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A GPS-based Algorithm for Brake and Turn Detection
Abstract
Driving behavior recognition is a notable topic in travel safety, as transportation and insurance companies could adopt effective tools to detect unsafe driving and internalize the associated costs. Different driving events and the related severity must be detected to distinguish abnormal behaviors. The global positioning system (GPS) provides useful information regarding the location of the vehicle at any time and is vastly used in various devices such as smartphones and GPS trackers. Other sensors, on the other hand, provide complementary valuable information but their implementation requires extra costs and more complex and intensive algorithms. We developed a threshold-based algorithm to detect the turning and braking of vehicles using the GPS sensor. The data contained 11 trips with a frequency of 1 Hz with a total duration of 2.7 h. The algorithm utilizes a supplementary map matching and a relabeling technique to boost the accuracy and yet preserve the reasonable computation load. The overall precision and recall rate of the turn-detecting model are respectively 77.5% and 92.5%. Also, this algorithm can detect braking events with a precision of 68.18% and a recall of 83.33%. To address the concerns about the overfitting, we tested our algorithm on a secondary dataset, and nearly similar values of accuracy were resulted, showing the flexible nature of our algorithm while dealing with a different set of driving behaviors and road characteristics. Additionally, a sensitivity analysis showed the sensitive nature of the brake detection algorithm, in contrast with the turn detection algorithm. Overall, our algorithm showed promising results and can be a pioneer one in the field of low-cost detection algorithms built for smartphones or GPS trackers possessed by various trucking and car insurance companies.
... Motion data of individual vehicles such as acceleration, velocity, angular velocity, and orientation data is recorded in these datasets using OBD (On-Board Diagnostic) devices or smartphones. The behavior of the drivers is also recorded alongside the data collection using questionnaires or labeling some specific events to investigate the validity of the methodologies (Chan et al., 2020;Kazemeini et al., 2022). Despite the existence of such efforts, the robustness of previous works has remained an open area of research. ...
Driving behavior monitoring by smartphone sensors is one of the most investigated approaches to ameliorate road safety. Various methods are adopted in the literature; however, to the best of our knowledge, their robustness to the prediction of new unseen data from different drivers and road conditions is not explored. In this paper, a two-phase Machine Learning (ML) method with taking advantage of high-pass, low-pass, and wavelet filters is developed to detect driving brakes and turns. In the first phase, accelerometer and gyroscope filtered time series are fed into Random Forest and Artificial Neural Network classifiers, and the suspicious intervals are extracted by a high recall. Following that, in the next phase, statistical features calculated based on the obtained intervals are used to determine the false and true positive events. To compare the predicted and real labels of the recorded events and calculate the accuracy, a method that covers the limitations of previous sliding windows is also employed. Real-world experimental result shows that the proposed method can predict new unseen datasets with average F1-scores of 71% in brake detection and 82% in turn detection which is comparable with previous works. Moreover, by sensitivity analysis of our proposed model, it is proven that implementing high-pass and low-pass filters can affect the accuracy for turn detection up to 30%.
Quality of life in Egypt, and especially in Cairo, has recently become one of the main priorities of the country. The provision of the public and green open spaces is one of the priorities in this regard. Cairo suffers from acute shortage of green space provision which is negatively affecting the main urban area and the quality of life. There have been numerous efforts regarding green spaces provision in Cairo lately, from governmental, non-governmental bodies, as well as the private sector. In addition to Governmental efforts, there are now a growing number of initiatives and NGOs calling for greening Cairo to improve the quality of life for the residents and helping in reducing pollution rates, either through better aesthetics or for economic revenues such as urban agriculture. These calls depend on small scale interventions and incremental change. On the other hand, the private sector is now supplying several green spaces as well.
In this regard, the paper aims at mapping out the different approaches of green spaces provision in Cairo, their motives and aims as well as the kind of commodities that they provide, especially in terms of their social impact. This shall be achieved through literature review, mapping out the different efforts in addition to interviews with the different stakeholders
A cost-effective approach to gather information in a smart city is to embed sensors in vehicles such as buses. To understand the limitations and opportunities of this model, it is fundamental to investigate the spatial coverage of such a network, especially in the case where only a subset of the buses have a sensing device embedded. In this paper, we propose a model to select the right subset of buses that maximizes the coverage of the city. We evaluate the model in a real scenario based on a large-scale dataset of more than 5700 buses in the city of Rio de Janeiro, Brazil. Among other findings, we observe that the fleet of buses covers approximately 5655 km of streets (approximately 47% of the streets) and show that it is possible to cover 94% of the same streets if only 18% of buses have sensing capabilities embedded.
Traffic accidents account for between 20% and 40% of work-related accidents in industrial countries, and research indicates that road transport companies often have little focus on organisational safety management (OSM). There is thus a huge and largely untapped road safety potential in improving the safety of people who drive in their work, by focusing on OSM. Road transport companies in European countries are often small, however, with limited resources in terms of time, financial resources and competence on road safety. The main aim of the present article is therefore to develop an OSM strategy for small road transport companies. Based on a systematic literature review, taking Norwegian research as its point of departure, the article concludes that four measures seem to be most realistic for small goods-transport businesses, and that these measures seem to have the greatest safety potential. These four measures can be arranged on a ladder, where businesses start at the lowest and most basic level, before proceeding to the next step. While our stepwise safety-ladder approach has not been validated, it is expected that further research would confirm the value of the strategy proposed.
Since pervasive smartphones own advanced computing capability and are equipped with various sensors, they have been used for dangerous driving behaviors detection, such as drunk driving. However, sensory data gathered by smartphones are noisy, which results in inaccurate driving behaviors estimations. Some existing works try to filter noise from sensor readings, but usually only the outlier data are filtered. The noises caused by hardware of the smartphone cannot be removed from the sensor reading. In this paper, we propose DrivingSense, a reliable dangerous driving behavior identification scheme based on smartphone autocalibration. We first theoretically analyze the impact of the sensor error on the vehicle driving behavior estimation. Then, we propose a smartphone autocalibration algorithm based on sensor noise distribution determination when a vehicle is being driven. DrivingSense leverages the corrected sensor parameters to identify three kinds of dangerous behaviors: speeding, irregular driving direction change, and abnormal speed control. We evaluate the effectiveness of our scheme under realistic environments. The results show that DrivingSense, on average, is able to detect the driving direction change event and abnormal speed control event with 93.95% precision and 90.54% recall, respectively. In addition, the speed estimation error is less than 2.1 m/s, which is an acceptable range.
The rise of e-commerce and globalization has changed consumption patterns. Different industries have different logistical needs. In meeting needs with different schedules logistics play a key role. Delivering a seamless service becomes a source of competitive advantage for the logistics industry. Global positioning system-based fleet management system technology provides synergy to transport companies and achieves many management goals such as monitoring and tracking commodity distribution, energy saving, safety, and quality. A case company, which is a subsidiary of a very famous food and retail conglomerate and operates the largest shipping line in Taiwan, has suffered from the nonsmooth introduction of GPS-based fleet management systems in recent years. Therefore, this study aims to identify key factors for introducing related systems to the case company. By using DEMATEL and ANP, we can find not only key factors but also causes and effects among key factors. The results showed that support from executives was the most important criterion but it has the worst performance among key factors. It is found that adequate annual budget planning, enhancement of user intention, and collaboration with consultants with high specialty could be helpful to enhance the faith of top executives for successfully introducing the systems to the case company.
In a fast-paced environment of today society, safety issue related to driving is considered a second priority in contrast to travelling from one place to another in the shortest possible time. This often leads to possible accidents. In order to reduce road traffic accidents, one domain which requires to be focused on is driving behaviour. This paper proposes three algorithms which detect driving events using motion sensors embedded on a smartphone since it is easily accessible and widely available in the market. More importantly, the proposed algorithms classify whether or not these events are aggressive based on raw data from various on board sensors on a smartphone. In addition, one of the outstanding features of the proposed algorithm is the ability to fine tune and adjust its sensitivity level to suit any given application domain appropriately. Initial experimental results reveal that the pattern matching algorithm outperforms the rule-based algorithm for driving events in both lateral and longitudinal movements where a high percentage of detection rate has been obtained for 11 out of 12 types of driving events. In addition, a trade-off between the detection rate and false alarm rate has been demonstrated under a range of algorithm settings in order to illustrate the proposed algorithm’s flexibility.
In 1900, less than 20 percent of the world population lived in cities, in 2007, just more than 50 percent of the world population lived in cities. In 2050, it has been predicted that more than 70 percent of the global population (about 6.4 billion people) will be city inhabitants. There is more pressure being placed on cities through this increase in population [1]. With advent of smart cities, information and communication technology is increasingly transforming the way city municipalities and city residents organize and operate in response to urban growth. In this paper, we create a generic scheme for navigating a route through out city. A requested route is provided by using combination of A* Algorithm and Haversine formula. Haversine Formula gives minimum distance between any two points on spherical body by using latitude and longitude. This minimum distance is then provided to A* algorithm to calculate minimum distance. The process for detecting the shortest path is mention in this paper.
Commonly used evaluation measures including Recall, Precision, F-Measure and Rand Accuracy are biased and should not be used without clear understanding of the biases, and corresponding identification of chance or base case levels of the statistic. Using these measures a system that performs worse in the objective sense of Informedness, can appear to perform better under any of these commonly used measures. We discuss several concepts and measures that reflect the probability that prediction is informed versus chance. Informedness and introduce Markedness as a dual measure for the probability that prediction is marked versus chance. Finally we demonstrate elegant connections between the concepts of Informedness, Markedness, Correlation and Significance as well as their intuitive relationships with Recall and Precision, and outline the extension from the dichotomous case to the general multi-class case.
Today's smartphones and mobile devices typically embed advanced motion sensors. Due to their increasing market penetration, there is a potential for the development of distributed sensing platforms. In particular, over the last few years there has been an increasing interest in monitoring vehicles and driving data, aiming to identify risky driving maneuvers and to improve driver efficiency. Such a driver profiling system can be useful in fleet management, insurance premium adjustment, fuel consumption optimization or CO2 emission reduction. In this paper, we analyze how smartphone sensors can be used to identify driving maneuvers and propose SenseFleet, a driver profile platform that is able to detect risky driving events independently from the mobile device and vehicle. A fuzzy system is used to compute a score for the different drivers using real-time context information like route topology or weather conditions. To validate our platform, we present an evaluation study considering multiple drivers along a predefined path. The results show that our platform is able to accurately detect risky driving events and provide a representative score for each individual driver.
In this paper, we propose an approach to understand the driver behavior using smartphone sensors. The aim for analyzing the sensory data acquired using a smartphone is to design a car-independent system which does not need vehicle mounted sensors measuring turn rates, gas consumption or tire pressure. The sensory data utilized in this paper includes the accelerometer, gyroscope and the magnetometer. Using these sensors we obtain position, speed, acceleration, deceleration and deflection angle sensory information and estimate commuting safety by statistically analyzing driver behavior. In contrast to state of the art, this work uses no external sensors, resulting in a cost efficient, simplistic and user-friendly system.
As vehicle manufacturers continue to increase their emphasis on safety with advanced driver-assistance systems (ADASs), we propose a device that is not only already in abundance but portable enough as well to be one of the most effective multipurpose devices that are able to analyze and advise on safety conditions. Mobile smartphones today are equipped with numerous sensors that can help to aid in safety enhancements for drivers on the road. In this paper, we use the three-axis accelerometer of an Android-based smartphone to record and analyze various driver behaviors and external road conditions that could potentially be hazardous to the health of the driver, the neighboring public, and the automobile. Effective use of these data can educate a potentially dangerous driver on how to safely and efficiently operate a vehicle. With real-time analysis and auditory alerts of these factors, we can increase a driver's overall awareness to maximize safety.
Driving behavior rating is an interesting subject relating to improving road safety. The rating may be used to monitor driver behavior or giving feedback to the driver. To give ratings, it is believed that driving maneuvers must first be identified then the rating is given for each maneuver. In this work, a method for classify the driving maneuvers automatically using GPS data are presented along with an experimental data. The driving maneuver considered in this work are acceleration, deceleration, turning, and lane changing. Each driving behavior is defined by the characteristic data such as velocity and heading angle. The algorithm for classification was created and was checked for reliability
We evaluate a mobile application that assesses driving behavior based on in-vehicle acceleration measurements and gives corresponding feedback to drivers. In the insurance business, such applications have recently gained traction as a viable alternative to the monitoring of drivers via "black boxes" installed in vehicles, which lacks interaction opportunities and is perceived as privacy intrusive by policyholders. However, pose uncertainty and other noise-inducing factors make smartphones potentially less reliable as sensor platforms. We therefore compare critical driving events generated by a smartphone with reference measurements from a vehicle-fixed IMU in a controlled field study. The study was designed to capture driver variability under real-world conditions, while minimizing the influence of external factors. We find that the mobile measurements tend to overestimate critical driving events, possibly due to deviation from the calibrated initial device pose. While weather and daytime do not appear to influence event counts, road type is a significant factor that is not considered in most current state-of-the-art implementations.
Recently the analysis on road traffic conditions with the use of probe-cars has been paid increasing attention. Probe-cars enable us to obtain the spacious data. However, since probe-cars generally provide the coordinates as vehicle’s location point, analysts have to identify the vehicle’s cruising route. It is difficult to identify the route at the section where elevated urban expressways are allocated parallel with the other roads. This study develops the method of identifying the vehicle’s cruising route even where there are elevated urban expressways. It also develops the travel time prediction method using accumulated probe-car data. International Journal of ITS Research, Vol.2, No.1, 2008, p.21-28
Pedestrian and bicycle safety is a key component in traffic safety studies. Various studies were conducted to address pedestrian and bicycle safety issues for intersections, road segments, etc. However, only a few studies investigated pedestrian and bicycle safety for bus stops, which usually have a relatively larger volume of pedestrians and bicyclists. Moreover, traditional reactive safety approaches require a significant number of historical crashes, while pedestrian and bicycle crashes are usually rare events. Alternatively, surrogate safety measures could proactively evaluate traffic safety status when crash data are rare or unavailable. This paper utilized critical bus driving events extracted from GPS trajectory data as pedestrian and bicycle surrogate safety measures for bus stops. A city-wide trajectory data from Orlando, Florida was used, which contains around 300 buses, 6,700,000 GPS records, and 1300 bus stops. Three critical driving events were identified based on the buses' acceleration rates and stop time; hard acceleration, hard deceleration, and long stop. The relationships between critical driving events and crashes were examined using Spearman's rank correlation coefficient. All three events were positively correlated with pedestrian and bicycle crashes. Long stop event has the highest correlation coefficient, followed by hard acceleration and hard deceleration. A Bayesian negative binomial model incorporating spatial correlation (Bayesian NB-CAR) was built to estimate the pedestrian and bicycle crash frequency using the generated events. The results were consistent with the correlation estimation. For example, hard acceleration and long stop events were both positively related to pedestrian and bicycle crashes. Moreover, model evaluation results indicated that the proposed Bayesian NB-CAR outperformed the standard Bayesian negative binomial model with lower Watanabe-Akaike Information Criterion (WAIC) and Deviance Information Criteria (DIC) values. In conclusion, this paper suggests the use of critical bus driving events as surrogate safety measures for pedestrian and bicycle crashes, which could be implemented in a proactive traffic safety management system.
There are many systems to evaluate driving style based on smartphone sensors without enough awareness from the context. To cover this gap, we propose a new system namely CADSE system to consider the effects of traffic levels and car types on driving evaluation. CADSE system includes three subsystems to calibrate smartphone, to classify the maneuvers, and to evaluate driving styles. For each maneuver, the smartphone sensors data are gathered in three successive time intervals referred as pre-maneuver, in-maneuver, and post-maneuver times. Then, we extract some important mathematical and experimental features from these data. Afterwards, we propose an ensemble learning method on these features to classify the maneuvers. This ensemble method includes decision tree, support vector machine, multi-layer perceptron, and k-nearest neighbors. Finally, we develop a rule-based fuzzy inference system to integrate the outputs of these algorithms and to recognize dangerous and safe maneuvers. CADSE saves this result in driver’s profile to consider more for dangerous driving recognition. The experimental results show that accuracy, precision, recall, and F-measure of CADSE system are greater than 94%, 92%, 92%, and 93%, respectively that prove the system efficiency.
Human factors are the primary catalyst for traffic accidents. Among different factors, fatigue, distraction, drunkenness, and/or recklessness are the most common types of abnormal driving behavior that leads to an accident. With technological advances, modern smartphones have the capabilities for driving behavior analysis. There has not yet been a comprehensive review on methodologies utilizing only a smartphone for drowsiness detection and abnormal driver behavior detection. In this paper, different methodologies proposed by different authors are discussed. It includes the sensing schemes, detection algorithms, and their corresponding accuracy and limitations. Challenges and possible solutions such as integration of the smartphone behavior classification system with the concept of context-aware, mobile crowdsensing, and active steering control are analyzed. The issue of model training and updating on the smartphone and cloud environment is also included.
Driving style evaluation by smartphones depends on the quality of the features extracted from sensors data. Typically, these features are extracted based on experiments, expertness, or heuristics. In more modern approaches, some automatic methods such as convolutional neural network (CNN) are used to extract features including obvious and hidden ones. We also used the CNN on acceleration data collected by smartphones to extract the knowledge regarding driving style, vehicle, environment, and human characteristics. We found that this novel idea was more successful for evaluating the driving style compared with the previous machine learning algorithms. However, we faced over-fitting in the training process of the CNN and to avoid this, we proposed the state-of-the-art learning method applying two adaptive regularization schemes called adaptive dropout and adaptive weight decay. To evaluate these techniques, first, we checked the results on three popular large-scale datasets. When we proved the efficiency, we utilized them on two transportation data sets. In transportation-modes dataset, the accuracy was at least 95.8%'; and regarding the driving-style dataset, the classification accuracy was 95%. Thus, the adaptive regularized CNN is an amazing option for driving style evaluation on smartphones.
Drivers' behavior evaluation is one of the most important problems in intelligent transportation systems and driver assistant systems. It has a great influence on driving safety and fuel consumption. One of the challenges in this regard is the modeling perspective to treat with uncertainty in judgments about driving behaviors. Really, assessing a single maneuver with a rigid threshold leads to a weak judgment for driving evaluation. To fill this gap, a novel neuro-fuzzy system is proposed to classify the driving behaviors based on their similarities to fuzzy patterns when all of the various maneuvers are stated with some fuzzy numbers. These patterns are also fuzzy numbers and they are extracted from statistical analysis on the smartphone sensors data. Our driving evaluation system consists of three processes. Firstly, it detects the type of all of the maneuvers through the driving period, by using a multi-layer perceptron neural network. Secondly, it extracts a new feature based on the acceleration and assigns three fuzzy numbers to driver's lane change, turn and U-turn maneuvers. Thirdly, it determines the similarity between these three fuzzy numbers and the fuzzy patterns to evaluate the safe and the aggressive driving scores. To validate this model, Driver's Angry Score (DAS) questionnaires are used. Results show that the fusion of Inertial Measurement Unit (IMU) sensors of smartphones is enough for the proposed driving evaluation system. Accuracy of this system is 87% without using GPS and GIS data and this system is independent of smartphones and vehicles types.
In this paper, we explore the feasibility of leveraging the accelerometer and gyroscope sensors in modern smartphones to detect instances of distracted driving activities (e.g., calling, texting, and reading while driving). To do so, we conducted an experiment with 16 subjects on a realistic driving simulator. As discussed later, the simulator is equipped with a realistic steering wheel, acceleration/braking pedals, and a wide screen to visualize background vehicular traffic. It is also programmed to simulate multiple environmental conditions like daytime, nighttime, fog, and rain/snow. The subjects were instructed to drive the simulator while performing a randomized sequence of activities that included texting, calling, and reading from a phone while they were driving, during which the accelerometer and gyroscope in the phone were logging sensory data. By extracting features from this sensory data, we then implemented a machine learning technique based on random forests to detect distracted driving. Our technique achieves very good precision, recall, and F-measure across all environmental conditions we tested. We believe that our contributions in this paper can have a significant impact on enhancing road safety.
Monitoring and evaluating of driving behavior is the main goal of this paper that encourage us to develop a new system based on Inertial Measurement Unit (IMU) sensors of smartphones. In this system, a hybrid of Discrete Wavelet Transformation (DWT) and Adaptive Neuro Fuzzy Inference System (ANFIS) is used to recognize overall driving behaviors. The behaviors are classified into the safe, the semi-aggressive, and the aggressive classes that are adopted with Driver Anger Scale (DAS) self-reported questionnaire results. The proposed system extracts four features from IMU sensors in the forms of time series. They are decomposed by DWT in two levels and their energies are sent to six ANFISs. Each ANFIS models the different perception about driving behavior under uncertain knowledge and returns the similarity or dissimilarity between driving behaviors. The results of these six ANFISs are combined by three different decision fusion approaches. Results show that Coiflet-2 is the most suitable mother wavelet for driving behavior analysis. In addition, the proposed system recognizes the overall driving behavior patterns with 92% accuracy without necessity to evaluate the maneuvers one by one. We show that without longitude acceleration data, the driver behavior cannot be recognized successfully while the results do not disturb substantially when the gyroscope is not available.
Network screening is a key element in identifying and prioritizing hazardous sites for engineering treatment. Traditional screening methods have used observed crash frequency or severity ranking criteria and statistical modelling approaches, despite the fact that crash-based methods are reactive. Alternatively, surrogate safety measures (SSMs) have become popular, making use of new data sources including video and, more rarely, GPS data. The purpose of this study is to examine vehicle manoeuvres of braking and accelerating extracted from a large quantity of GPS data collected using the smartphones of regular drivers, and to explore their potential as SSMs through correlation with historical collision frequency and severity across different facility types. GPS travel data was collected in Quebec City, Canada in 2014. The sample for this study contained over 4000 drivers and 21,000 trips. Hard braking (HBEs) and accelerating events (HAEs) were extracted and compared to historical crash data using Spearman's correlation coefficient and pairwise Kolmogorov-Smirnov tests. Both manoeuvres were shown to be positively correlated with crash frequency at the link and intersection levels, though correlations were much stronger when considering intersections. Locations with more braking and accelerating also tend to have more collisions. Concerning severity, higher numbers of vehicle manoeuvres were also related to increased collision severity, though this relationship was not always statistically significant. The inclusion of severity testing, which is an independent dimension of safety, represents a substantial contribution to the existing literature. Future work will focus on developing a network screening model that incorporates these SSMs.
Safety issues while driving in smart cities are considered to be top-notch priority in contrast to traveling. Today’s fast paced society, often leads to accidents. In order to reduce the road accidents, one key area of research is monitoring the driving behavior of drivers. Understanding the driver behavior is an essential component in Intelligent Driver Assistance Systems. One of potential cause of traffic fatalities is aggressive driving behavior. However, drivers are not fully aware of their aggressive actions. So, in order to increase awareness and to promote driver safety, a novel system has been proposed. In this work, we focus on DTW based event detection technique, which have not been researched in motion sensors based time series data to a great extent. Our motivation is to improve the classification accuracy to detect sudden braking and aggressive driving behaviors using sensory data collected from smartphone. A very significant feature of DTW is to be able to automatically cope with time deformations and different speeds associated with time-dependent data which makes it suitable for our chosen application where data might get affected due to factors such as: high variability in road and vehicle conditions, heterogeneous smartphone sensors, etc. Our technique is novel as it uses fusion of sensors to enhance detection accuracy. The experimental results show that proposed algorithm outperforms the existing machine learning and threshold-based techniques with 100% detection rate of braking events and 97% & 86.67% detection rate of normal left & right turns and aggressive left & right turns respectively.
The proliferation of connected mobile devices together with advances in their sensing capacity has enabled a new distributed telematics platform. In particular, smartphones can be used as driving sensors to identify individual driver behavior and risky maneuvers. However, in order to estimate driver behavior with smartphones, the system must deal with different vehicle characteristics. This is the main limitation of existing sensing platforms, which are principally based on fixed thresholds for different sensing parameters. In this paper, we propose an adaptive driving maneuver detection mechanism that iteratively builds a statistical model of the driver, vehicle, and smartphone combination using a multivariate normal model. By means of experimentation over a test track and public roads, we first explore the capacity of different sensor input combinations to detect risky driving maneuvers, and we propose a training mechanism that adapts the profiling model to the vehicle, driver, and road topology. A large-scale evaluation study is conducted, showing that the model for maneuver detection and scoring is able to adapt to different drivers, vehicles, and road conditions.
Rapid deceleration occurs when substantial force slows the speed of a vehicle. Rapid deceleration events (RDEs) have been proposed as a surrogate safety measure. As there is concern about crash involvement of older drivers and the effect of age-related declining visual and cognitive function on driving performance, we examined the relationship between RDEs and older driver’s vision, cognitive function and driving confidence, using naturalistic driving measures. Participants aged 75 to 94 years had their vehicle instrumented for 12 months. To minimise the chance of identifying false positives, accelerometer data was processed to identify RDEs with a substantial deceleration of >750 milli-g (7.35 m/s²). We examined the incidence of RDEs amongst older drivers, and how this behaviour is affected by differences in age; sex; visual function, cognitive function; driving confidence; and declines over the 12 months. Almost two-thirds (64%) of participants were involved in at least one RDE, and 22% of these participants experienced a meaningful decline in contrast sensitivity during the 12 months. We conducted regression modelling to examine associations between RDEs and predictive measures adjusted for (i) duration of monitoring and (ii) distance driven. We found the rate of RDEs per distance increased with age; although, this did not remain in the multivariate model. In the multivariate model, we found older drivers who experienced a decline in contrast sensitivity over the 12 months and those with lower baseline driving confidence were at increased risk of involvement in RDEs adjusted for distance driven. In other studies, contrast sensitivity has been associated with increased crash involvement for older drivers. These findings lend support for the use of RDEs as a surrogate safety measure, and demonstrate an association between a surrogate safety measure and a decline in contrast sensitivity of older drivers.
Traffic accidents resulting from driving behavior and road conditions are crucial problems for drivers. The causes and responses to traffic accidents have been widely studied by researchers. Whereas several approaches have been proposed to ease these problems, most works entail high computational costs or rigid hardware conditions. To address these challenges, we propose Health Driving, a smartphone-based system for detecting driving events and road conditions solely with a built-in smartphone acceleration sensor. More specifically, we first collect acceleration data from the acceleration sensor of a smartphone on a vehicle, and then utilize an acceleration reorientation calibration algorithm to convert the obtained acceleration data from the smartphone to acceleration data of the vehicle. Finally, we exploit Health Driving to detect driving events and road conditions, and evaluate the seriousness of the road conditions and driving events by using an efficient scoring mechanism based on the ISO 2631 standard. An extensive evaluation demonstrates that Health Driving operates successfully with an ordinary smartphone, and operates with a low computational cost compared with other methods. Copyright
In this paper, we develop a smart phone-based driving behavior evaluation system, named Join Driving, which helps drivers notice how aggressive their driving behaviors are and be aware of the riding comfort level of passengers. The proposed evaluation system is made of two parts: driving events detection and evaluation part and riding comfort level evaluation part. In driving events detection and evaluation part, the proposed system, Join Driving, first presents a model to detect drivers' driving events, based on the data collected from the acceleration, orientation and GPS sensors in smart phones. Then, based on the detected drivers' driving events, Join Driving implements a novel scoring mechanism to quantitatively evaluate how aggressive these driving events are. In riding comfort level evaluation part, the proposed system gives the specific scores to rate passengers' riding comfort level based on ISO 2631. Finally, several practical experiments are conducted to evaluate the effectiveness of the proposed scoring system.
Driving speed and vehicle movement are important factors in road safety. Abnormal driver generally drives above speed limit, changes speed suddenly or changes vehicle lateral position incessantly. One approach to tackling this problem is to develop automated instrument for detecting abnormal drivers. Global Position System (GPS) provides special features which can be utilized for this application as it could supplies location, time, and speed simultaneously. In-vehicle transponders send real time GPS data via radio frequency to a Monitoring Station for processing, and from detection algorithm, abnormal situation will be detected automatically. The normal speed parameter and lateral position changing obtained from our previous study can be used as threshold value in detection algorithm. If the driver behavior is evidently different from normal driving, then abnormal driving is recognized. Experiments and simulated data were used to verify the effectiveness of the detection algorithm. Keywords-component; Abnormal driving; Real time GPS; Speed assessment; Detection algorithm.
Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentially-aggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warp-ing (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone.
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