Relevant bibliographies by topics / Autonomus driving

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Autonomus driving'

Author: Grafiati
Published: 25 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Autonomus driving"

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Knabl, Florian, and Lars Mesow. "Autonomes Fahren im Kleinformat Audi Autonomous Driving Cup." Sonderprojekte ATZ/MTZ 22, S2 (December 2017): 26–29. http://dx.doi.org/10.1007/s41491-017-0006-z.

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STAYTON, ERIK, MELISSA CEFKIN, and JINGYI ZHANG. "Autonomous Individuals in Autonomous Vehicles: The Multiple Autonomies of Self-Driving Cars." Ethnographic Praxis in Industry Conference Proceedings 2017, no. 1 (November 2017): 92–110. http://dx.doi.org/10.1111/1559-8918.2017.01140.

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Ansari, Hashim Shakil, and Goutam R. "Autonomous Driving using Deep Reinforcement Learning in Urban Environment." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1573–75. http://dx.doi.org/10.31142/ijtsrd23442.

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Salow, Holger. "Autonomous driving." ATZ worldwide 110, no. 1 (January 2008): 14–18. http://dx.doi.org/10.1007/bf03224976.

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Walch, Marcel, Kristin Mühl, Martin Baumann, and Michael Weber. "Autonomous Driving." International Journal of Mobile Human Computer Interaction 9, no. 2 (April 2017): 58–74. http://dx.doi.org/10.4018/ijmhci.2017040104.

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Autonomous vehicles will need de-escalation strategies to compensate when reaching system limitations. Car-driver handovers can be considered one possible method to deal with system boundaries. The authors suggest a bimodal (auditory and visual) handover assistant based on user preferences and design principles for automated systems. They conducted a driving simulator study with 30 participants to investigate the take-over performance of drivers. In particular, the authors examined the effect of different warning conditions (take-over request only with 4 and 6 seconds time budget vs. an additional pre-cue, which states why the take-over request will follow) in different hazardous situations. Their results indicated that all warning conditions were feasible in all situations, although the short time budget (4 seconds) was rather challenging and led to a less safe performance. An alert ahead of a take-over request had the positive effect that the participants took over and intervened earlier in relation to the appearance of the take-over request. Overall, the authors' evaluation showed that bimodal warnings composed of textual and iconographic visual displays accompanied by alerting jingles and spoken messages are a promising approach to alert drivers and to ask them to take over.
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Fuchs, Andreas. "Autonomous Driving." ATZoffhighway worldwide 11, no. 1 (March 2018): 3. http://dx.doi.org/10.1007/s41321-018-0013-3.

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Poledna, S., F. Eichler, and P. Schöggl. "Autonomous Driving." Sonderprojekte ATZ/MTZ 24, S1 (August 2019): 47. http://dx.doi.org/10.1007/s41491-019-0029-8.

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Mühl, Kristin, Christoph Strauch, Christoph Grabmaier, Susanne Reithinger, Anke Huckauf, and Martin Baumann. "Get Ready for Being Chauffeured." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 8 (September 9, 2019): 1322–38. http://dx.doi.org/10.1177/0018720819872893.

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Objective We investigated passenger’s trust and preferences using subjective, qualitative, and psychophysiological measures while being driven either by human or automation in a field study and a driving simulator experiment. Background The passenger’s perspective has largely been neglected in autonomous driving research, although the change of roles from an active driver to a passive passenger is incontrovertible. Investigations of passenger’s appraisals on self-driving vehicles often seem convoluted with active manual driving experiences instead of comparisons with being driven by humans. Method We conducted an exploratory field study using an autonomous research vehicle ( N = 11) and a follow-up experimental driving simulation ( N = 24). Participants were driven on the same course by a human and an autonomous agent sitting on a passenger seat. Skin conductance, trust, and qualitative characteristics of the perceived driving situation were assessed. In addition, the effect of driving style (defensive vs. sporty) was evaluated in the simulator. Results Both investigations revealed a close relation between subjective trust ratings and skin conductance, with increased trust and by trend reduced arousal for human compared with automation in control. Even though driving behavior was equivalent in the simulator when being driven by human and automation, passengers most preferred and trusted the human-defensive driver. Conclusion Individual preferences for driving style and human or autonomous vehicle control influence trust and subjective driving characterizations. Application The findings are applicable in human-automation research, reminding to not neglect subjective attributions and psychophysiological reactions as a result of ascribed control duties in relation to specific execution characteristics.
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Li, W., C. W. Pan, R. Zhang, J. P. Ren, Y. X. Ma, J. Fang, F. L. Yan, et al. "AADS: Augmented autonomous driving simulation using data-driven algorithms." Science Robotics 4, no. 28 (March 27, 2019): eaaw0863. http://dx.doi.org/10.1126/scirobotics.aaw0863.

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Simulation systems have become essential to the development and validation of autonomous driving (AD) technologies. The prevailing state-of-the-art approach for simulation uses game engines or high-fidelity computer graphics (CG) models to create driving scenarios. However, creating CG models and vehicle movements (the assets for simulation) remain manual tasks that can be costly and time consuming. In addition, CG images still lack the richness and authenticity of real-world images, and using CG images for training leads to degraded performance. Here, we present our augmented autonomous driving simulation (AADS). Our formulation augmented real-world pictures with a simulated traffic flow to create photorealistic simulation images and renderings. More specifically, we used LiDAR and cameras to scan street scenes. From the acquired trajectory data, we generated plausible traffic flows for cars and pedestrians and composed them into the background. The composite images could be resynthesized with different viewpoints and sensor models (camera or LiDAR). The resulting images are photorealistic, fully annotated, and ready for training and testing of AD systems from perception to planning. We explain our system design and validate our algorithms with a number of AD tasks from detection to segmentation and predictions. Compared with traditional approaches, our method offers scalability and realism. Scalability is particularly important for AD simulations, and we believe that real-world complexity and diversity cannot be realistically captured in a virtual environment. Our augmented approach combines the flexibility of a virtual environment (e.g., vehicle movements) with the richness of the real world to allow effective simulation.
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Manawadu, Udara Eshan, Masaaki Ishikawa, Mitsuhiro Kamezaki, and Shigeki Sugano. "Analysis of Preference for Autonomous Driving Under Different Traffic Conditions Using a Driving Simulator." Journal of Robotics and Mechatronics 27, no. 6 (December 18, 2015): 660–70. http://dx.doi.org/10.20965/jrm.2015.p0660.

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<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/08.jpg"" width=""300"" /> Driving simulator</div>Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the preferences and individual driving experiences of novice and experienced drivers of autonomous and conventional vehicles under different traffic and road conditions. We first developed a simplified driving simulator that could connect to different driver-vehicle interfaces (DVI). We then created virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and we implemented fully autonomous driving. We then conducted experiments to clarify how the autonomous driving experience differed for the two groups. The results showed that experienced drivers opt for conventional driving overall, mainly due to the flexibility and driving pleasure it offers, while novices tend to prefer autonomous driving due to its inherent ease and safety. A further analysis indicated that drivers preferred to use both autonomous and conventional driving methods interchangeably, depending on the road and traffic conditions.
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Dissertations / Theses on the topic "Autonomus driving"

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Becci, Simone. "Feasibility, requirements and preliminary assessment of e-horizon driven speed targeting algorithms, for enhanced Adaptive Cruise Control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23435/.

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According to the report from the EU,every year more than 40 000 lives are lost in a road accident. More than 90% of accidents are caused by human error. Advance Driver-Assistance Systems (ADAS) can help drivers to avoid these accidents or at least to reduce the consequence. Such systems use a multitude of sensors to perceive the environment around the vehicle,and act consequently. In the future, cars will be a part of an intelligent transport system where all players are connected. With these abilities, an electronic horizon is created pushing forward the sensor range. Information regarding traffic or hazard can be obtained from other cars or from the cloud. With the use of ADAS, the driver can be assisted to avoid dangerous situations. In a near future, all this information will be elaborated and used from the vehicle itself, reducing the possibility of human error. The thesis is a study on how and if it is possible to create an enhanced Adaptive Cruise Control (ACC) function relying on e-Horizon data. Standard ACC use a radar to keep distance from the car in front, by accelerating up to a preset speed or braking. The goal is to research if curvature can be taken into consideration to create a more comfortable and safe speed profile. To do so, the onboard function obtains curvature information from different data sources. The first one, is the navigation data, provided in real time to the car through Vehicle to Cloud connection. The second one, is an onboard camera used for ADAS functions, which outputs the curvature. The recording from a GPS tracker is used to create an ideal path description, to be used as benchmark. Geographic coordinates from map service provider are elaborated, with the use of Simulink models and MATLAB to obtain a curvature profile. This will be used in the test environment to create and actuate the speed profile. The parameters, used to evaluate the performance of the speed profile, are lateral and longitudinal accelerations and jerk.
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Edvardsson, Felicia, and Therése Warberg. "Konceptuell utveckling av interiören hos en framtida fullt autonom bil." Thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12794.

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Målet med examensarbetet har varit att samla information åt ett tekniskt konsultföretag för att öka deras kunskap om autonoma system och fordonskommunikation. Statusen på arbetet kring dessa aktiva säkerhetssystem hos olika aktörer och hur systemen implementeras i dagens och framtidens fordon har undersökts genom omfattande litteraturstudier, intervjuer och marknadsanalyser. De autonoma systemen kan samla information från omgivningen genom sensorer och bidra till ett jämnare trafikflöde, ökad säkerhet, lättare bilar och bättre miljö. Genom fordonskommunikationen kan fordon kommunicera med varandra samt infrastrukturen och garantera en säker bilfärd. År 2030 utgörs innerstaden av autonom, elektrifierad kollektivtrafik för att transportera människor på begäran, samtidigt som personbilar till viss del förbjuds. Potentiella behov för människan i en fullt autonom bil har identifierats och diverse produktutvecklingsmetoder har tillämpats för att utforma två konceptuella lösningar för en framtida bilinteriör. Lösningarna visar interaktionen mellan människa och system eftersom underhållning och bekvämlighet blir viktigt i en fullt autonom bil. Respektive lösning är statsägd och rymmer fyra passagerare. I lösningarna är sittplatserna placerade på ett sätt som underlättar kommunikation mellan passagerarna. Passagerarna kan underhållas eller informeras individuellt eller gemensamt via text, ljud och bild.
The goal with this thesis project has been to collect information for a technical consulting company in order to increase their knowledge about autonomous systems and vehicular communication. The status of how various operators work with active safety systems and how the systems are implemented in current and future vehicles has been investigated through extensive literature studies, interviews and market research. The autonomous systems can collect information from the surrounding through sensors and contribute to better traffic efficiency, increased safety, lighter cars and a better environment. Through vehicle communication, the vehicle can communicate with each other in order to guarantee a safe ride. In 2030 the inner city constitutes of autonomous, electrified public transport to transport people on demand, meanwhile private cars are prohibited. Potential needs for the human in a fully, autonomous car has been identified and various product development methods has been applied in order to develop two conceptual solutions for a future car interior. The solutions show the interaction between human and system since entertainment and comfort becomes important in a fully, autonomous car. Each solution is state-owned and holds four passengers. In the solutions, the seats are placed in regard to facilitate communication between the passengers. The passengers can be entertained or informed individually or collectively by text, sound and images.
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Espada, Yoan. "Localisation visuelle par approche neuromimétique dans le contexte de la délégation de conduite." Thesis, CY Cergy Paris Université, 2020. http://www.theses.fr/2020CYUN1070.

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L'objectif premier de cette thèse est de tester et d'étendre des modèles de navigation initialement développés pour de la navigation en intérieur et de les appliquer à la délégation de conduite des véhicules autonomes. La particularité de ces architectures de contrôle pour robot mobile est qu'elles sont issus d'une approche bio-inspirée et sont dite neurorobotiques : elles reproduisent les mécanismes neuronaux impliqués dans les comportements de navigation observés chez les animaux.Ces travaux concourent à introduire auprès de la communauté du véhicule autonome les approches bio-inspirées et leur potentiel au travers des modèles présentés.Cette thèse commence par aborder les technologies utilisées (GPS, lidar, ...) et les différentes stratégies de navigation (réactive, avec carte). Cette présentation se termine en circonscrivant le problème de la navigation des véhicules autonomes à la reconnaissance de lieux par la vision.Le manuscrit présente ensuite les approches bio-inspirée et neurorobotique avant de donner les notions d'anatomie facilitant la compréhension des modèles bio-inspirés décrits par la suite.Un modèle de reconnaissance de lieux par la vision est présenté en détail. Des expériences de simulations avec des données réelles permettent de tester ce modèle, initialement conçu pour la navigation intérieure, dans des environnements propres aux véhicules. Les résultats de ces tests montrent que le modèle construit une représentation de l'environnement capable de discriminer et de reconnaître des lieux.Une architecture neuronale de navigation réactive reposant sur le précédent système, le modèle PerAc est décrite ensuite. Ce modèle utilise les lieux construits par le modèle de reconnaissance et associe chaque lieu à une direction à suivre. Par un ensemble d'associations, il est possible d'apprendre à suivre de manière robuste des trajectoires ou une position à rejoindre. Ce modèle, conçu pour de la navigation en intérieur, est testé sur un véhicule dans des expériences servant à évaluer sa capacité à fonctionner sur véhicule. Les résultats prouvent que le modèle est capable d'apprendre des trajectoires avec une voiture en environnement extérieur.Ma dernière contribution porte sur un modèle de contextes visuels permettant d'accroître les performances computationnelles de la reconnaissance de lieux. Une chaîne de traitements visuelle inspirée du cortex visuel primaire effectue un découpage de l'environnement qui vient moduler la reconnaissance de lieux du modèle précédent.Ce manuscrit se termine en proposant un ensemble de pistes à même d'améliorer les différents aspects des modèles présentés, par exemple, en modélisant plus finement certaines zones du cerveau ou en utilisant un codage épars afin de réduire le coût computationnel
The main aim of this thesis is to extend navigation models built for indoor environments to autonomous vehicles.These models follow a neurorobotics approach: they reproduce neural substrates that sustain the mechanism responsible for spatial cognition in the mammalian brain. This thesis introduces and shows the potential of bio-inspired robotics and neurorobotics to the community of autonomous driving.This manuscrit begins by giving a state of the art of robotic navigation. It presents the subsidiary question of visual recognition.The notions of bio-inspired robotics and neurorobotics are then introduced. An overview of anatomical observations necessary to the comprehension of bio-inspired models is also given.The document describes a model for visual place recognition inspired by hippocampal place cells. This model is tested in outdoor on-road environments. The results show that the model is able to build a representation of the environment that can be used in navigation tasks.A reactive navigation model (PerAc) is then presented. This model uses the representation built by the visual place recognition model to learn sensorimotor units linking places and actions. A set of these associations allows the model to learn robust trajectories or goal positions. This model was initially built for robots evolving in indoor environments. It is embbedded and tested on an autonomous vehicle. The experiments show that the model is able to learn trajectory with a car in outdoor environments.My last contribution presented is about a visual contexts model. A second visual processing chain inspired by the primary visual cortex is proposed to build a coarse representation of the environment that modulates the activities of the visual place cells.The essay ends by giving a set of leads to improve different aspects of the proposed models, by modeling in more depth other brain structures or change the encoding of data for instance
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Tirumaladasu, Sai Subhakar, and Shirdi Manjunath Adigarla. "Autonomous Driving: Traffic Sign Classification." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17783.

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Autonomous Driving and Advance Driver Assistance Systems (ADAS) are revolutionizing the way we drive and the future of mobility. Among ADAS, Traffic Sign Classification is an important technique which assists the driver to easily interpret traffic signs on the road. In this thesis, we used the powerful combination of Image Processing and Deep Learning to pre-process and classify the traffic signs. Recent studies in Deep Learning show us how good a Convolutional Neural Network (CNN) is for image classification and there are several state-of-the-art models with classification accuracies over 99 % existing out there. This shaped our thesis to focus more on tackling the current challenges and some open-research cases. We focussed more on performance tuning by modifying the existing architectures with a trade-off between computations and accuracies. Our research areas include enhancement in low light/noisy conditions by adding Recurrent Neural Network (RNN) connections, and contribution to a universal-regional dataset with Generative Adversarial Networks (GANs). The results obtained on the test data are comparable to the state-of-the-art models and we reached accuracies above 98% after performance evaluation in different frameworks
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Ávila, Emanuel da Silva. "Servo-pilot for autonomous driving." Master's thesis, Universidade de Aveiro, 2010. http://hdl.handle.net/10773/2537.

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Mestrado em Engenharia Mecânica
Foram simulados numericamente jogos de recursos públicos em redes usando algoritmo de Monte Carlo. Foram usadas redes regulares unidimensionais em anel, redes regulares bidimensionais (rede quadrada) e redes scale-free. São apresentados os métodos seguidos, a teoria e os algoritmos usados. Estes jogos apresentam uma transição de fase entre uma fase dominada por oportunistas de uma fase dominada por cooperadores em função de um parâmetro de rendimento das contribuições. Foi encontrado um intervalo, dependente do número médio de vizinhos, para o qual a fracção de configurações sobreviventes tende para 1 quando o tamanho da rede aumenta. Foi também encontrada uma dependência no valor de parâmetro crítico de transição no número médio de vizinhos para as configurações sobreviventes. Esses efeitos foram observados em todos os tipos de rede estudados neste trabalho. ABSTRACT: Public goods games were numerically simulated in networks using Monte Carlo Algorithm. Regular one-dimensional ring networks, regular two-dimensional lattice networks and scale-free networks had been used. The methods followed, the theory and the algorithms used are presented. This games have a phase transition between one phase dominated by defectors from one dominated by cooperators in function of the value of efficiency from the contributions. It was found an interval, dependent on the average number of neighbors, where the fraction of surviving configurations tens to 1 when the size of the network increases. It was found dependence in the critical value of transition value with the average number of neighbors. Both effects were observed in all types of networks studied in this work.
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Sportillo, Daniele. "Préparation à la conduite automatisée en Réalité Mixte." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEM017/document.

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L'automatisation de la conduite est un processus en cours qui est en train de changer radicalement la façon dont les gens voyagent et passent du temps dans leur voiture pendant leurs déplacements. Les véhicules conditionnellement automatisés libèrent les conducteurs humains de la surveillance et de la supervision du système et de l'environnement de conduite, leur permettant d'effectuer des activités secondaires pendant la conduite, mais requièrent qu’ils puissent reprendre la tâche de conduite si nécessaire. Pour les conducteurs, il est essentiel de comprendre les capacités et les limites du système, d’en reconnaître les notifications et d'interagir de manière adéquate avec le véhicule pour assurer leur propre sécurité et celle des autres usagers de la route. À cause de la diversité des situations de conduite que le conducteur peut rencontrer, les programmes traditionnels de formation peuvent ne pas être suffisants pour assurer une compréhension efficace de l'interaction entre le conducteur humain et le véhicule pendant les transitions de contrôle. Il est donc nécessaire de permettre aux conducteurs de vivre ces situations avant leur première utilisation du véhicule. Dans ce contexte, la Réalité Mixte constitue un outil d'apprentissage et d'évaluation des compétences potentiellement efficace qui permettrait aux conducteurs de se familiariser avec le véhicule automatisé et d'interagir avec le nouvel équipement dans un environnement sans risque. Si jusqu'à il y a quelques années, les plates-formes de Réalité Mixte étaient destinées à un public de niche, la démocratisation et la diffusion à grande échelle des dispositifs immersifs ont rendu leur adoption plus accessible en termes de coût, de facilité de mise en œuvre et de configuration. L'objectif de cette thèse est d'étudier le rôle de la réalité mixte dans l'acquisition de compétences pour l'interaction d'un conducteur avec un véhicule conditionnellement automatisé. En particulier, nous avons exploré le rôle de l'immersion dans le continuum de la réalité mixte en étudiant différentes combinaisons d'espaces de visualisation et de manipulation et la correspondance entre le monde virtuel et le monde réel. Du fait des contraintes industrielles, nous avons limité les candidats possibles à des systèmes légers portables, peu chers et facilement accessibles; et avons analysé l’impact des incohérences sensorimotrices que ces systèmes peuvent provoquer sur la réalisation des activités dans l’environnement virtuel. À partir de ces analyses, nous avons conçu un programme de formation visant l'acquisition des compétences, des règles et des connaissances nécessaires à l'utilisation d'un véhicule conditionnellement automatisé. Nous avons proposé des scénarios routiers simulés de plus en plus complexes pour permettre aux apprenants d’interagir avec ce type de véhicules dans différentes situations de conduite. Des études expérimentales ont été menées afin de déterminer l'impact de l'immersion sur l'apprentissage, la pertinence du programme de formation conçu et, à plus grande échelle, de valider l'efficacité de l'ensemble des plateformes de formation par des mesures subjectives et objectives. Le transfert de compétences de l'environnement de formation à la situation réelle a été évalué par des essais sur simulateurs de conduite haut de gamme et sur des véhicules réels sur la voie publique
Driving automation is an ongoing process that is radically changing how people travel and spend time in their cars during journeys. Conditionally automated vehicles free human drivers from the monitoring and supervision of the system and driving environment, allowing them to perform secondary activities during automated driving, but requiring them to resume the driving task if necessary. For the drivers, understanding the system’s capabilities and limits, recognizing the system’s notifications, and interacting with the vehicle in the appropriate way is crucial to ensuring their own safety and that of other road users. Because of the variety of unfamiliar driving situations that the driver may encounter, traditional handover and training programs may not be sufficient to ensure an effective understanding of the interaction between the human driver and the vehicle during transitions of control. Thus, there is the need to let drivers experience these situations before their first ride. In this context, Mixed Reality provides potentially valuable learning and skill assessment tools which would allow drivers to familiarize themselves with the automated vehicle and interact with the novel equipment involved in a risk-free environment. If until a few years ago these platforms were destined to a niche audience, the democratization and the large-scale spread of immersive devices since then has made their adoption more accessible in terms of cost, ease of implementation, and setup. The objective of this thesis is to investigate the role of Mixed Reality in the acquisition of competences needed for a driver’s interaction with a conditionally automated vehicle. In particular, we explored the role of immersion along the Mixed Reality continuum by investigating different combinations of visualization and manipulation spaces and the correspondence between the virtual and the real world. For industrial constraints, we restricted the possible candidates to light systems that are portable, cost-effective and accessible; we thus analyzed the impact of the sensorimotor incoherences that these systems may cause on the execution of tasks in the virtual environment. Starting from these analyses, we designed a training program aimed at the acquisition of skills, rules and knowledge necessary to operate a conditionally automated vehicle. In addition, we proposed simulated road scenarios with increasing complexity to suggest what it feels like to be a driver at this level of automation in different driving situations. Experimental user studies were conducted in order to determine the impact of immersion on learning and the pertinence of the designed training program and, on a larger scale, to validate the effectiveness of the entire training platform with self-reported and objective measures. Furthermore, the transfer of skills from the training environment to the real situation was assessed with test drives using both high-end driving simulators and actual vehicles on public roads
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Olsson, Magnus. "Behavior Trees for decision-making in Autonomous Driving." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183060.

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This degree project investigates the suitability of using Behavior Trees (BT) as an architecture for the behavioral layer in autonomous driving. BTs originate from video game development but have received attention in robotics research the past couple of years. This project also includes implementation of a simulated traffic environment using the Unity3D engine, where the use of BTs is evaluated and compared to an implementation using finite-state machines (FSM). After the initial implementation, the simulation along with the control architectures were extended with additional behaviors in four steps. The different versions were evaluated using software maintainability metrics (Cyclomatic complexity and Maintainability index) in order to extrapolate and reason about more complex implementations as would be required in a real autonomous vehicle. It is concluded that as the AI requirements scale and grow more complex, the BTs likely become substantially more maintainable than FSMs and hence may prove a viable alternative for autonomous driving.
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Hernández, Juárez Daniel. "Embedded 3D Reconstruction for Autonomous Driving." Doctoral thesis, Universitat Autònoma de Barcelona, 2020. http://hdl.handle.net/10803/671166.

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L’objectiu d’aquesta tesi és estudiar algoritmes de reconstrucció 3D adequats per a la conducció autònoma. Per fer-ho, necessitem implementacions i representacions ràpides de l’entorn 3D que tinguin en compte la informació geomètrica i semàntica. L’ús de paral·lelització CUDA i GPU permet aprofitar maquinari flexible i programable d’alt rendiment per complir els requisits de temps exigents. La tesi presenta tres contribucions principals. En primer lloc, descrivim la paral·lelització del conegut algorisme d’estèreo basat en el Semi-Global Matching (SGM), que estima la profunditat a partir de dues imatges estèreo. Desplegem un disseny de paral·lelització eficient que funciona a les GPU de baix consum energètic i aconsegueix un rendiment en temps real. Com a segona contribució, presentem una millora del model de representació 3D anomenat Stixel World, que representa les superfícies inclinades. L’extensió del model ajuda a representar escenes reals que fallen sota els supòsits anteriors i, mitjançant una regularització eficient del model, manté la mateixa precisió que el model anterior. També proposem una estratègia algorítmica per accelerar el procés, que redueix la quantitat de combinacions Stixel provades. Finalment, expliquem les nostres estratègies de paral·lelització per a l’algorisme de segmentació de Stixel. Proposem una estratègia de paral·lelització que s’adapti a l’arquitectura massivament paral·lela de les GPU. També estudiem les diferents tècniques d’acceleració disponibles per a Stixels i com es poden implementar de manera eficient per a aquesta arquitectura. A més, el nostre enfocament redueix la complexitat computacional de l’algorisme mitjançant la reformulació del model.
El objetivo de esta tesis es estudiar algoritmos de reconstrucción 3D aptos para la conducción autónoma. Para ello, necesitamos implementaciones y representaciones rápidas del entorno 3D que tengan en cuenta la información geométrica y semántica. El uso de la paralelización de CUDA y GPU permite aprovechar el hardware de alto rendimiento flexible y programable para cumplir con los estrictos requisitos de tiempo. La tesis presenta tres contribuciones principales. Primero, describimos la paralelización del conocido algoritmo de estéreo basado en Semi-Global Matching (SGM), que estima la profundidad de dos imágenes estéreo. Implementamos un diseño de paralelización eficiente que se ejecuta sobre GPU de bajo consumo de energía y logra un rendimiento en tiempo real. Como segunda contribución, presentamos una mejora del modelo de representación 3D llamado Stixel World que da cuenta de las superficies inclinadas. La extensión del modelo ayuda a representar escenas reales que fallan bajo los supuestos anteriores y, mediante una regularización eficiente del modelo, mantiene la misma precisión del modelo anterior. También proponemos una estrategia algorítmica para acelerar el proceso, lo que reduce la cantidad de combinaciones de Stixel probadas. Finalmente, explicamos nuestras estrategias de paralelización para el algoritmo de segmentación de Stixel. Proponemos una estrategia de paralelización que se adapta a la arquitectura masivamente paralela de las GPU. También estudiamos las diferentes técnicas de aceleración disponibles para Stixels y cómo se pueden implementar de manera eficiente para esta arquitectura. Además, nuestro enfoque reduce la complejidad computacional del algoritmo al reformular el modelo.
The objective of this thesis is to study 3D reconstruction algorithms suitable for autonomous driving. In order to do so, we need fast implementations and representations of the 3D environment that take into account geometric and semantic information. The use of CUDA and GPU parallelization allows to leverage flexible and programmable high performance hardware to fulfill the strong time requirements. The thesis presents three main contributions. First, we describe the parallelization of the well-known stereo matching algorithm based on Semi-Global Matching (SGM), which estimates depth from two stereo images. We deploy an efficient parallelization design that runs on top of low-energy consumption GPUs and achieves real-time performance. As our second contribution, we present an improvement of the 3D representation model called the Stixel World that accounts for slanted surfaces. The extension of the model helps representing real scenes that fail under the previous assumptions, and, by an efficient model regularization, keeps the same accuracy of the previous model. We also propose an algorithmic strategy to speed up the process, which reduces the amount of Stixel combinations tested by the dynamic programming approach. Finally, we explain our parallelization strategies for the Stixel segmentation algorithm. We propose a parallelization strategy that fits the massively parallel architecture of GPUs. We also study the different speed up techniques available for Stixels and how they can be implemented efficiently for this architecture. Additionally, our approach reduces the computational complexity of the algorithm by reformulating the measurement depth model, relying on the confidence of the depth estimation and the identification of invalid values to handle outliers.
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Zivkovic, A. (Aleksandar). "Development of autonomous driving using ROS." Master's thesis, University of Oulu, 2018. http://urn.fi/URN:NBN:fi:oulu-201806062488.

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Autonomous driving, or self-driving, is the ability of a vehicle to drive itself without human input. To achieve this, the vehicle uses mechanical and electronic parts, sensors, actuators and an on-board computer. The on-board computer runs sophisticated software which allows the vehicle to perceive and understand its environment based on sensor input, localise itself in that environment and plan the optimal route from point A to point B. Autonomous driving is no longer a thing of the future, and to develop autonomous driving solutions is a highly valuable skill in today’s software engineering field. Robot Operating System (ROS) is a meta-operating system that simplifies the process of robotics programming. This master’s thesis aims to demonstrate how ROS could be used to develop autonomous driving software by analysing autonomous driving problems, examining existing solutions and developing a prototype vehicle using ROS. This thesis provides an overview of autonomous driving and usage of ROS in the development of autonomous driving, then elaborates on the benefits and challenges of using ROS for autonomous car development. The research methods used in this master’s thesis are design science research (DSR) and a literature review. An artefact is developed and evaluated—a remote-controlled (RC) car equipped with Raspberry Pi 3 board as the on-board computer, an Arduino Uno board, Teensy LC board, a set of sensors and ROS-based software. The thesis is supported by the author’s employer, automotive software company called Elektrobit. By following the steps described in this thesis, it is possible to develop an autonomous driving RC car which runs on ROS. Additionally, this thesis shows why ROS provides good solutions for the autonomous driving issues. It points to the benefits of ROS: open sourced, peer-to-peer, network-based meta-operating system with ready-made components for autonomous driving, and highlights some of the challenges of ROS: security issues and single point of failure.
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Langner, Tobias [Verfasser]. "Visual Perception for Autonomous Driving / Tobias Langner." Berlin : Freie Universität Berlin, 2020. http://d-nb.info/1205735518/34.

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Books on the topic "Autonomus driving"

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Maurer, Markus, J. Christian Gerdes, Barbara Lenz, and Hermann Winner, eds. Autonomous Driving. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8.

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Chai, Zhanxiang, Tianxin Nie, and Jan Becker. Autonomous Driving Changes the Future. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6728-5.

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Langheim, Jochen, ed. Energy Consumption and Autonomous Driving. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-19818-7.

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Jurgen, Ronald K. Autonomous Vehicles for Safer Driving. Warrendale, PA: SAE International, 2013. http://dx.doi.org/10.4271/pt-158.

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Autonomous vehicles for safer driving. Warrendale, Pennsylvania, USA: SAE International, 2013.

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Jurgen, Ronald K., ed. Autonomous Vehicles for Safer Driving. Warrendale, PA: SAE International, 2013. http://dx.doi.org/10.4271/0768080398.

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Gamba, Jonah. Radar Signal Processing for Autonomous Driving. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9193-4.

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Gupta, Nishu, Arun Prakash, and Rajeev Tripathi, eds. Internet of Vehicles and its Applications in Autonomous Driving. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-46335-9.

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Marsico, Katie. Self-driving cars. New York, NY: Children's Press, 2016.

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Waschl, Harald, Ilya Kolmanovsky, and Frank Willems, eds. Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91569-2.

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Book chapters on the topic "Autonomus driving"

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Lin, Patrick. "Why Ethics Matters for Autonomous Cars." In Autonomous Driving, 69–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_4.

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Winkle, Thomas. "Safety Benefits of Automated Vehicles: Extended Findings from Accident Research for Development, Validation and Testing." In Autonomous Driving, 335–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_17.

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Flämig, Heike. "Autonomous Vehicles and Autonomous Driving in Freight Transport." In Autonomous Driving, 365–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_18.

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Kröger, Fabian. "Automated Driving in Its Social, Historical and Cultural Contexts." In Autonomous Driving, 41–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_3.

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Maurer, Markus. "Introduction." In Autonomous Driving, 1–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_1.

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Beiker, Sven. "Deployment Scenarios for Vehicles with Higher-Order Automation." In Autonomous Driving, 193–211. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_10.

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Heinrichs, Dirk. "Autonomous Driving and Urban Land Use." In Autonomous Driving, 213–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_11.

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Cyganski, Rita. "Automated Vehicles and Automated Driving from a Demand Modeling Perspective." In Autonomous Driving, 233–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_12.

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Winner, Hermann, and Walther Wachenfeld. "Effects of Autonomous Driving on the Vehicle Concept." In Autonomous Driving, 255–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_13.

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Beiker, Sven. "Implementation of an Automated Mobility-on-Demand System." In Autonomous Driving, 277–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_14.

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Conference papers on the topic "Autonomus driving"

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Binghong, Gan. "Automotive Radar Technology & Test Solution for Autonomus Driving." In 2019 IEEE MTT-S International Wireless Symposium (IWS). IEEE, 2019. http://dx.doi.org/10.1109/ieee-iws.2019.8803901.

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Bahati, Raphael M., and Michael A. Bauer. "Adapting to Run-Time Changes in Policies Driving Autonomic Management." In 2008 Fourth International Conference on Autonomic and Autonomous Systems (ICAS). IEEE, 2008. http://dx.doi.org/10.1109/icas.2008.47.

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Zöbel, Dieter, and Christian Weyand. "An Integrated Software Concept for Autonomous Driving in the Context of Intermodal Freight Traffic." In 2010 Sixth International Conference on Autonomic and Autonomous Systems (ICAS). IEEE, 2010. http://dx.doi.org/10.1109/icas.2010.25.

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Victor, Trent. "The Role of Attention in Increasingly Autonomous Driving." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2015. http://dx.doi.org/10.17077/drivingassessment.1573.

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Bahati, Raphael M., and Michael A. Bauer. "An Adaptive Reinforcement Learning Approach to Policy-Driven Autonomic Management." In 2009 Fifth International Conference on Autonomic and Autonomous Systems. IEEE, 2009. http://dx.doi.org/10.1109/icas.2009.58.

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Fernandes, Lúcia Abrunhosa, Beatriz Helena Neto, Vladimir Fagundes, Geraldo Zimbrão, Jano Moreira de Souza, and Rodrigo Salvador. "Model-Driven Architecture Approach for Data Warehouse." In 2010 Sixth International Conference on Autonomic and Autonomous Systems (ICAS). IEEE, 2010. http://dx.doi.org/10.1109/icas.2010.37.

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Bahati, Raphael M., Michael A. Bauer, and Elvis M. Vieira. "Adaptation Strategies in Policy-Driven Autonomic Management." In Third International Conference on Autonomic and Autonomous Systems (ICAS'07). IEEE, 2007. http://dx.doi.org/10.1109/conielecomp.2007.49.

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Walch, Marcel, Kristin Lange, Martin Baumann, and Michael Weber. "Autonomous driving." In AutomotiveUI '15: The 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2799250.2799268.

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Tobias, Crystal, Chen-Yung Su, Lutz Kolburg, and Brian Lathrop. "Cocktail Party Effect& Attention Capture in Semi-Autonomous Driving." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2013. http://dx.doi.org/10.17077/drivingassessment.1528.

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Barnard, Melody, Robert Hitt, Michael Norton, and Yi-Ching Lee. "Posing Questions and Policy Suggestions: Autonomous Vehicles & Climate Change." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2019. http://dx.doi.org/10.17077/drivingassessment.1711.

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Reports on the topic "Autonomus driving"

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Albus, James, John Evans, Craig Schlenoff, Tony Barbera, Elena Messina, and Stephen Balakirsky. Achieving intelligent performance in autonomous driving. Gaithersburg, MD: National Institute of Standards and Technology, 2003. http://dx.doi.org/10.6028/nist.ir.7166.

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Porcel Magnusson, Cristina. Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles. SAE International, January 2021. http://dx.doi.org/10.4271/epr2021002.

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Autonomous vehicles (AVs) utilize multiple devices, like high-resolution cameras and radar sensors, to interpret the driving environment and achieve full autonomy. One of these instruments—the light detection and ranging (LiDAR) sensor—utilizes pulsed infrared (IR) light, typically at wavelengths of 905 nm or 1,550 nm, to calculate object distance and position. Exterior automotive paint covers an area larger than any other exterior material. Therefore, understanding how LiDAR wavelengths interact with vehicle coatings is extremely important for the safety of future automated driving technologies. Sensing technologies and materials are two different industries that have not directly interacted in the perception and system sense. With the new applications in the AV industry, multidisciplinary approaches need to be taken to ensure reliability and safety in the future. Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles provides a transversal view of different industry segments, from pigment and coating manufacturers to LiDAR components and vehicle system development and integration. The report includes a structured decomposition of the different variables and technologies involved.
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Qin, Tong, Zhen Chen, John Jakeman, and Dongbin Xiu. Data-driven learning of non-autonomous systems. Office of Scientific and Technical Information (OSTI), June 2020. http://dx.doi.org/10.2172/1763550.

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Favarò, Francesca M. Impact of Smart Phones’ Interaction Modality on Driving Performance for Conventional and Autonomous Vehicles. Mineta Transportation Institute, January 2020. http://dx.doi.org/10.31979/mti.2020.1813.

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Rodier, Caroline. The Potential for Autonomous Vehicle Technologies to Address Barriers to Driving for Individuals with Autism. Mineta Transportation Institute, February 2020. http://dx.doi.org/10.31979/mti.2020.1706.

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Razdan, Rahul. Unsettled Topics Concerning Human and Autonomous Vehicle Interaction. SAE International, December 2020. http://dx.doi.org/10.4271/epr2020025.

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This report examines the current interaction points between humans and autonomous systems, with a particular focus on advanced driver assistance systems (ADAS), the requirements for human-machine interfaces as imposed by human perception, and finally, the progress being made to close the gap. Autonomous technology has the potential to benefit personal transportation, last-mile delivery, logistics, and many other mobility applications enormously. In many of these applications, the mobility infrastructure is a shared resource in which all the players must cooperate. In fact, the driving task has been described as a “tango” where we—as humans—cooperate naturally to enable a robust transportation system. Can autonomous systems participate in this tango? Does that even make sense? And if so, how do we make it happen?
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Balali, Vahid, Arash Tavakoli, and Arsalan Heydarian. A Multimodal Approach for Monitoring Driving Behavior and Emotions. Mineta Transportation Institute, July 2020. http://dx.doi.org/10.31979/mti.2020.1928.

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Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways.
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Al-Qadi, Imad, Egemen Okte, Aravind Ramakrishnan, Qingwen Zhou, and Watheq Sayeh. Truck Platooning on Flexible Pavements in Illinois. Illinois Center for Transportation, May 2021. http://dx.doi.org/10.36501/0197-9191/21-010.

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Truck platoons have many benefits over traditional truck mobility. Truck platoons have the potential to improve safety and reduce fuel consumption between 5% and 15%, based on platoon configuration. In Illinois, trucks carry more than 50% of freight tonnage and constitute 25% of the traffic on interstates. Therefore, expected fuel savings would be significant for trucks. Deployment of truck platoons within interstate highways may have a direct effect on flexible pavement performance, as the time between consecutive axle loads (i.e., resting time) is expected to decrease significantly. Moreover, platoons could potentially accelerate pavement damage accumulation due to trucks’ channelized position, decreasing pavement service life and increasing maintenance and rehabilitation costs. The main objective of this project was to quantify the effects of truck platoons on pavements and to provide guidelines to control corresponding potential pavement damage. Finite-element models were utilized to quantify the impact of rest period on pavement damage. Recovered and accumulated strains were predicted by fitting exponential functions to the calculated strain profiles. The results suggested that strain accumulation was negligible at a truck spacing greater that 10 ft. A new methodology to control pavement damage due to truck platoons was introduced. The method optimizes trucks’ lateral positions on the pavements, and an increase in pavement service life could be achieved if all platoons follow this optimization method. Life cycle assessment and life cycle cost analysis were conducted for fully autonomous, human-driven, and mixed-traffic regimes. For example, for an analysis period of 45 years, channelized truck platoons could save life cycle costs and environmental impacts by 28% and 21% compared with human-driven trucks, respectively. Furthermore, optimum truck platoon configuration could reduce life cycle costs and environmental impacts by 48% and 36%, respectively, compared with human-driven trucks. In contrast, channelized traffic could increase pavement roughness, increasing fuel consumption by 15%, even though platooning vehicles still benefit from reduction in air drag forces. Given that truck platoons are expected to be connected only in the first phase, no actions are required by the agency. However, in the second phase when truck platoons are also expected to be autonomous, a protocol for driving trends should be established per the recommendation of this study.
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Levine, Edward R. Multi-Scale Model-Driven Sampling with Autonomous Systems at a National Littoral Laboratory: Turbulence Characterization from an AUV. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada630605.

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Levine, Edward R. Renewal of Multi-Scale Model-Driven Sampling with Autonomous Systems at a National Littoral Laboratory: Turbulence Characterization with an AUV. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada625153.

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