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.

When introducing autonomous functionality in personal vehicles the ability to control the quality of the ride is transferred from the driver to the vehicle control system. In this context, a reference method for quantifying ride quality may be a useful tool in the development and tuning process. This master’s thesis investigates whether general quantitative measures of ride quality can be of value in the tuning of motion controllers for autonomous vehicles. A set of tools is built for a specific case study, analysing a lateral path planning algorithm, based on a finite horizon linear quadratic tracking controller, and how its tuning affects ride quality performance. A graphical user interface is built, with functionality for frequency domainanalysis of the path planning algorithm, individually and in combination with the remaining lateral control system, as well as ride quality evaluation based on lateral acceleration data, from logged test runs and simulation results. In addition, a simulation environment for the lateral control system is modified to be used in combination with the evaluation tool. Results of the case study indicate a measurable difference in ride quality performance when comparing manual and autonomous driving with the current implementation. Attempts were made to improve ride quality by re-tuning the path planning algorithm but little or no improvement from the previous tuning was made. The work has recognized the potential of using ride quality measures in the development and tuning process for autonomous vehicles as well as devising a tuning strategy incorporating frequency analysis and ride quality evaluation through simulation for the lateral control system. To further increase ride quality performance via the path planning algorithm an altered controller structure, such as a frequency weighted linear quadratic controller is suggested.

Background: Nowadays, logistics service providers face several challenges which create an urge to rethink their strategy to improve their position within the market,decrease their costs and their environmental impact. At the same time theintroduction of autonomous driving potentially has an impact on logistics.Self-driving trucks can help logistics companies to tackle these challenges.However, the implementation of this technology could fundamentally alterthe competitive landscape. Hence, certain competitive advantages currentlyheld by logistics firms might lose their relevance in the future and need tobe adapted to maintain a strong market position. Purpose: The purpose of this study is to explore the perception of self-driving trucks within logistics and the impact on competitive advantages of logistics service providers. Thereby, this thesis will look at experts from Germany and Sweden and their opinion on future implications of self-driving trucks. Method: An inductive research approach is used to explore the topic. A multi-method research strategy is applied to gather data through qualitative semi-structured interviews with 17 participants. These were divided into five different case groups. To interpret the data a thematic analysis approach was chosen. Conclusion: The main contribution is a model representing the impact of autonomous driving on competitive advantages and the implications for the logistics industry. Findings are based on the perception of experts about autonomous driving, current resources and capabilities.

This work investigates the problem of transfer from simulation to the real world in the context of autonomous navigation. To this end, we first present a photo-realistic training and evaluation simulator (Sim4CV)* which enables several applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator features cars and unmanned aerial vehicles (UAVs) with a realistic physics simulation and diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning. Using the insights gained from aerial object tracking, we find that current object trackers are either too slow or inaccurate for online tracking from an UAV. In addition, we find that in particular background clutter, fast motion and occlusion are preventing fast trackers such as correlation filter (CF) trackers to perform better. To address this issue we propose a novel and general framework that can be applied to CF trackers in order incorporate context. As a result the learned filter is more robust to drift due to the aforementioned tracking challenges. We show that our framework can improve several CF trackers by a large margin while maintaining a very high frame rate. For the application of autonomous driving, we train a driving policy that drives very well in simulation. However, while our simulator is photo-realistic there still exists a virtual-reality gap. We show how this gap can be reduced via modularity and abstraction in the driving policy. More specifically, we split the driving task into several modules namely perception, driving policy and control. This simplifies the transfer significantly and we show how a driving policy that was only trained in simulation can be transferred to a robotic vehicle in the physical world directly. Lastly, we investigate the application of UAV racing which has emerged as a modern sport recently. We propose a controller fusion network (CFN) which allows fusing multiple imperfect controllers; the result is a navigation policy that outperforms each one of them. Further, we embed this CFN into a modular network architecture similar to the one for driving, in order to decouple perception and control. We use our photo-realistic simulation environment to demonstrate how navigation policies can be transferred to different environment conditions by this network modularity.

No description available.

Thanks to the major advancements in hardware and computational power, sensor technology, and artificial intelligence, the race for fully autonomous driving systems is heating up. With a countless number of challenging conditions and driving scenarios, researchers are tackling the most challenging problems in driverless cars. One of the most critical components is the perception module, which enables an autonomous vehicle to "see" and "understand" its surrounding environment. Given that modern vehicles can have large number of sensors and available data streams, this thesis presents a deep learning-based framework that leverages multimodal data – i.e. sensor fusion, to perform the task of 3D object detection and localization. We provide an extensive review of the advancements of deep learning-based methods in computer vision, specifically in 2D and 3D object detection tasks. We also study the progress of the literature in both single-sensor and multi-sensor data fusion techniques. Furthermore, we present an in-depth explanation of our proposed approach that performs sensor fusion using input streams from LiDAR and Camera sensors, aiming to simultaneously perform 2D, 3D, and Bird’s Eye View detection. Our experiments highlight the importance of learnable data fusion mechanisms and multi-task learning, the impact of different CNN design decisions, speed-accuracy tradeoffs, and ways to deal with overfitting in multi-sensor data fusion frameworks.

An IMU is a sensor with many differing use cases, it makes use of an accelerometer, gyroscope and sometimes a magnetometer. One of the biggest problems with IMU sensors is the effect vibrations can have on their data. The reason for this study is to find a solution to this problem by filtering the data. The tests for this study were conducted in cooperation with Husqvarna using two of their automowers. The tests were made by running the automowers across different surfaces and recording the IMU data. To find filters for the IMU data a comprehensive literature survey was conducted to find suitable methods to filter out vibrations. The two filters selected for further testing were the complementary filter and the LMS filter. When the tests had been run all the data was added to data sheets where it could be analyzed and have the filters added to the data. From the gathered data the data spikes were clearly visible and were more than enough to trigger the mower's emergency stop and need to be manually reset. The vibrations were too irregular to filter using the LMS filter since it requires a known signal to filter against. Hence only the complementary filter was implemented fully. With the complementary filter these vibrations can be minimized and brought well below the level required to trigger an emergency stop. With a high filter weight constant such as 0.98, the margin of error from vibrations can be brought down to +- 1 degrees as the lowest and +- 4,6 degrees as highest depending on the surface and automower under testing. The main advantage with using the complementary filter is that it only requires one weight constant to adjust the filter intensity making it easy to use. The one disadvantage is that the higher the weight constant is the more delay there is on the data.

The master's thesis deals with the design and realization of embedded control system and software of the autonomous mobile robot DACEP. The research section focuses on the selection of sensory equipment. Moreover, the design of the embedded control system and the communication interface between this system and the master PC is described in detail, followed by the design of localization and navigation software that uses ROS framework. The section is written as instructive as possible for the development of robots of similar construction. Finally the development of a graphical interface for robot diagnostics and remote control is depicted.

<div><div><div><p>Towing capacity affects a vehicle’s towing ability and it is usually costly to buy or even rent a vehicle that can tow certain amount of weight. A widely swaying towing trailer is one of the main causes for accidents that involves towing trailers. This study propose an affordable automated disconnected towing system (ADTS) that does not require physical connection between leading vehicle and the trailer vehicle by only using a computer vision system. The ADTS contains two main parts: a leading vehicle which can perform lane detection and a trailer vehicle which can automatically follow the leading vehicle by detecting the license plate of the leading vehicle. The trailer vehicle can adjust its speed according to the distance from the leading vehicle.</p></div></div></div>

Background. Machine learning technology is used daily in many aspects of computers. Neural network is a machine learning technique. The importance of carsthat are self driving has increased in recent years and the research about it has alsoincreased. Some cars that are produced today already have an autopilot feature inthem. Objectives. In this thesis the objective is to find how the training duration of aneural network changes, when the track becomes harder and harder. Methods. To achieve this a small game and a neural network was developed inUnity. The neural network receive input from 5 sensors and gave 2 outputs, an angleand a velocity. The neural network will be trained in the game on 4 different tracks,each with one more obstacle than the last one. The neural network will generatetwenty cars that will drive simultaneously to the end of the track. If a car collidewith a wall, it gets destroyed. When all cars are destroyed a new generation is generated based on the 2 cars that got destroyed last. Results. The first track was finished in 8,3 seconds, the second track in 28,9 secondsand that is a 248% increase from the first track. The third track was finished in 44,5seconds and that is a 54% increase from the second track. Fourth track were finishedin 39,9 seconds and that was a 10,3% decrease in time. Conclusions. In conclusion the training duration increased from track 1 to track2. from track 2 to track 3 and 4 the increase is presumed to be because the trackis larger and because of the speed output from the neural network. The trainingduration does change, but the change corresponds with the change of the length ofthe track.