Analysis of Stopping Behavior at Rural T-Intersections Using Naturalistic Driving Study Data

Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic since high speeds on intersection approaches are present which can exacerbate the impact of a crash. Additionally, rural areas are often underserved with EMS services which can further contribute to negative crash outcomes. This paper describes an analysis of driver stopping behavior at rural T-intersections using the SHRP 2 Naturalistic Driving Study data. Type of stop was used as a safety surrogate measure using full/rolling stops compared to non-stops. Time series traces were obtained for 157 drivers at 87 unique intersections resulting in 1277 samples at the stop controlled approach for T-intersections. Roadway (i.e. number of lanes, presence of skew, speed limit, presence of stop bar or other traffic control devices), driver (age, gender, speeding), and environmental characteristics (time of day, presence of rain) were reduced and included as independent variables. Results of a logistic regression model indicated drivers were less likely to stop during the nighttime. However presence of intersection lighting increased the likelihood of full/rolling stops. Presence of intersection skew was shown to negatively impact stopping behavior. Additionally drivers who were traveling over the posted speed limit upstream of the intersection approach were less likely to stop at the approach stop sign.

Intelligent collision risk detection in medium-sized cities of developing countries,using naturalistic driving:A review

Traffic accidents are one of the most serious problems worldwide,being one of the leading causes of death and economic loss in the world.Low-and middle-income countries,mainly their medium-sized cities,are among the most affected by this problem.93%of traffic accidents occur in low and middle-income countries,even though these countries have approximately 60%of the world’s vehicles.This occurs mainly because in these types of countries,especially in medium-sized cities(target context),there are no ideal conditions for driving,such as adequate road infrastructure,good condition of vehicles,and rigorous safety policies.Advanced data analysis techniques including machine learning(ML)have increasingly been used to solve this problem.Naturalistic driving(ND)can be applied as a data collection method that provides information on traffic accidents.ND commonly uses a vehicle’s kinematic data to detect high-risk driving behaviors that could cause an accident.The objectives of this document are to present a review of different alternatives that help in data collection and creation of intelligent solutions related to detection of possible traffic accidents,principally using ND;and to propose an intelligent collision risk detection system(ICRDS)for identification of areas with a high probability of TA in the target context.Through the review,it was possible to analyze and evaluate the devices,variables and algorithms that help characterize a risk event in driving,considering the target context.The development of a prototype of an ICRDS for a medium-sized city in a developing country is considered viable,considering the identified components,with the aim of identifying risk events in driving,and areas of high probability of accidents in the city.

Effects of feature selection on lane-change maneuver recognition:an analysis of naturalistic driving data

Purpose–Feature selection is crucial for machine learning to recognize lane-change(LC)maneuver as there exist a large number of feature candidates.Blindly using feature could take up large storage and excessive computation time,while insufficient feature selection would cause poor performance.Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition.This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data.Design/methodology/approach–In total,1,375 LC cases are analyzed.To comprehensively select features,the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid.Then the effect size(Cohen’s d)and p-value of every feature are computed to assess their contribution for each scenario.Findings–It has been found that the common lateral features,e.g.yaw rate,lateral acceleration and time-to-lane crossing,are not strong features for recognition of LC maneuver as empirical knowledge.Finally,cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic.Experimental results show that the selected features can achieve better recognition performance than using all the features without purification.Originality/value–In this paper,the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data.The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.

An investigation on the link between driver demographic characteristics and distracted driving by using the SHRP 2 naturalistic driving data

Purpose–The purpose of this paper is to investigate the influence of driver demographic characteristics on the driving safety involving cell phone usages.Design/methodology/approach–A total of 1,432 crashes and 19,714 baselines were collected for the Strategic Highway Research Program 2 naturalistic driving research.The authors used a case-control approach to estimate the prevalence and the population attributable risk percentage.The mixed logistic regression model is used to evaluate the correlation between different driver demographic characteristics(age,driving experience or their combination)and the crash risk regarding cell phone engagements,as well as the correlation among the likelihood of the cell phone engagement during the driving,multiple driver demographic characteristics(gender,age and driving experience)and environment conditions.Findings–Senior drivers face an extremely high crash risk when distracted by cell phone during driving,but they are not involved in crashes at a large scale.On the contrary,cell phone usages account for a far larger percentage of total crashes for young drivers.Similarly,experienced drivers and experienced-middle-aged drivers seem less likely to be impacted by the cell phone while driving,and cell phone engagements are attributed to a lower percentage of total crashes for them.Furthermore,experienced,senior or male drivers are less likely to engage in cell phone-related secondary tasks while driving.Originality/value–The results provide support to guide countermeasures and vehicle design.

Investigating the trip configured causal effect of distracted driving on aggressive driving behavior for e-hailing taxi drivers

Risky driving behavior of taxi drivers typically evaluated for full operation or sometimes sorted into occupied and empty running trips.In this paper,we simultaneously analyze aggressive driving and distracted driving of taxi drivers under three different trip categories.Trip origin is considered a transition from without ride-order to with ride-order travelling or from with ride-order to occupied travelling,and a destination as a transition from occupied to without ride-order travelling and vice versa.Distracted driving is characterized by driver interference,driver mobile use and some entertainment aspects,while specific harmful and risky actions are considered for aggressive driving.High-resolution and real-time kinematic parameters of taxis were recorded by the in-vehicle recorder VBOX for overall 562 trips.The distracted driving parameters and aggressive driving actions were monitored through python data collector web application that was specially programmed for this particular research.Besides dual dash cam(i.e.,front and inside car camera),drivers’ whole day driving history from their Chinese ride-hailing Di Di smart application was used to differentiate occupied trips,unoccupied trips with ride-order and unoccupied trips without ride-order.Structural equation modeling(SEM) is practiced in this paper to understand the influence of distracted driving indicators on aggressive driving behaviors.The multi-group model analysis of SEM indicated that handling distracted risky driving could control aggressive driving behavior up to 96% and 98% inunoccupied without ride-order trips and unoccupied trips with ride-order respectively.The model has also identified the sensitive risky driving indicators for each group separately.

A machine learning pipeline for fuel-economical driving model

Purpose-A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour.Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy.Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration.A single-step application of machine learning(ML)is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy.The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.Design/methodology/approach-This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars.The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data,and the second step detects abnormal fuel economy in relation to contextual information.Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model.The contextual anomaly is detected by following two approaches,kernel quantile estimator and one-class support vector machine.The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour.Any error beyond a threshold is classified as an anomaly.The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection.The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder,and the performance of both models is compared.The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.Findings-A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder.Both models achieve prediction accuracy within a range of 98%-100%for prediction as a first step.Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%-100%,whereas the one-class support vectormachine approach performs within the range of 99.3%-100%.Research limitations/implications-The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns.However,it can be extended to correlate driver’s physiological state such as fatigue,sleep and stress to correlate with driving behaviour and fuel economy.The anomaly detection approach here is limited to providing feedback to driver,it can be extended to give contextual feedback to the steering controller or throttle controller.In the future,a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.Practical implications-The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts.It can also be used as a training tool for improving driving efficiency for new drivers.It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.Originality/value-This paper contributes to the existing literature by providing anMLpipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values.The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy.The main contributions for this approach are as follows:(1)a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption.(2)Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.

Review and assessment of different perspectives of vehicle-pedestrian conflicts and crashes:Passive and active analysis approaches

The importance of investigating pedestrian safety has been evaluated repeatedly in safety studies.The present study attempts to evaluate the various methods used by previous researchers in a hierarchical process,to determine the characteristics,advantages,and limitations of each method.Two general analysis approaches(passive and active) were taken into account to categorize 169 previous types of research.In the passive approach,the studied methods were those based on crash databases,questionnaires,and post-crash field observation data;while,in the active approach,the studied methods were those based on driving simulations and videography.The result of the passive approach reveals that road users’ features and road characteristics(crash database studies),and error,lapses,intentional and unintentional violations(questionnaire studies) by them were among the most important causes of crashes and conflicts.Furthermore,road users’ distractions also reported a set of factors affecting the possibility of conflicts and crashes based on postcrash field observation studies.Also,results of the active approach showed that risky behaviors are the most important factor in threatening pedestrian safety such as unauthorized speeding,non-compliance with traffic law,unauthorized overtaking by drivers,and illegal crossing.Furthermore,risk perception and decision-making processes are the mostimportant bond between the attitude and behavior of road users in dangerous driving situations.Examining studies through passive approach would lead to identifying the causes of crashes,recognizing the attitude of road users towards safety,and determining road users’ behavioral patterns in certain situations,while the active approach has led to a more detailed understanding of behaviors and attitudes of road users.The inference of the findings obtained in this study will lead to a better understanding of the behavior of road users for studies on advanced driving assistance systems(ADAS).

Development of test scenarios and bicyclist surrogate for the evaluation of bicyclist automatic emergency braking systems

Purpose–To support the standardized evaluation of bicyclist automatic emergency braking(AEB)systems,test scenarios,test procedures and test system hardware and software tools have been investigated and developed by the Transportation Active Safety Institute(TASI)at Indiana University-Purdue University Indianapolis.This paper aims to focus on the development of test scenarios and bicyclist surrogate for evaluating vehicle–bicyclist AEB systems.Design/methodology/approach–The harmonized general estimates system(GES)/FARS 2010-2011 crash data and TASI 110-car naturalistic driving data(NDD)are used to determine the crash geometries and environmental factors of crash scenarios including lighting conditions,vehicle speeds,bicyclist speeds,etc.A surrogate bicyclist including a bicycle rider and a bicycle surrogate is designed to match the visual and radar characteristics of bicyclists in the USA.A bicycle target is designed with both leg pedaling and wheel rotation to produce proper micro-Doppler features and generate realistic motion for camera-based AEB systems.Findings–Based on the analysis of the harmonized GES/FARS crash data,five crash scenarios are recommended for performance testing of bicyclist AEB systems.Combined with TASI 110-car naturalistic driving data,the crash environmental factors including lighting conditions,obscuring objects,vehicle speed and bicyclist speed are determined.The surrogate bicyclist was designed to represent the visual and radar characteristics of the real bicyclists in the USA.The height of the bicycle rider mannequin is 173 cm,representing the weighted height of 50th percentile US male and female adults.The size and shape of the surrogate bicycle were determined as 26-inch wheel and mountain/road bicycle frame,respectively.Both leg pedaling motion and wheel rotation are suggested to produce proper micro-Doppler features and support the camera-based AEB systems.Originality/value–The results have demonstrated that the developed scenarios,test procedures and bicyclist surrogate will provide effective objective methods and necessary hardware and software tools for the evaluation and validation of bicyclist AEB systems.This is crucial for the development of advanced driver assistance systems.

Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles

Automated driving systems are often used for lane keeping tasks.By these systems,a local path is planned ahead of the vehicle.However,these paths are often found unnatural by human drivers.In response to this,this paper proposes a linear driver model,which can calculate node points reflective of human driver preferences and based on these node points a human driver preferred motion path can be designed for autonomous driving.The model input is the road curvature,effectively harnessed through a self-developed Euler-curve-based curve fitting algorithm.A comprehensive case study is undertaken to empirically validate the efficacy of the proposed model,demonstrating its capacity to emulate the average behavioral pat-terns observed in human curve path selection.Statistical analyses further underscore the model's robustness,affirming the authenticity of the established relationships.This paradigm shift in trajectory planning holds promising implications for the seamless integration of autonomous driving systems with human driving preferences.