Safety of the Introduction of Self-driving Vehicles in a Logistics Environment

Thanks to the increasing speed of technological development brought by the 21st century, many of the processes in industrial production have been automated. The next big step in this progress of artificial intelligence could potentially be in corporate logistics, road haulage, and even private transport. Naturally, automation within corporations and in critical infrastructure raises ethical and trust-related issues, reflected in the attitude of emerging markets toward this form of development. This research aims to discover the current state of development of self-driving vehicles through comprehensive literary research and to present those ethical problems, the solutions to which are paramount for society to accept and to incorporate the concept of self-driving vehicles. As a result of the analysis of a knowledge-based system with the inductive inference method in primary research, the current strengths and weaknesses of automation in the field of logistics are revealed. The paper summarizes the results of the primary research and projects them for the future of automation

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

Mediating disruption in human-computer interaction from implicit metrics of attention

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 143-150).Multitasking environments cause people to be interrupted constantly, often disrupting their ongoing activities and impeding reaching their goals. This thesis presents a disruption reducing approach designed to support the user's goals and optimize productivity that is based on a model of the user's receptivity to an interruption. The model uses knowledge of the interruption content, context and priority of the task(s) in progress, user actions and goal-related concepts to mediate interruptions. The disruption management model is distinct from previous work by the addition of implicit sensors that deduce the interruption content and user context to help determine when an interruption will disrupt an ongoing activity. Domain-independent implicit sensors include mouse and keyboard behaviors, and goal-related concepts extracted from the user documents. The model also identifies the contextual relationship between interruptions and user goals as an important factor in how interruptions are controlled. The degree to which interruptions are related to the user goal determines how those interruptions will be received. We tested and evolved the model in various cases and showed significant improvement in both productivity and satisfaction. A disruption manager application controls interruptions on common desktop computing activities, such as web browsing and instant messaging. The disruption manager demonstrates that mediating interruptions by supporting the user goals can improve performance and overall productivity. Our evaluation shows an improvement in success of over 25% across prioritization conditions for real life computing environments.(cont.) Goal priority and interruption relevance play an important role in the interruption decision process and several experiments these factors on people's reactions and availability to interruptions, and overall performance. These experiments demonstrate that people recognize the potential benefits of being interrupted and adjust their susceptibility to interruptions during highly prioritized tasks. The outcome of this research includes a usable model that can be extended to tasks as diverse as driving an automobile and performing computer tasks. This thesis supports mediating technologies that will recognize the value of communication and control interruptions so that people are able to maintain concentration amidst their increasingly busy lifestyles.by Ernesto Arroyo Acosta.Ph.D

A tool to measure engineering students' design strategies and abilities

Malaysia is moving from service-based industries to knowledge-based industries to achieve Vision 2020. This is also in line with innovation-led economy and globalization. Hence, there is a pressing need to transform the current engineering education system to meet the ever-growing roles and responsibilities of contemporary engineers. Design is central to engineering activity because students can apply the theoretical knowledge into practice. Students' engineering design knowledge and abilities are unknown when they enroll into the engineering programs. These engineering programs are either too low or too advanced for the students' design abilities. Hence, dropout rates in the engineering programs escalate each year. The invention is an online questionnaire which will assist in determining students' engineering design knowledge and abilities. It will guide engineering faculties in identifying suitable candidates for their engineering program through the scores obtained by the students. The scores will determine the students' level of engineering design knowledge. This will function as a mechanism for the faculties to filter the candidates' knowledge of engineering design and reduce the number of dropouts from the engineering program

A model for mobile, context-aware in-car communication systems to reduce driver distractions

Driver distraction remains a matter of concern throughout the world as the number of car accidents caused by distracted driving is still unacceptably high. Industry and academia are working intensively to design new techniques that will address all types of driver distraction including visual, manual, auditory and cognitive distraction. This research focuses on an existing technology, namely in-car communication systems (ICCS). ICCS allow drivers to interact with their mobile phones without touching or looking at them. Previous research suggests that ICCS have reduced visual and manual distraction. Two problems were identified in this research: existing ICCS are still expensive and only available in limited models of car. As a result of that, only a small number of drivers can obtain a car equipped with an ICCS, especially in developing countries. The second problem is that existing ICCS are not aware of the driving context, which plays a role in distracting drivers. This research project was based on the following thesis statement: A mobile, context-aware model can be designed to reduce driver distraction caused by the use of ICCS. A mobile ICCS is portable and can be used in any car, addressing the first problem. Context-awareness will be used to detect possible situations that contribute to distracting drivers and the interaction with the mobile ICCS will be adapted so as to avert calls and text messages. This will address the second problem. As the driving context is dynamic, drivers may have to deal with critical safety-related tasks while they are using an existing ICCS. The following steps were taken in order to validate the thesis statement. An investigation was conducted into the causes and consequences of driver distraction. A review of literature was conducted on context-aware techniques that could potentially be used. The design of a model was proposed, called the Multimodal Interface for Mobile Info-communication with Context (MIMIC) and a preliminary usability evaluation was conducted in order to assess the feasibility of a speech-based, mobile ICCS. Despite some problems with the speech recognition, the results were satisfying and showed that the proposed model for mobile ICCS was feasible. Experiments were conducted in order to collect data to perform supervised learning to determine the driving context. The aim was to select the most effective machine learning techniques to determine the driving context. Decision tree and instance-based algorithms were found to be the best performing algorithms. Variables such as speed, acceleration and linear acceleration were found to be the most important variables according to an analysis of the decision tree. The initial MIMIC model was updated to include several adaptation effects and the resulting model was implemented as a prototype mobile application, called MIMIC-Prototype

Determining Additional Modulus of Subgarde Reaction Based on Tolerable Settlement for the Nailed-slab System Resting on Soft Clay.

Abstract—Nailed-slab System is a proposed alternative solution for rigid pavement problem on soft soils. Equivalent modulus of subgrade reaction (k’) can be used in designing of nailed-slab system. This modular is the cumulative of modulus of subgrade reaction from plate load test (k) and additional modulus of subgrade reaction due to pile installing (∆∆∆∆k). A recent method has used reduction of pile resistance approach in determining ∆∆∆∆k. The relative displacement between pile and soils, and reduction of pile resistance has been identified. In fact, determining of reduction of pile resistance is difficult. This paper proposes an approach by considering tolerable settlement of rigid pavement. Validation is carried out with respect to a loading test of nailed-slab models. The models are presented as strip section of rigid pavement. The theory of beams on elastic foundation is used to calculate the slab deflection by using k’. Proposed approach can results in deflection prediction close to observed one. In practice, the Nailed-slab System would be constructed by multiple-row piles. Designing this system based on one-pile row analysis will give more safety design and will consume less time

Project54 vehicle telematics for remote diagnostics, fleet management and traffic monitoring

The Project54 system was developed to introduce advanced technologies into the operations of the New Hampshire Department of Safety and other law enforcement agencies. The application of computing, sensing and telecommunication technologies within the Project54 system enables advanced telematics services that can provide benefits to vehicle operators, fleet managers and the public. This thesis describes the implementation of remote diagnostics and fleet management services for the Project54 system and investigates the use of radar equipped police vehicles as traffic probes. Aftermarket diagnostic hardware has been integrated in the Project54 system and software applications have been developed to control the hardware and record diagnostic information. An electronic data entry form has been created for tracking vehicle operating expenses and a vehicle status reporting system is described. Additionally, a traffic congestion scoring method using information from traffic radar units is presented

Maximum risk reduction with a fixed budget in the railway industry

Decision-makers in safety-critical industries such as the railways are frequently faced with the complexity of selecting technological, procedural and operational solutions to minimise staff, passengers and third parties’ safety risks. In reality, the options for maximising risk reduction are limited by time and budget constraints as well as performance objectives. Maximising risk reduction is particularly necessary in the times of economic recession where critical services such as those on the UK rail network are not immune to budget cuts. This dilemma is further complicated by statutory frameworks stipulating ‘suitable and sufficient’ risk assessments and constraints such as ‘as low as reasonably practicable’. These significantly influence risk reduction option selection and influence their effective implementation. This thesis provides extensive research in this area and highlights the limitations of widely applied practices. These practices have limited significance on fundamental engineering principles and become impracticable when a constraint such as a fixed budget is applied – this is the current reality of UK rail network operations and risk management. This thesis identifies three main areas of weaknesses to achieving the desired objectives with current risk reduction methods as: Inaccurate, and unclear problem definition; Option evaluation and selection removed from implementation subsequently resulting in misrepresentation of risks and costs; Use of concepts and methods that are not based on fundamental engineering principles, not verifiable and with resultant sub-optimal solutions. Although not solely intended for a single industrial sector, this thesis focuses on guiding the railway risk decision-maker by providing clear categorisation of measures used on railways for risk reduction. This thesis establishes a novel understanding of risk reduction measures’ application limitations and respective strengths. This is achieved by applying ‘key generic engineering principles’ to measures employed for risk reduction. A comprehensive study of their preventive and protective capability in different configurations is presented. Subsequently, the fundamental understanding of risk reduction measures and their railway applications, the ‘cost-of-failure’ (CoF), ‘risk reduction readiness’ (RRR), ‘design-operationalprocedural-technical’ (DOPT) concepts are developed for rational and cost-effective risk reduction. These concepts are shown to be particularly relevant to cases where blind applications of economic and mathematical theories are misleading and detrimental to engineering risk management. The case for successfully implementing this framework for maximum risk reduction within a fixed budget is further strengthened by applying, for the first time in railway risk reduction applications, the dynamic programming technique based on practical railway examples