New Technologies in Transportation

Elektrické vozidlo je lepší náhradou vozidla s vnitřním spalováním, zachovávaje si svůj účel. Elektrické vozidlo nabízí vylepšení výkonu, může vyprodukovat maximální výkon během sekund, bez potřeby převodovky, čímž minimalizuje ztráty na výkonu. Jednoduchý dizajn motoru s jedinou pohybující se částí snižuje náklady na údržbu a celkové opotřebení. Tepelní účinnost vozidla s vnitřním spalováním činí 38 %, v porovnání s 90 % efektivitou proměny elektrické energie na výkon, ukazuje to dominanci elektrického vozidla, v případě že se jedná o výkon a možnosti motoru. Je známo, že vozidla s vnitřním spalováním znečisťují životní prostředí svým procesem spalování, produkcí 67 tun oxidu uhličitého za celý svůj životní cyklus, v porovnání s elektrickým vozidlem, které přímo svým chodem neprodukuje žádné emise, jehož výrobní emise a emise z elektrické výroby, jsou nižší o 50 %. Spolehneme-li na čistší zdroje energie, emise na životní cyklus vozidla mohou být sníženy až o 300 %, nahrazováním znečisťujících tepelných elektráren, čistšími zdroji elektrické energie. Z hlediska bezpečnosti v dopravě, od roku 2001 zaznamenala Evropská unie více než 50% snížení úmrtnosti na cestách s prosazováním pokročilých asistenčních systémů pro řidiče. Má v zájmu tuto úmrtnost dále snižovat testováním a zaváděním nových systémů (eCall, systémy pro předcházení kolizím, …). S pokročilými asistenčními systémy pro řidiče je veřejnost jen pár let od prvních autonomních vozidel, která mohou zlepšit způsob cestování, jak ho nyní známe. Jsou limitovány pouze jejich bezpečností, technologickými chybami a zákonnou odpovědností.Electric vehicle is a better replacement of internal combustion vehicle, while maintaining its purpose. An electric vehicle offers enhancements in power, it can produce maximum power in seconds, without the need for transmission, minimizing losses in power. A simple design of its engine with single movable part reduces maintenance costs and overall wear and tear. Internal combustion vehicle's 38% thermal efficiency, when compared to 90% energy conversion efficiency. showing dominance of electric vehicle in terms of power and engine capabilities. It is known that internal combustion vehicles are polluting environment with their combustion process, producing 67 tons of CO2 emissions per life cycle of the vehicle, in comparison to electric vehicle that does not directly produce any CO2 emissions, its manufacturing and upstream emissions are 50% lower. Depending on cleaner sources of the energy, emissions can be lowered by more than 300% per life cycle of vehicle by replacing polluting heat power plants with cleaner energy generation solutions. In terms of safety in transportation since 2001 European union recorded more than 50% reduction in causalities on the roads with enforcement of advanced driver assistance systems. It tends to further reduce these causalities by testing and implementing new systems (eCall, collision avoidance systems,…). With advanced driver assistance systems society is only few years from first autonomous vehicles that can improve way of transportation we know now. Limited by their own security, technological faults and law liability.

Product Development within Artificial Intelligence, Ethics and Legal Risk

This open-access-book synthesizes a supportive developer checklist considering sustainable Team and agile Project Management in the challenge of Artificial Intelligence and limits of image recognition. The study bases on technical, ethical, and legal requirements with examples concerning autonomous vehicles. As the first of its kind, it analyzes all reported car accidents state wide (1.28 million) over a 10-year period. Integrating of highly sensitive international court rulings and growing consumer expectations make this book a helpful guide for product and team development from initial concept until market launch

Product Development within Artificial Intelligence, Ethics and Legal Risk

This open-access-book synthesizes a supportive developer checklist considering sustainable Team and agile Project Management in the challenge of Artificial Intelligence and limits of image recognition. The study bases on technical, ethical, and legal requirements with examples concerning autonomous vehicles. As the first of its kind, it analyzes all reported car accidents state wide (1.28 million) over a 10-year period. Integrating of highly sensitive international court rulings and growing consumer expectations make this book a helpful guide for product and team development from initial concept until market launch

Next-Generation Smart Cars: Towards a More Intelligent Interactive Infotainment System

abstract: Today, in a world of automation, the impact of Artificial Intelligence can be seen in every aspect of our lives. Starting from smart homes to self-driving cars everything is run using intelligent, adaptive technologies. In this thesis, an attempt is made to analyze the correlation between driving quality and its impact on the use of car infotainment system and vice versa and hence the driver distraction. Various internal and external driving factors have been identified to understand the dependency and seriousness of driver distraction caused due to the car infotainment system. We have seen a number UI/UX changes, speech recognition advancements in cars to reduce distraction. But reducing the number of casualties on road is still a persisting problem in hand as the cognitive load on the driver is considered to be one of the primary reasons for distractions leading to casualties. In this research, a pathway has been provided to move towards building an artificially intelligent, adaptive and interactive infotainment which is trained to behave differently by analyzing the driving quality without the intervention of the driver. The aim is to not only shift focus of the driver from screen to street view, but to also change the inherent behavior of the infotainment system based on the driving statistics at that point in time without the need for driver intervention.Dissertation/ThesisMasters Thesis Software Engineering 201

Predicting Safety Benefits of Automated Emergency Braking at Intersections - Virtual simulations based on real-world accident data

Introduction: Intersections are a global traffic safety concern. In the United States, around half of all fatal road traffic accidents take place at intersections or were related to them. In the European Union, about one fifth of road traffic fatalities occur at intersections.Intersection Automated Emergency Braking (AEB) seems to be a promising technology with which to address intersection accidents, as information retrieval by on-board sensing is operational on its own, and, in critical situations, braking is initiated independent of driver reaction. This is not the case for Vehicle-to-Everything (V2X) communication, which requires all conflict-involved vehicles to be equipped with this technology and drivers to respond to an initiated warning. The objective of this thesis is to evaluate the effectiveness of a theoretical Intersection AEB system in avoiding accidents and mitigating injuries. As it will take several decades for a new safety technology to penetrate the vehicle fleet and full coverage of all vehicles may never be achieved, the technology benefit is here analyzed as a function of market penetration. Finally, this research assesses whether a set of test scenarios can be derived without compromising the variance of real-world accidents.Methods: Data from the United States National Automotive Sampling System / General Estimates System and the Fatality Analysis Reporting System was used to compare the capacity of on-board sensing and V2X communication to save lives. To investigate Intersection AEB in detail, the German In-Depth Accident Study (GIDAS) data and the related Pre-Crash Matrix (PCM) were utilized to re-simulate accidents with and without Intersection AEB using different parameter settings of technical aspects and driver comfort boundaries. Machine learning techniques were used to identify opportunities for data clustering.Result: On-board sensing has a substantially higher capability to save lives than V2X communication during the period before full market penetration of both is reached. The analysis of GIDAS and PCM data indicate that about two thirds of left-turn across path accidents with oncoming traffic (LTAP/OD) and about 80 percent of straight crossing path (SCP) accidents can be avoid by an idealized Intersection AEB. Moderate to fatal injuries could be avoided to an even higher extent. Key parameters impacting effectiveness are vehicle speed and potential path choice; to increase effectiveness, these should be limited and narrowed down, respectively.Conclusion and Limitations: Intersection AEB is effective in reducing LTAP/OD and SCP accidents and mitigating injuries However, intersection accidents are highly diverse and accurate performance evaluation requires taking variations into account. The simulations were conducted using ideal sensing without processing delays and an ideal coefficient of friction estimation

How do drivers avoid crashes: the role of driving headway

Rear-end crashes are a major part of road injury burden, accounting for one-third of all vehicle-to-vehicle crashes in New South Wales, Australia. Close following or driving with short headways is a key cause, yet the role of driver behaviour in rear-end crash risk is not well researched. The primary aim of this research was to develop a better understanding of rear-end crashes by assessing headways on Australian roads and investigating driver behaviour and performance associated with close following in crash and non-crash scenarios. Two systematic reviews of headway were conducted. First, a review of research on headway identified the need for a consistent and accurate definition of headway, so the thesis puts forward an improved definition. The second review identified the range of external factors that increase the risk of short headway and increase crash risk including speed, task engagement, lead vehicle type, traffic conditions, road characteristics, weather/visibility, drug use, driving fatigue, innovative lane markings, and various warning systems. These factors were then explored in New South Wales data on rear-end casualty and multiple vehicle crashes. The modelling of these associated factors were confirmed as contributing factors in rear-end crashes, congruent with the review of headway. Higher speed, free flowing traffic, volitional task engagement, low cue environments, and collision warning lead to longer headway. Despite lower fatalities, higher odds of injury were observed for rear-end crashes than other crash types. Rear-end crashes were more likely to lead to multiple vehicle crashes, which had a higher chance of fatality than other types of crashes. Finally, naturalistic driving study data was used to investigate headway during normal driving, exploring close following at different speeds and classifying potential risky driving at various headways. In 64 hrs accumulated across 2101 trips, short headways of under 1 s occurred in around 15% of driving. Common manoeuvres to avoid rear-end crashes when close following were changing lanes, or braking, almost always by the following driver. Headway was associated with both driver speed and posted speed limits, decreasing as posted speed limits increased. Over-the-speed-limit driving was observed in all headway scenarios, but especially in higher speed zones. The findings challenge the notion that rear-end crashes are less severe with low injuries. Road users should be made aware of how frequently safe headways are violated and severity of injury outcomes. Driver education, community engagement, application of driver assistance technology consistent with driver behaviour and safety campaigns need to focus on safer speed and headway management to reduce rear-end crash risk

ASSESSING THE IMPACT OF BICYCLE TREATMENTS ON BICYCLE SAFETY: A MULTI-METHODS APPROACH

Compared to other modes, bicyclists are disproportionally affected by crashes considering their low mode share. There is evidence that crashes between bicyclists and motorized vehicle take place at road segments and signalized intersections where bicycle treatments (e.g., bike lanes) are present, urging for in-dept analysis of the safety impact of the various bicycle treatment types. Additionally, it is important to identify sensor types that have the potential to advance field data collection and traffic monitoring in multi-modal road environments. In this dissertation, three approaches, namely crash analysis, traffic conflict analysis, and analysis of driver speeding and glancing behavior, were implemented to investigate the safety impact of bicycle treatments at the segment- and the intersection-levels on bicycle safety. Prediction models were developed to predict bicycle-motorized vehicle crashes at road segments and signalized intersections, and traffic conflicts between straight-going bicyclists and right-turning vehicles at signalized intersections. Driver speeding and glancing behavior was analysed for the segment and the intersection levels. A mode classification framework to classify trajectories recorded using a radar-based sensor was developed to test the feasibility of using radar-based sensors in field studies. The findings of this dissertation contribute to bicycle safety research in terms of quantifying the safety impact of various bicycle treatment types and how to assess and also, by showing how to assess bicycle safety. The findings of this research have the potential to stand as a valuable tool for transportation policymakers and officials in charge of establishing safe bicycle networks