A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

open access articleDetecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics. Knowing the degree of driver distraction can aid in accident prevention techniques, including transitioning of control to a level 4 semi- autonomous vehicle, when a high distraction severity level is reached. Thus, enhancement of Advanced Driving Assistance Systems (ADAS) is a critical component in the safety of vehicle drivers and other road users. In this paper, a new methodology is introduced, using an expert knowledge rule system to predict the severity of distraction in a contiguous set of video frames using the Naturalistic Driving American University of Cairo (AUC) Distraction Dataset. A multi-class distraction system comprises the face orientation, drivers’ activities, hands and previous driver distraction, a severity classification model is developed as a discrete dynamic Bayesian (DDB). Furthermore, a Mamdani-based fuzzy system was implemented to detect multi- class of distractions into a severity level of safe, careless or dangerous driving. Thus, if a high level of severity is reached the semi-autonomous vehicle will take control. The result further shows that some instances of driver’s distraction may quickly transition from a careless to dangerous driving in a multi-class distraction context

Coordination of horizontal and sag vertical curves on two-lane rural roads: Driving simulator study

Abstract The highway geometric design guidelines for several countries provide suggestions for the coordination of horizontal curves overlapping with sag vertical curves (sag combinations) to avoid combined configurations that produce undesirable optical effects and reduced safety. Such suggestions are derived from studies based on the drawing of the perspective of the road. This drawing method is severely limited with respect to the simulation of the perspective view of the highway to the driver during the dynamic task of driving. Interactive driving simulation methods are deemed to be more efficient for these objectives. This paper reports the results of a study carried out using an interactive driving simulator to evaluate the effects on the driver's speed behavior of different configurations of sag combinations and non-combined curves on a flat grade with the same features as the horizontal curves of the sag combinations (reference curves). The speed behaviors of drivers along the tangent–curve transitions of sag combinations and reference curves were recorded. The speed on the approach tangent, the speed at the midpoint of the horizontal curve and the maximum speed reduction (MSR), the difference between the maximum speed on the last 200 m of the approach tangent and the minimum speed on the first half of the horizontal curve, were analyzed. One-way repeated MANOVA was performed to determine if the driver's speed behavior on the horizontal curves was influenced by different configurations of sag combinations and reference curves. The primary result was that on suggested sag combinations, the driver's speed behavior did not differ in any statistically significant way from that on the reference curves. Whereas the critical sag combinations (configurations that should be avoided) caused high values of maximum speed reduction along the tangent–curve transition, which pointed to the driver's reaction to an incorrect perception of the road alignment. Therefore, this result confirmed the effectiveness of the road design guidelines for the coordination of horizontal curves and sag vertical curves

Modeling and Verification of Naturalistic Lane Keeping System

In order to lower human drivers’ driving load and to enhance their systematic performance during driving, driver assistant systems have been introduced during the past few decades. Unfortunately, a large proportion of existing lane keeping techniques only focus on how to hold the car in the center of the lane, which may be contrary to the driver's natural motion sense. This research focuses on developing a rational and precise driver model with fully human driver operating behavior, which is crucial for the study of active safety technology and can provide drivers with a comfortable motion by imitating driving habits and trajectory. Modeling a naturalistic lane keeping control requires understanding of how a driver operates the vehicle, analysis from vehicle lateral dynamics perspective, and knowledge of the combination of driver’s physical limitation. Another requirement to build an adaptive steering control model is to regard driver’s steering behavior as a reciprocal process between anticipation and compensation. Based on two angles (near and far angles) mechanism and experimental data recorded by the SIMULINK and dSpace co-platform, a close-loop system is designed. The whole system is a combination of a PI (proportional–integral) controller driver model and a vehicle model, which integrates vehicle lateral dynamic characteristics and upcoming road information. Moreover, a nonlinear steering driver model is designed. This open loop driver model can effectively correct steering wheel angle by minimizing the error between recorded driving data and that of the simulated model. The simulation outcome shows that the proposed model captures human drivers’ behavior well and has an excellent adaptability towards the change of vehicle dynamic parameters and external disturbances

Making overtaking cyclists safer: Driver intention models in threat assessment and decision-making of advanced driver assistance system

Introduction: The number of cyclist fatalities makes up 3% of all fatalities globally and 7.8% in the European Union. Cars overtaking cyclists on rural roads are complex situations. Miscommunication and misunderstandings between road users may lead to crashes and severe injuries, particularly to cyclists, due to lack of protection. When making a car overtaking a cyclist safer, it is important to understand the interaction between road users and use in the development of an Advanced Driver Assistance System (ADAS). Methods: First, a literature review was carried out on driver and interaction modeling. A Unified Modeling Language (UML) framework was introduced to operationalize the interaction definition to be used in the development of ADAS. Second, the threat assessment and decision-making algorithm were developed that included the driver intention model. The counterfactual simulation was carried out on artificial crash data and field data to understand the intention-based ADAS\u27s performance and crash avoidance compared to a conventional system. The method focused on cars overtaking cyclists when an oncoming vehicle was present. Results: An operationalized definition of interaction was proposed to highlight the interaction between road users. The framework proposed uses UML diagrams to include interaction in the existing driver modeling approaches. The intention-based ADAS results showed that using the intention model, earlier warning or emergency braking intervention can be activated to avoid a potential rear-end collision with a cyclist without increasing more false activations than a conventional system. Conclusion: The approach used to integrate the driver intention model in developing an intention-based ADAS can improve the system\u27s effectiveness without compromising its acceptance. The intention-based ADAS has implications towards reducing worldwide road fatalities and in achieving sustainable development goals and car assessment program

Human-Centric Detection and Mitigation Approach for Various Levels of Cell Phone-Based Driver Distractions

abstract: Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance.Dissertation/ThesisDoctoral Dissertation Applied Psychology 201

Modelling and analysis of current and concept vehicles for the purpose of enhancing vehicle handling: executive summary

In this document, research into the modelling and analysis of current and concept vehicles for the purpose of enhancing vehicle handling is summarised. This work is recounted in detail in a portfolio of reports that has been submitted for the degree of Doctor of Engineering. The portfolio includes fifteen submissions, eleven of which are concerned with the analysis and simulation of drivers' steering behaviour. Two relate to a novel suspension concept. One addresses a current problem caused by suspension variability and one introduces a process for selecting between new suspension concepts. Each of these fifteen submissions is summarised in this document. In addition, the order in which it is recommended that these submissions be read is listed. In section 4, a project summary of the research into the analysis and simulation of drivers' steering behaviour is presented. Existing models of drivers' steering behaviour are reviewed. Vehicle tests that illustrate the different steering styles used by different drivers are recounted. A driver model that simulates the steering behaviour exhibited in these tests is formulated . Then, this driver model is used to develop a switching strategy for variable dampers. It is demonstrated that the switching strategy enhances vehicle handling and reduces the roll experienced by drivers during a handling manoeuvre. Finally, it is verified that this research complies with the requirement of the degree of Doctor of Engineering to demonstrate innovation in the application of knowledge to the engineering business environment. This is achieved by specifying eight examples of where new ideas and methods have been applied to address current issues within the automotive industry

Driving Information in a Transition to a Connected and Autonomous Vehicle Environment: Impacts on Pollutants, Noise and Safety

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