from wheel force instead of the acceleration calculated from the vehicle speed, so they are proportional to the engine load. In addition, a variable of driving dynamics during braking is calculated using the negative wheel force. This variable can be used to improve the accuracy of the emission assessment by analyzing the braking pattern.

What driving style makes pedestrians think a passing vehicle is driving automatically?

An important question in the development of automated vehicles (AVs) is which driving style AVs should adopt and how other road users perceive them. The current study aimed to determine which AV behaviours contribute to pedestrians' judgements as to whether the vehicle is driving manually or automatically as well as judgements of likeability. We tested five target trajectories of an AV in curves: playback manual driving, two stereotypical automated driving conditions (road centre tendency, lane centre tendency), and two stereotypical manual driving conditions, which slowed down for curves and cut curves. In addition, four braking patterns for approaching a zebra crossing were tested: manual braking, stereotypical automated driving (fixed deceleration), and two variations of stereotypical manual driving (sudden stop, crawling forward). The AV was observed by 24 participants standing on the curb of the road in groups. After each passing of the AV, participants rated whether the car was driven manually or automatically, and the degree to which they liked the AV's behaviour. Results showed that the playback manual trajectory was considered more manual than the other trajectory conditions. The stereotype automated ‘road centre tendency’ and ‘lane centre tendency’ trajectories received similar likeability ratings as the playback manual driving. An analysis of written comments showed that curve cutting was a reason to believe the car is driving manually, whereas driving at a constant speed or in the centre was associated with automated driving. The sudden stop was the least likeable way to decelerate, but there was no consensus on whether this behaviour was manual or automated. It is concluded that AVs do not have to drive like a human in order to be liked.

Estimation Method of Energy Consumption Using Train Operation Power Simulator

Recently, railway companies are introducing energy-saving technologies, such as energy storage systems, energy-saving vehicles, and energy-saving driving operations. This results in the need for a method to accurately estimate the energy-saving effect of each technology. To this end, we developed a method to estimate of train operation energy consumption using the Train Operation Power Simulator. First, we developed a physical calculation algorithm of the Train Operation Power Simulator, unitizing the simultaneous measurements of substations and rolling stocks within a limited feeding section. Then, we developed an estimation method of commercial train operation energy consumption, unitizing the measurements of substations and commercial running data obtain from a train data collection device. We conclude that speed profile, running resistance, and auxiliary power of rolling stock are important for the energy consumption estimation. This study employs an algorithm of the speed profile generator for energy consumption estimation and a randomized braking method that adopts braking patterns derived from actual driving patterns obtained from commercial running data. In addition, this study employs the characteristics of running resistance obtained from running tests and the commercial running data, and the characteristics of auxiliary power obtained from commercial running data.

Energy storage traction power supply system and control strategy for an electrified railway

To solve the negative sequence (NS) problem and enhance the regenerative braking energy (RBE) utilisation in an electrified railway, a novel energy storage traction power supply system (ESTPSS) is proposed in this study. In the new system, a power flow controller is adopted to compensate for the NS, and a super-capacitor energy storage system is applied to absorb and release the RBE. In addition, through the cooperation of each part, the proposed power supply system can provide continuous power without neutral sections. The NS compensation principle of the new system is illustrated. Depending on the operational state of the traction load, the control model is presented for different working patterns, including a regenerative braking pattern, a peak clipping pattern and a valley filling pattern. Moreover, a hierarchical control strategy including upper energy management and low-layer converter control is proposed, which effectively coordinate switches between the different working patterns. Finally, the validity of the control method and the excellent performance of the system are demonstrated by a case study. The results indicate that compared to a conventional TPSS, the ESTPSS increases the RBE utilisation of the system by 83%.

Temperature-dependent wear of tread-braked railway wheels

The paper describes the results of brake dynamometer experiments conducted to evaluate the influence of the tread temperature caused by tread braking on the wear rate of railway wheel tread. Employing a full-scale tread brake dynamometer composed of a brake block, a wheel, and a railwheel, wheel-rail rolling contact was imposed on the wheel tread surface heated under several braking scenarios. In dynamometer tests simulating the braking patterns of an actual vehicle exposed to unexpected hollow tread wear, no tread wear was observed after repetitive station stop braking, whereas tread wear was observed only for the rolling contact region after repetitive holding braking for load cases in which the internal tread temperature reached 300 °C. The wear rate of the rolling contact region was determined for various temperatures by performing experiments in which rolling contact was imposed at a constant tread temperature. The wear rate of the wheel tread increased rapidly with temperature at the tread surface for internal temperatures of 200 °C–300 °C. The wear volume obtained in the experiment was converted and generalized to determine the temperature dependence of the wear rate of the wheel tread as a function of the travel distance. The surface temperature of the tread and the internal temperature distribution were computed by FEM. The maximum temperature on the tread surface was estimated to range from 240 °C to 380 °C. The hollow wear of tread-braked railway wheels is primarily attributed to plastic deformation of the tread surface caused by hot rolling, i.e., rolling under a softened state with an increased temperature of the wheel steel on the tread surface. Verification of thermally activated process using Arrhenius equation confirms this conclusion.

A Pilot Recommender System using K-Means Clustering to Find Desirable Paths in Aircraft Takeoffs

This paper aims to study the geometric patterns produced by aircrafts on different takeoff paths and to establish a correlation between the fuel consumption and the geometry of the path. Based on the findings of the study, pilots could be advised to change their flying styles and strategy during take-off on a path that maximizes fuel conservation. This is validated by grouping the similar takeoff paths using the k-means clustering technique and by verifying linear relationship between the parameters of different paths in the clusters and their corresponding braking patterns. In addition, various runways are classified in order to study the variations in the takeoff paths.

E-bikers’ braking behavior: Results from a naturalistic cycling study

Objective: The number of e-bike users has increased significantly over the past few years and with it the associated safety concerns. Because e-bikes are faster than conventional bicycles and more prone to be in conflict with road users, e-bikers may need to perform avoidance maneuvers more frequently. Braking is the most common avoidance maneuver but is also a complex and critical task in emergency situations, because cyclists must reduce speed quickly without losing balance. The aim of this study is to understand the braking strategies of e-bikers in real-world traffic environments and to assess their road safety implications. This article investigates (1) how cyclists on e-bikes use front and rear brakes during routine cycling and (2) whether this behavior changes during unexpected conflicts with other road users.Methods: Naturalistic data were collected from 6 regular bicycle riders who each rode e-bikes during a period of 2 weeks, for a total of 32.5 h of data. Braking events were identified and characterized through a combined analysis of brake pressure at each wheel, velocity, and longitudinal acceleration. Furthermore, the braking patterns obtained during unexpected events were compared with braking patterns during routine cycling.Results: In the majority of braking events during routine cycling, cyclists used only one brake at a time, favoring one of the 2 brakes according to a personal pre-established pattern. However, the favored brake varied among cyclists: 66% favored the rear brake and 16% the front brake. Only 16% of the cyclists showed no clear preference, variously using rear brake, front brake, or combined braking (both brakes at the same time), suggesting that the selection of which brake to use depended on the characteristics of the specific scenario experienced by the cyclist rather than on a personal preference. In unexpected conflicts, generally requiring a larger deceleration, combined braking became more prevalent for most of the cyclists; still, when combined braking was not applied, cyclists continued to use the favored brake of routine cycling. Kinematic analysis revealed that, when larger decelerations were required, cyclists more frequently used combined braking instead of single braking.Conclusions: The results provide new insights into the behavior of cyclists on e-bikes and may provide support in the development of safety measures including guidelines and best practices for optimal brake use. The results may also inform the design of braking systems intended to reduce the complexity of the braking operation.