Integrated Vehicular System with Black Box Capability and Intelligent Driving Diagnosis

Hoy en día, una de las causas de las altas tasas de mortalidad en el mundo son los accidentes de tránsito. Según la Organización Mundial de la Salud (OMS), los accidentes de tránsito alcanzan más de 1.3 millones de víctimas anuales en el mundo; y sólo en Colombia más de 5000 víctimas al año. Por esta razón, esta investigación presenta el desarrollo de un “Agente para el Diagnóstico Inteligente de Conducción”, implementado mediante un algoritmo de Lógica Difusa. Con la aproximación computacional del conocimiento experto en conducción vehicular, este trabajo permite realizar el diagnóstico de las maniobras del conductor de manera que se pueda determinar si son riesgosas o si no lo son. Los experimentos fueron realizados bajo condiciones reales de “conducción segura” en la ciudad de Barranquilla. Los resultados muestran que se puede lograr un diagnóstico inteligente de conducción gracias al “Agente para el Diagnóstico Inteligente de Conducción” propuesto

Identification and safety effects of road user related measures. Deliverable 4.2 of the H2020 project SafetyCube

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the second deliverable (4.2) of work package 4, which is dedicated to identifying and assessing road safety measures related to road users in terms of their effectiveness. The focus of deliverable 4.2 is on the identification and assessment of countermeasures and describes the corresponding operational procedure and outcomes. Measures which intend to increase road safety of all kind of road user groups have been considered [...continues]

AI-based framework for automatically extracting high-low features from NDS data to understand driver behavior

Our ability to detect and characterize unsafe driving behaviors in naturalistic driving environments and associate them with road crashes will be a significant step toward developing effective crash countermeasures. Due to some limitations, researchers have not yet fully achieved the stated goal of characterizing unsafe driving behaviors. These limitations include, but are not limited to, the high cost of data collection and the manual processes required to extract information from NDS data. In light of this limitations, the primary objective of this study is to develop an artificial intelligence (AI) framework for automatically extracting high-low features from the NDS dataset to explain driver behavior using a low-cost data collection method. The author proposed three novel objectives for achieving the study's objective in light of the identified research gaps. Initially, the study develops a low-cost data acquisition system for gathering data on naturalistic driving. Second, the study develops a framework that automatically extracts high- to low-level features, such as vehicle density, turning movements, and lane changes, from the data collected by the developed data acquisition system. Thirdly, the study extracted information from the NDS data to gain a better understanding of people's car-following behavior and other driving behaviors in order to develop countermeasures for traffic safety through data collection and analysis. The first objective of this study is to develop a multifunctional smartphone application for collecting NDS data. Three major modules comprised the designed app: a front-end user interface module, a sensor module, and a backend module. The front-end, which is also the application's user interface, was created to provide a streamlined view that exposed the application's key features via a tab bar controller. This allows us to compartmentalize the application's critical components into separate views. The backend module provides computational resources that can be used to accelerate front-end query responses. Google Firebase powered the backend of the developed application. The sensor modules included CoreMotion, CoreLocation, and AVKit. CoreMotion collects motion and environmental data from the onboard hardware of iOS devices, including accelerometers, gyroscopes, pedometers, magnetometers, and barometers. In contrast, CoreLocation determines the altitude, orientation, and geographical location of a device, as well as its position relative to an adjacent iBeacon device. The AVKit finally provides a high-level interface for video content playback. To achieve objective two, we formulated the problem as both a classification and time-series segmentation problem. This is due to the fact that the majority of existing driver maneuver detection methods formulate the problem as a pure classification problem, assuming a discretized input signal with known start and end locations for each event or segment. In practice, however, vehicle telemetry data used for detecting driver maneuvers are continuous; thus, a fully automated driver maneuver detection system should incorporate solutions for both time series segmentation and classification. The five stages of our proposed methodology are as follows: 1) data preprocessing, 2) segmentation of events, 3) machine learning classification, 4) heuristics classification, and 5) frame-by-frame video annotation. The result of the study indicates that the gyroscope reading is an exceptional parameter for extracting driving events, as its accuracy was consistent across all four models developed. The study reveals that the Energy Maximization Algorithm's accuracy ranges from 56.80 percent (left lane change) to 85.20 percent (right lane change) (lane-keeping) All four models developed had comparable accuracies to studies that used similar models. The 1D-CNN model had the highest accuracy (98.99 percent), followed by the LSTM model (97.75 percent), the RF model (97.71 percent), and the SVM model (97.65 percent). To serve as a ground truth, continuous signal data was annotated. In addition, the proposed method outperformed the fixed time window approach. The study analyzed the overall pipeline's accuracy by penalizing the F1 scores of the ML models with the EMA's duration score. The pipeline's accuracy ranged between 56.8 percent and 85.0 percent overall. The ultimate goal of this study was to extract variables from naturalistic driving videos that would facilitate an understanding of driver behavior in a naturalistic driving environment. To achieve this objective, three sub-goals were established. First, we developed a framework for extracting features pertinent to comprehending the behavior of natural-environment drivers. Using the extracted features, we then analyzed the car-following behaviors of various demographic groups. Thirdly, using a machine learning algorithm, we modeled the acceleration of both the ego-vehicle and the leading vehicle. Younger drivers are more likely to be aggressive, according to the findings of this study. In addition, the study revealed that drivers tend to accelerate when the distance between them and the vehicle in front of them is substantial. Lastly, compared to younger drivers, elderly motorists maintain a significantly larger following distance. This study's results have numerous safety implications. First, the analysis of the driving behavior of different demographic groups will enable safety engineers to develop the most effective crash countermeasures by enhancing their understanding of the driving styles of different demographic groups and the causes of collisions. Second, the models developed to predict the acceleration of both the ego-vehicle and the leading vehicle will provide enough information to explain the behavior of the ego-driver.Includes bibliographical references

Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior

Connected and automated vehicles (CAVs) are supposed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic environment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to understand HDV behaviors to make safe actions. In this study, we develop a Driver Digital Twin (DDT) for the online prediction of personalized lane change behavior, allowing CAVs to predict surrounding vehicles' behaviors with the help of the digital twin technology. DDT is deployed on a vehicle-edge-cloud architecture, where the cloud server models the driver behavior for each HDV based on the historical naturalistic driving data, while the edge server processes the real-time data from each driver with his/her digital twin on the cloud to predict the lane change maneuver. The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles connected through the 4G/LTE cellular network. The lane change intention can be recognized in 6 seconds on average before the vehicle crosses the lane separation line, and the Mean Euclidean Distance between the predicted trajectory and GPS ground truth is 1.03 meters within a 4-second prediction window. Compared to the general model, using a personalized model can improve prediction accuracy by 27.8%. The demonstration video of the proposed system can be watched at https://youtu.be/5cbsabgIOdM

The human factors associated with responding to emergency vehicles

Emergency vehicles undertake emergency driving, using lights and sirens, to move rapidly through traffic in response to situations where life and property are at risk. For the emergency driving to be effective, other motorists need to drive in a manner that facilitates their passage. Despite laws to support this, problematic encounters can result in emergency vehicles being unable to get through. The current research expanded on earlier exploratory research into motorists’ encounters with emergency vehicles (Grant, 2010) to examine psychological factors involved with motorists’ responses to emergency vehicles. A construct validity approach was used to develop a scale through which a larger representative sample could be assessed. A qualitative study with emergency service drivers and motorists combined with existing literature to provide the basis for the scale development, and the subsequent testing and refinement resulted in the Responding to Emergency Vehicles Scale (REVS). The data obtained throughout development of the scale, from 1089 participants, were used to investigate psychological factors associated with responding to emergency vehicles and have identified the following overarching factors: Reasons for responding to emergency vehicles; attitudes and beliefs about emergency vehicles/services; appraisal of the encounter and their ability to respond; prior associations with emergency services personnel, or vehicles; and beliefs around punishment. The study also explored participants’ demographic factors relative to their reported driving behaviours during emergency vehicle encounters. Lastly, it identified the needs of the emergency service drivers during encounters, suggesting that existing road safety messages were inconsistent with actual needs of emergency service drivers, and suggested an alternative model of response. Overall, the psychological factors provided an understanding of the participants’ aptitude to be trained to respond more effectively. Their strong pro-social intentions indicated an intention to respond appropriately to emergency vehicles and they were cognisant of the potential consequences of not doing so. Their generally positive views about emergency vehicles as well as associated services, and beliefs in the appropriateness of punishment further supported their willingness to respond appropriately. Finally, participants reported that they were aroused by emergency vehicles encounters, but not stressed to the extent they were incapable of responding. Whilst the research was undertaken from a predominately theoretical lens, the applied nature of the phenomenon under scrutiny yielded findings that can inform policy around responding to emergency vehicles. Specifically, the findings suggest the need to embed explicit training on emergency vehicles within the existing driver training framework. They also recommend amendment to the road safety message used to guide motorists’ actions during encounters with emergency vehicles. Future studies could confirm the appropriateness of the recommended response model with a larger sample of emergency service drivers, and use the REVS to assess larger samples and different driving populations

A Context Aware Classification System for Monitoring Driver’s Distraction Levels

Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner

A systematic review of proactive driver support systems and underlying technologies

Recently, there has been an incredible growth of recommender systems as well as proactive, context-oriented technologies, based on cloud services, ubiquitous computing and service-oriented architecture. This composition of techniques and technologies has made it possible to create intelligent support systems in areas with rapidly changing environment, like car driving. However, such systems are not yet widespread, and available prototypes, in most cases, are only useful for research trials, so their development remains an important issue. Thereby, this paper reviews the existing body of literature on recommender systems and related technologies in order to carry out their systematic analysis and draw the appropriate conclusions on the prospects for their development

Autonomous Vehicle Regulation & Trust: The Impact of Failures to Comply with Standards

The autonomous vehicle (AV) industry works very hard to create public trust in both AV technology and its developers. Building trust is part of a strategy to permit the industry itself to manage the testing and deployment of AV technology without regulatory interference. This article explains how industry actions to promote trust (both individually and collectively) have created concerns rather than comfort with this emerging technology. The article suggests how the industry might change its current approach to law and regulation from an adversarial posture to a more cooperative one in which a space is created for government regulation consistent with technology development. This article proposes a way forward that involves re-thinking the use of SAE J3016 as part of AV law and regulation, instead taking a new direction based on distinguishing test platforms from production vehicles

Driver lane change intention inference using machine learning methods.

Lane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways. This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ. Finally, discussions and conclusions are made in Part Ⅵ. A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour.PhD in Transpor

Exploration of Older Adults’ Travel Behavior and Their Transportation Barriers

Both the number of older adults and their proportion of the population are increasing rapidly in the United States. By 2040, about 20.7% of the U.S. population will be 65 and older (Harrison & Ragland, 2003a). These dramatic changes in the composition of the population will bring new challenges to the provision of transportation services. This is because the travel patterns and needs of older adults are likely to become more complicated. A growing number of people will find it increasingly difficult to meet their transportation needs. As the life expectancy of older adults is likely to continue to increase, a greater number of older people will face mobility issues alone (Alsnih & Hensher, 2003). Researchers widely agree that the aging population in the U.S. relies heavily on cars (as drivers or passengers) because they are convenient, flexible, and allow them to live independently and participate in normal daily activities (Haustein, 2012; Rosenbloom, 2005). However, dispersed land use patterns in the United States, the growing number of older adults living in suburban areas, and the current transportation infrastructure in the country make the use of a car a necessity rather than an option for a large proportion of older adults. However, as they age, their physical and mental health deteriorates, making driving dangerous for them. Therefore, it is of great importance to understand the transportation problems of older adults and provide them with reliable and acceptable alternative modes of transportation to help them meet their transportation needs. The study presented here aims to examine the transportation problems of older adults living in urban and suburban areas, make policy recommendations, and identify effective strategies to help them meet their mobility needs. To this end, the study used a mixed-method approach to identify the factors that influence older adults\u27 travel behavior and the issues they face when walking, biking, and using transit. In-depth, one-on-one surveys were conducted in three counties in southeastern Wisconsin with 178 English-speaking older adults aged 65 and older living independently in institutionalized senior housing (i.e., subsidized housing and retirement communities) and in noninstitutionalized buildings. The first main chapter of the thesis (Chapter 4) examines the factors that influence older adults\u27 mode choice for grocery shopping and aims to predict older adults\u27 travel behavior for going to the grocery store. A quantitative analysis involving statistical and machine learning techniques was conducted with older adults who traveled to the grocery store by car, carpool, walking, or public transit (N=153). The results of the study show that household car ownership and having a valid driver\u27s license are the most important factors influencing travel mode choice by older adults. However, age group (65-74 or 75+) and physical disability were not significant factors influencing older adults\u27 choice of transportation mode for grocery shopping. The second main chapter of this study (Chapter 5) examines the reasons why older adults who hold a valid driver\u27s license intend to renew their license when it expires (yes), or whether they do not intend to do so or are hesitant (no/not sure). Using a mixed-method approach including binomial logit regression and qualitative analysis, 116 older adults were surveyed. Results suggest that being 75 years of age and older, having a physical disability, and having a lower level of education (high school and below) negatively influence older adults\u27 decision to renew their driver\u27s license. Older adults who drive frequently and indicate that they would like to be able to drive to destinations easily are more likely to renew their driver\u27s license after it expires. The third main chapter of this thesis (Chapter 6) aims to examine the barriers and challenges older adults face when using modes of transportation other than the personal automobile, such as walking, bicycling, public transit, and ride-hailing. A qualitative content analysis of the 103 open-ended responses was used to fit the results into an ecological model. The study recommends four main actions to help policymakers and city governments overcome these barriers: (1) implement transportation education and outreach programs, (2) improve accessibility to services and facilities through land use policies, (3) improve transportation infrastructure and services, and (4) help for-profit and nonprofit organizations organize informal groups to walk, bike, or carpool together. This thesis has important implications for policy makers and urban practitioners to meet the transportation needs of older adults. Improving transportation infrastructure and providing older adults with reliable and high-standard non-automobile transportation alternatives, managing future land use dynamics and investing in sustainable land use patterns, and coordinating with organizations to support social networks (such as informal clubs and local groups) that help older adults meet their travel needs are among some of these important implications