Precise and robust automotive localization is a must for autonomous vehicles. Radar is a cheap and robust sensor, and this project aimed to find a method to use automotive radar to localize globally. By using radar data to build occupancy grids based on other state-of-the-art radar localization methods, and applying image correlation techniques, a localization precision of below 20 cm could be achieved, delivering poses at frequency higher than 0.5 Hz along with a characterization of the uncertainty. By using an improved sensor model for the occupancy grid mapping, filtering the radar data, and using image correlation in the Fourier domain. The presented results are better than the state-of-the-art radar localization methods, both in terms of precision and frequency, however not in terms of heading estimation. The work provides a foundation for future investigations and improvements of radar as a sensor for localization. / Exakt och robust fordonslokalisering är ett måste för framtidens autonoma fordon. Radar är billig och robust sensor, och detta projekt utfördes i syfte att hitta en metod för att använda bilradar för att lokalisera globalt. Genom att använda radardata för att bygga occupancyg grids baserade på de senaste bästa radarlokaliseringsmetoder och tillämpa bildkorrelationstekniker, kunde en lokaliseringsprecision bättre än 20 cm uppnås, vilket ger positioner vid frekvens högre än 0,5 Hz tillsammans osäkerhetens karaktärisering. Genom att använda en förbättrad sensormodell för kartläggning av occupancy grids, filtrera radardata och använda bildkorrelation i Fourier- domänen. De presenterade resultaten är bättre än de senaste metoderna för radarlokalisering, både när det gäller precision och frekvens, men inte när det gäller riktning. Arbetet utgör en grund för framtida undersökningar av radar som en sensor för lokalisering.

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Autonomous vehicles are predicted to disrupt the current landscape of urban mobility.Many studies have investigated how autonomous vehicles, either operated as a serviceor as private cars, could compete against public transport and even replace it. Fewerstudies have investigated how autonomous vehicles could actually be an opportunityfor the public transport sector, as a new type of offer that would cover specific needsalong traditional modes such as buses or metros.The aim of this project is to quantify the effect of replacing part of the public transportnetwork of Uppsala by demand-responsive autonomous fleets. This is achieved bybuilding a transport model based on the traditional four-step transport model andcalculating the total cost of the network both from the passenger and the operator’sperspective.The study shows that autonomous vehicles can slightly improve the performance of thenetwork and work best when combined with traditional bus lines. However, they alsoincrease the traffic and have a risk to cause congestion.

Gaffeltruckar är motordrivna fordon som används flitigt inom industrin för att transportera gods. Automatiseringen av dessa är ett steg i linje med digitaliseringen av industrin och ett prioriterat säkerhetsarbete, och då semiautonoma truckar anses säkrare ökar både utbud och efterfrågan på dessa. Syftet med denna rapport var att undersöka hur företag involverar användare samt integrerar säkerhetsaspekter i sin produktutvecklingsprocess av semiautonoma gaffeltruckar för att minimera risken för personskador. Även synen på automatisering bland användare och tillverkare studerades, samt vilka möjligheter och utmaningar som automatiseringen medför. Forskningsfrågorna besvarades med hjälp av en litteraturstudie och en intervjustudie med fem respondenter från tre olika företag. Resultaten från intervjustudien sammanställdes och jämfördes med den teoretiska referensramen i en efterföljande analys och diskussion. Studien visade att säkerhetsaspekterna beaktas och integreras systematiskt i början av produktutvecklingsprocessen. Tillverkarna såväl som användarna ansåg att säkerhet är en allt viktigare aspekt som upplevs ha blivit mycket bättre under bara de senaste tio åren och säkerhetsaspekterna har utvecklats till att bli mer av hygienfaktorer som måste uppnås på grund av lagkrav och regleringar. En upptäckt som gjordes var därför att användare i regel inte involveras i syfte att integrera säkerhetsfunktioner utan snarare för att möta behov som är svårare att definiera och som handlar om hur produkten ska kännas och upplevas vid användning; såsom körkänsla och ergonomi. Det finns goda möjligheter att öka säkerheten med hjälp av automatisering av gaffeltruckar. Många tillbud och olyckor uppstår på grund av mänskliga faktorer, såsom missförstånd och ouppmärksamhet. Att reducera eller eliminera den mänskliga delen skulle troligen bidra till färre problematiska situationer och misstag, vilket kan rädda liv. Andra möjligheter är kopplade till produktivitet, där det bland annat finns ekonomiska vinster att hämta när industrin effektiviseras. Samtidigt finns det alltid utmaningar i samband med stora digitala omställningar. Automatiseringen av materialhanteringen innebär en investering som behöver godkännas och genomföras, vilket även kräver förarbete och efterforskning. Just bristande kunskap var något som framhävdes som ett hinder, men varken bland användare eller tillverkare ansågs hindrena större än möjligheterna. / Forklifts are motor-driven vehicles which are frequently used for the transportation of goods in the industry. The automation of these is a step in the right direction when it comes to digitalization and prioritized safety work, and as semi-autonomous forklifts are regarded as safer, both the supply and the demand for them are increasing. The aim of this report was to investigate how companies involve users and integrate safety aspects in their product development process of semi-autonomous forklifts to minimize the risk for bodily injuries. Furthermore, the view on automation among users and manufacturers was studied, along with the possibilities and challenges automation entails. The research questions were answered through a literature study and an interview study with five respondents from three different companies. The results from the interview study were compiled and compared with the theoretical source in a subsequent analysis and discussion. The study showed that safety aspects are regarded and integrated systematically during the start of the product development process. The manufacturers as well as the users believed that safety is an aspect which is becoming more and more important and which is perceived as having improved immensely during the last ten years. The safety aspects have evolved into more of hygiene factors which need to be reached due to legislative demands and regulations. A finding from this study was therefore that users were not involved with the purpose to integrate safety functions but rather to meet demands which are more difficult to define and which concern how the product should feel and be perceived in usage, such as driving sense and ergonomics. There are good possibilities to increase safety by automation. Many mishaps and accidents originate from human factors, such as misunderstandings and lack of attention. Reducing or eliminating the human contribution would probably lead to fewer problematic situations and mistakes, which could save lives. Other possibilities are in productivity, where there are financial profits to gain when streamlining the industry. However, there are always challenges related to large, digital transitions. Automation of the material handling means an investment which needs to be approved and realized, which also requires preparatory work and research. Lack of knowledge was a suggested obstacle, but neither the users nor the manufacturers considered the obstacles larger than the possibilities.

A major challenge in the transportation industry is how to reduce the emissions of greenhouse gases. One way of achieving this in vehicles is to drive more fuel-efficiently. One recently developed technique that has been successful in reducing the fuel consumption is the look-ahead cruise controller, which utilizes future conditions such as road topography. In this this thesis, similar methods are used in order to reduce the fuel consumption of heavy-duty vehicles driving in environments where the required and desired velocity vary. The main focus is on vehicles in urban driving, which must alter their velocity due to, for instance, changing legal speed restrictions and the presence of intersections. The driving missions of such vehicles are here formulated as optimal control problems. In order to restrict the vehicle to drive in a way that does not deviate too much from a normal way of driving, constraints on the velocity are imposed based on statistics from real truck operation. In a first approach, the vehicle model is based on forces and the cost function involves the consumed energy. This problem is solved both offline using Pontryagin's maximum principle and online using a model predictive controller with a quadratic program formulation. Simulations show that 7 % energy can be saved without increasing the trip time nor deviating from a normal way of driving. In a second approach, the vehicle model is extended to include an engine and a gearbox with the objective of minimizing the fuel consumption. A fuel map for the engine and a polynomial function for the gearbox losses are extracted from experimental data and used in the model. This problem is solved using dynamic programming taking into consideration gear changes, coasting with gear and coasting in neutral. Simulations show that by allowing the use of coasting in neutral gear, 13 % fuel can be saved without increasing the trip time or deviating from a normal way of driving. Finally, an implementation of a rule-based controller into an advanced vehicle model in highway driving is performed. The controller identifies sections of downhills where fuel can be saved by coasting in neutral gear. / En stor utmaning för transportsektorn är hur utsläppen av växthusgaser ska minskas. Detta kan åstadkommas i fordon genom att köra bränslesnålare. En nyligen utvecklad teknik som har varit framgångsrik i att minska bränsleförbrukningen är framförhållningsreglering, som använder framtida förhållanden så som vägtopografi. I denna avhandling används liknande metoder för att minska bränsleförbrukningen i tunga fordon som kör i miljöer där önskad och tvingad hastighet varierar. Fokus ligger framförallt på fordon i stadskörning, där hastigheten måste varieras beroende på bland annat hastighetsbegränsningar och korsningar. Denna typ av körning formuleras här som optimala reglerproblem. För att hindra fordonet från att avvika för mycket från ett normalt körbeteende sätts begränsningar på tillåten hastighet baserat på statistik från verklig körning. Problemet angrips först genom att använda en fordonsmodell baserad på krafter och en kriteriefunktion innehållande energiförbrukning. Problemet löses både offline med Pontryagin's maximum princip och online med modellprediktiv reglering baserad på kvadratisk programmering. Simuleringar visar att 7 % energi kan sparas utan att öka körtiden eller avvika från ett normalt körbeteende. Problemet angrips sedan genom att utöka fordonsmodellen till att också innehålla motor och växellåda med målet att minimera bränsleförbrukningen. Specifik bränsleförbrukning och en polynomisk approximation av förlusterna i växellådan är extraherade från experiment och används i simuleringarna. Problemet löses genom dynamisk programmering som tar hänsyn till växling, släpning och frirullning. Simuleringar visar att 13 % bränsle kan sparas utan att öka körtid eller avvika från normalt körbeteende genom att tillåta frirullning. Slutligen görs en implementering av en regelbaserad regulator på en avancerad fordonsmodell för ett fordon i motorvägskörning. Regulatorn identifierar sektioner med nedförsbackar där bränsle kan sparas genom frirulllning. / <p>QC 20171011</p>

Autonomous Vehicles (AVs) should result in tremendous benefits to safe human transportation. Recent reports indicate a global average of 3,287 road crash related fatalities a day with the blame, in most cases, assigned to the human driver. By replacing the main cause, AVs are predicted to significantly reduce road accidents -- some claiming up to a 90% reduction on US roads. However, achieving these numbers is not simple. AVs are expected to assume tasks that human drivers perform both consciously and unconsciously -- in some instances, with Machine Learning. AVs incur new levels of complexity that, if handled incorrectly, can result in failures that cause loss of human life and damage to the environment. Accidents involving SAE Level 2 vehicles have highlighted such failures and demonstrated that AVs have a long way to go. The path towards safe AVs includes system architectures that provide effective failure monitoring, detection and mitigation. These architectures must produce AVs that degrade gracefully and remain sufficiently operational in the presence of failures. We introduce Safe-AV, a fault tolerant safety architecture for AVs that is based on the commonly adopted E-Gas 3 Level Monitoring Concept, the Simplex Architecture and guided by a thorough hazard analysis in the form of Systems-Theoretic Process Analysis (STPA). We commenced the architecture design with a review of some modern AV accidents which helped identify the types of failures AVs can present and acted as a first step to our STPA. The hazard analysis was applied to an initial AV architecture (without safety mechanisms) consisting of components that should be present in a typical AV (based on the literature and our ideas). Our STPA identified the system level accidents, hazards and corresponding loss scenarios that led to well-founded safety requirements which, in turn, evolved the initial architecture into Safe-AV. / Thesis / Master of Applied Science (MASc)

In the realm of autonomous vehicles, ensuring safety through advanced anomaly detection is crucial. This thesis integrates Reservoir Computing with temporal-aware data analysis to enhance driver behavior assessment and traffic flow prediction. Our approach combines Reservoir Computing with autoencoder-based feature extraction to analyze driving metrics from vehicle sensors, capturing complex temporal patterns efficiently. Additionally, we extend our analysis to forecast traffic flow dynamics within road networks using the same framework. We evaluate our model using the PEMS-BAY and METRA-LA datasets, encompassing diverse traffic scenarios, along with a GPS dataset of 10,000 taxis, providing real-world driving dynamics. Through a support vector machine (SVM) algorithm, we categorize drivers based on their performance, offering insights for tailored anomaly detection strategies. This research advances anomaly detection for autonomous vehicles, promoting safer driving experiences and the evolution of vehicle safety technologies. By integrating Reservoir Computing with temporal-aware data analysis, this thesis contributes to both driver behavior assessment and traffic flow prediction, addressing critical aspects of autonomous vehicle systems. / Master of Science / Our cities are constantly growing, and traffic congestion is a major challenge. This project explores how innovative technology can help us predict traffic patterns and develop smarter management strategies. Inspired by the rigorous safety systems being developed for self-driving cars, we'll delve into the world of machine learning. By combining advanced techniques for identifying unusual traffic patterns with tools that analyze data over time, we'll gain a deeper understanding of traffic flow and driver behavior. We'll utilize data collected by car sensors, such as speed and turning patterns, to not only predict traffic jams but also see how drivers react in different situations. However, our project has a broader scope than just traffic flow. We aim to leverage this framework to understand driver behavior in general, with a particular focus on its implications for self-driving vehicles. Through meticulous data analysis and sophisticated algorithms, we can categorize drivers based on their performance. This valuable information can be used to develop improved methods for detecting risky situations, ultimately leading to safer roads and smoother traffic flow for everyone. To ensure the effectiveness of our approach, we'll rigorously test it using real-world data from GPS data from taxi fleets and nationally recognized traffic datasets. By harnessing the power of machine learning and tools that can adapt to changing data patterns, this project has the potential to revolutionize traffic management in cities. This paves the way for a future with safer roads, less congestion, and a more positive experience for everyone who lives in and travels through our bustling urban centers.

<p dir="ltr">Autonomous vehicles require motion forecasting of their surrounding multiagents (pedestrians</p><p dir="ltr">and vehicles) to make optimal decisions for navigation. The existing methods focus on</p><p dir="ltr">techniques to utilize the positions and velocities of these agents and fail to capture semantic</p><p dir="ltr">information from the scene. Moreover, to mitigate the increase in computational complexity</p><p dir="ltr">associated with the number of agents in the scene, some works leverage Euclidean distance to</p><p dir="ltr">prune far-away agents. However, distance-based metric alone is insufficient to select relevant</p><p dir="ltr">agents and accurately perform their predictions. To resolve these issues, we propose the</p><p dir="ltr">Semantics-aware Interactive Multiagent Motion Forecasting (SIMMF) method to capture</p><p dir="ltr">semantics along with spatial information and optimally select relevant agents for motion</p><p dir="ltr">prediction. Specifically, we achieve this by implementing a semantic-aware selection of relevant</p><p dir="ltr">agents from the scene and passing them through an attention mechanism to extract</p><p dir="ltr">global encodings. These encodings along with agents’ local information, are passed through</p><p dir="ltr">an encoder to obtain time-dependent latent variables for a motion policy predicting the future</p><p dir="ltr">trajectories. Our results show that the proposed approach outperforms state-of-the-art</p><p dir="ltr">baselines and provides more accurate and scene-consistent predictions. </p>

Traffic signals are used at intersections to manage the flow of vehicles by allocating right-of-way in a timely manner for different users of the intersection. Traffic signals are therefore installed at an intersection to improve overall safety and to decrease vehicular average delay. However, the variation of driving speed in response to these signals causes an increase in fuel consumption and air emission levels. One solution to this problem is Eco-Cooperative Adaptive Cruise Control (Eco-CACC), which attempts to reduce vehicle fuel consumption and emission levels by optimizing driver behavior in the vicinity of a signalized intersection. Various Eco-CACC algorithms have been proposed by researchers to address this issue. With the help of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication, algorithms are being developed that utilize signal phasing and timing (SPaT) data together with queue information to optimize vehicle trajectories in the vicinity of signalized intersections. The research presented in this thesis constitutes the third phase of a project that entailed developing and evaluating an Eco-CACC system. Its main objective is to evaluate the benefits of the newly developed Eco-CACC algorithm that was proposed by the Center for Sustainable Mobility at the Virginia Tech Transportation Institute. This algorithm uses advanced signal information (SPaT) to compute the fuel-optimal trajectory of vehicles, and, then, send recommended speeds to drivers as an audio message or implement them directly into the subject vehicle. The objective of this study is to quantitatively quantify the fuel-efficiency of the Eco-CACC system in a real field environment. In addition, another goal of this study is to address the implementation issues and challenges with the field application of the Eco-CACC system. A dataset of 2112 trips were collected as part of this research effort using a 2014 Cadillac SRX equipped with a vehicle onboard unit for (V2V) and (V2I) communication. A total of 32 participants between the ages of 18 and 30 were randomly selected from one age group (18-30) with an equal number of males and females. The controlled experiment was conducted on the Virginia Smart Road facility during daylight hours for dry pavement conditions. The controlled field experiment included four different scenarios: normal driving, driving with red indication countdown information provided to drivers, driving with recommended speed information computed by the Eco-CACC system and delivered to drivers, and finally automated driving (automated Eco-CACC system). The controlled field experiment was conducted for four values of red indication offsets along an uphill and downhill approach. The collected data were compared with regard to fuel economy and travel time over a fixed distance upstream and downstream of the intersection (820 ft (250 m) upstream of the intersection to 590 ft (180 m) downstream for a total length of 1410 ft (430 m)). The results demonstrate that the Eco-CACC system is very efficient in reducing fuel consumption levels especially when driving downhill. The field data indicates that the automated scenario could produce fuel and travel time savings of 31% and 9% on average, respectively. In addition, the study demonstrates that driving with a red indication countdown and recommended speed information can produce fuel savings ranging from 4 to 21 percent with decreases in travel times ranging between 1 and 10 percent depending on the value of red indication offset and the direction. Split-split-plot design was used to analyze the data and test significant differences between the four scenarios with regards to fuel consumption and travel time. The analysis shows that the differences between normal driving and driving with either the manual or automated Eco-CACC systems are statistically significant for all the red indication offset values. / Master of Science

Thesis (PhD (Electrical and Electronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: Autonomous vehicles have recently received much attention from researchers. The prospect of safe and reliable autonomous vehicles for general, unregulated environments promises several advantages over human-controlled vehicles, including increased efficiency, reliability and capability with the associated decrease in danger to humans and reduction in operating costs. A critical requirement for the safe operation of fully autonomous vehicles is their ability to avoid collisions with obstacles and other vehicles. In addition, they are often required to maintain a minimum separation from obstacles and other vehicles, which is called conflict avoidance. The research presented in thesis focuses on methods for effective conflict avoidance. Existing conflict avoidance methods either make limiting assumptions or cannot execute in real-time due to computational complexity. This thesis proposes methods for real-time conflict avoidance in uncertain, cluttered and dynamic environments. These methods fall into the category of non-cooperative conflict avoidance. They allow very general vehicle and environment models, with the only notable assumption being that the position and velocity states of the vehicle and obstacles have a jointly Gaussian probability distribution. Conflict avoidance for fully autonomous vehicles consists of three functions, namely modelling and identification of the environment, conflict detection and conflict resolution. We present an architecture for such a system that ensures stable operation. The first part of this thesis comprises the development of a novel and efficient probabilistic conflict detection method. This method processes the predicted vehicle and environment states to compute the probability of conflict for the prediction period. During the method derivation, we introduce the concept of the flow of probability through the boundary of the conflict region, which enables us to significantly reduce the complexity of the problem. The method also assumes Gaussian distributed states and defines a tight upper bound to the conflict probability, both of which further reduce the problem complexity, and then uses adaptive numerical integration for efficient evaluation. We present the results of two simulation examples which show that the proposed method can calculate in real-time the probability of conflict for complex and cluttered environments and complex vehicle maneuvers, offering a significant improvement over existing methods. The second part of this thesis adapts existing kinodynamic motion planning algorithms for conflict resolution in uncertain, dynamic and cluttered environments. We use probabilistic roadmap methods and suggest three changes to them, namely using probabilistic conflict detection methods, sampling the state-time space instead of the state space and batch generation of samples. In addition, we propose a robust and adaptive way to choose the size of the sampling space using a maximum least connection cost bound. We then put all these changes together in a proposed motion planner for conflict resolution. We present the results of two simulation examples which show that the proposed motion planner can only find a feasible path in real-time for simple and uncluttered environments. However, the manner in which we handle uncertainty and the sampling space bounds offer significant contributions to the conflict resolution field / AFRIKAANSE OPSOMMING: Outonome voertuie het die afgelope tyd heelwat aandag van navorsers geniet. Die vooruitsig van veilige en betroubare outonome voertuie vir algemene en ongereguleerde omgewings beloof verskeie voordele bo menslik-beheerde voertuie en sluit hoër effektiwiteit, betroubaarheid en vermoëns asook die gepaardgaande veiligheid vir mense en laer bedryfskoste in. ’n Belangrike vereiste vir die veilige bedryf van volledig outonome voertuie is hul vermoë om botsings met hindernisse en ander voertuie te vermy. Daar word ook dikwels van hulle vereis om ’n minimum skeidingsafstand tussen hulle en die hindernisse of ander voertuie te handhaaf – dit word konflikvermyding genoem. Die navorsing in hierdie tesis fokus op metodes vir effektiewe konflikvermyding. Bestaande konflikvermydingsmetodes maak óf beperkende aannames óf voer te stadig uit as gevolg van bewerkingskompleksiteit. Hierdie tesis stel metodes voor vir intydse konflikvermyding in onsekere en dinamiese omgewings wat ook baie hindernisse bevat. Die voorgestelde metodes val in die klas van nie-samewerkende konflikvermydingsmetodes. Hulle kan algemene voertuig- en omgewingsmodelle hanteer en hul enigste noemenswaardige aanname is dat die posisie- en snelheidstoestande van die voertuig en hindernisse Gaussiese waarskynliksheidverspreidings toon. Konflikvermyding vir volledig outonome voertuie bestaan uit drie stappe, naamlik modellering en identifikasie van die omgewing, konflikdeteksie en konflikresolusie. Ons bied ’n argitektuur vir so ’n stelsel aan wat stabiele werking verseker. Die eerste deel van die tesis beskryf die ontwikkeling van ’n oorspronklike en doeltreffende metode vir waarskynliksheid-konflikdeteksie. Die metode gebruik die voorspelde toestande van die voertuig en omgewing en bereken die waarskynlikheid van konflik vir die betrokke voorspellingsperiode. In die afleiding van die metode definiëer ons die konsep van waarskynliksheidvloei oor die grens van die konflikdomein. Dit stel ons in staat om die kompleksiteit van die probleem beduidend te verminder. Die metode aanvaar ook Gaussiese waarskynlikheidsverspreiding van toestande en definiëer ’n nou bogrens tot die waarskynlikheid van konflik om die kompleksiteit van die probleem verder te verminder. Laastens gebruik die metode aanpasbare integrasiemetodes vir vinnige berekening van die waarskynlikheid van konflik. Die eerste deel van die tesis sluit af met twee simulasies wat aantoon dat die voorgestelde konflikdeteksiemetode in staat is om die waarskynlikheid van konflik intyds te bereken, selfs vir komplekse omgewings en voertuigbewegings. Die metode lewer dus ’n beduidende bydrae tot die veld van konflikdeteksie. Die tweede deel van die tesis pas bestaande kinodinamiese beplanningsalgoritmes aan vir konflikresolusie in komplekse omgewings. Ons stel drie veranderings voor, naamlik die gebruik van waarskynliksheid-konflikdeteksiemetodes, die byvoeg van ’n tyd-dimensie in die monsterruimte en die generasie van meervoudige monsters. Ons stel ook ’n robuuste en aanpasbare manier voor om die grootte van die monsterruimte te kies. Al die voorafgaande voorstelle word saamgevoeg in ’n beplanner vir konflikresolusie. Die tweede deel van die tesis sluit af met twee simulasies wat aantoon dat die voorgestelde beplanner slegs intyds ’n oplossing kan vind vir eenvoudige omgewings. Die manier hoe die beplanner onsekerheid hanteer en die begrensing van die monsterruimte lewer egter waardevolle bydraes tot die veld van konflikresolusie