Inspired by the idea of vision zero, there is a lot of work that needs to be done in the field of advance driver assistance systems to develop more safer systems. Driver intention detection with a prediction of upcoming behavior of the driver is one possible solution to reduce the fatalities in road traffic. Driver intention detection provides an early warning of the driver's behavior to an Advanced Driver Assistance Systems (ADAS) and at the same time reduces the risk of non-essential warnings. This will significantly reduce the problem of warning dilemma and the system will become more safer. A driving maneuver prediction can be regarded as an implementation of driver's behavior. So the aim of this thesis is to determine the driver's intention by early prediction of a driving maneuver using Controller Area Network (CAN) bus data. The focus of this thesis is to optimize and further develop an algorithm for driver intention detection with fuzzy logic and edit distance method. At first the basics concerning driver's intention detection are described as there exists different ways to determine it. This work basically uses CAN bus data to determine a driver's intention. The algorithm overview with the design parameters are described next to have an idea about the functioning of the algorithm. Then different implementation tasks are explained for optimization and further development of the algorithm. The main aim to execute these implementation tasks is to improve the overall performance of the algorithm concerning True Positive Rate (TPR), False Positive Rate (FPR) and earliness values. At the end, the results are validated to check the algorithm performance with different possibilities and a test drive is performed to evaluate the real time capability of the algorithm. Lastly the use of driver intention detection algorithm for an ADAS to make it more safer is described in details. The early warning information can be feed to an ADAS, for example, an automatic collision avoidance or a lane change assistance ADAS to further improve safety for these systems.

One of the most common causes of death is road accidents and in 95% of these accidents the human factor is involved. Therefore, Advanced-Driver-Assistance-Systems (ADAS) have been developed to support the drivers. To achieve the great potential that ADAS have, it is important that they are designed well for the drivers to trust, appreciate and use them. Previous research has investigated this, but mostly in the private car industry and where there is limited research within heavy vehicles. Therefore, the aim with this study was to investigate truck drivers’ attitudes towards ADAS, how they interacted with the systems and their thoughts about them in the future. This was done by interviewing and observing truck drivers in two different real-world environments, local and long haulage, which later were analysed with a thematic analysis. The results indicated a difference between the drivers’ attitudes towards ADAS, where the local drivers had a more overall appreciation to the systems than the long haulage drivers. It was also identified the drivers trusted the systems, but not completely. Furthermore, the feedback from the systems supported both groups of drivers but could in some situations be annoying to listen to. Regarding the interaction with ADAS, it was identified that the driving context affected the driver’s usage of the systems, but also the system performance. Lastly, the drivers were sceptical towards automation and self-driving trucks in the future where they also emphasized the importance of letting the drivers having the control over the vehicle. The results confirmed previous research to some extent, while other parts were contradictory. Therefore, more research has to be done to achieve a future safe transport system. / En av de vanligaste dödsorsakerna är trafikolyckor och i 95% av dessa olyckor är den mänskliga faktorn inblandad. Därför har Avancerade-Förar-Assistans-System (ADAS) utvecklats för att stödja förarna. För att uppnå den potential som dessa system har är det viktigt att de är utformade så att förarna kan lita, uppskatta och använda dem. Tidigare forskning har undersökt detta, men främst inom den privata bilindustrin och där det finns begränsad forskning inom tunga fordon. Syftet med denna studie var därför att undersöka lastbilschaufförers attityder mot ADAS, hur de interagerade med systemen samt deras tankar om dem i framtiden. Detta genomfördes genom intervjuer och observationer av lastbilschaufförer i två olika miljöer, lokala och långa transporter, som senare analyserades med hjälp av en tematisk analys. Resultaten indikerade på en skillnad i förarnas attityder till ADAS, där de lokala förarna generellt uppskattade systemen mer än fjärrförarna. Det identifierades också att förarna litade på systemen, men inte helt. Dessutom stöttade feedbacken från systemen båda grupperna av förare, men kunde i vissa situationer vara irriterande att lyssna på. Gällande interaktionen med ADAS identifierades det att körkontexten påverkade förarens användning av systemen, men också systemets prestanda. Slutligen var förarna skeptiska till automatisering och självkörande lastbilar i framtiden, där de även betonade vikten av att låta förarna ha kontrollen över fordonet. Resultaten både bekräftade och dementerade tidigare forskning. Därför måste mer forskning genomföras för att uppnå ett framtida säkert transportsystem.

With the growing number of vehicles on the road, improving traffic safety and efficiency has become a major challenge. One of the promising solutions is the implementation of Intelligent Transportation Systems (ITS), which utilizes communication technologies to facilitate real-time information sharing between vehicles and infrastructure. This research aims to contribute to the field of Intelligent Transportation Systems (ITS) improving traffic safety and efficiency achieving the Vision Zero goal. In this thesis, we focus on the performance analysis of two communication protocols used for two signal dissemination techniques for IVI (infrastructure-vehicle-information communication). Periodic, which sends signals at fixed intervals, and On-demand which sends signals upon request from road users. The results of our experiments showed that there are trade-offs between the periodic and on-demand approaches in V2I communications. The on-demand approach performs better in terms of PER (Packet error ratio), but it results in higher latency, even with low congestion. On the other hand, the periodic approach exhibits higher PDR (Packet delivery ratio) but suffers from inefficiencies due to the transmission of redundant messages resulting in higher PER. Our findings have implications for the scalability of V2I communications and can be translatable to other access technologies used in ITS.

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En raison de l'augmentation du volume et de la complexité des systèmes de transport, de nouveaux systèmes avancés d'assistance à la conduite (ADAS) sont étudiés dans de nombreuses entreprises, laboratoires et universités. Ces systèmes comprennent des algorithmes avec des techniques qui ont été étudiés au cours des dernières décennies, comme la localisation et cartographie simultanées (SLAM), détection d'obstacles, la vision stéréoscopique, etc. Grâce aux progrès de l'électronique, de la robotique et de plusieurs autres domaines, de nouveaux systèmes embarqués sont développés pour garantir la sécurité des utilisateurs de ces systèmes critiques. Pour la plupart de ces systèmes, une faible consommation d'énergie ainsi qu'une taille réduite sont nécessaires. Cela crée la contrainte d'exécuter les algorithmes sur les systèmes embarqués avec des ressources limitées. Dans la plupart des algorithmes, en particulier pour la vision par ordinateur, une grande quantité de données doivent être traitées à des fréquences élevées, ce qui exige des ressources informatiques importantes. Un FPGA satisfait cette exigence, son architecture parallèle combinée à sa faible consommation d'énergie et la souplesse pour les programmer permet de développer et d'exécuter des algorithmes plus efficacement que sur d'autres plateformes de traitement. Les composants virtuels développés dans cette thèse ont été utilisés dans trois différents projets: PICASSO (vision stéréoscopique), COMMROB (détection d'obstacles à partir d'une système multicaméra) et SART (Système d'Aide au Roulage tous Temps). / Due to the increase of traffic volume and complexity of new transport systems, new Advanced Driver Assistance Systems (ADAS) are a subject of research of many companies, laboratories and universities. These systems include algorithms with techniques that have been studied during the last decades like Simultaneous Lo- calization and Mapping (SLAM), obstacle detection, stereo vision, etc. Thanks to the advances in electronics, robotics and other domains, new embedded systems are being developed to guarantee the safety of the users of these critical systems. For most of these systems a low power consumption as well as reduced size is required. It creates the constraint of execute the algorithms in embedded devices with limited resources. In most of algorithms, moreover for computer vision ones, a big amount of data must be processed at high frequencies, this amount of data demands strong computing resources. FPGAs satisfy this requirement; its parallel architecture combined with its low power consumption and exibility allows developing and executing some algorithms more efficiently than any other processing platforms. In this thesis different embedded computer vision architectures intended to be used in ADAS using FPGAs are presented such as: We present the implementation of a distortion correction architecture operating at 100 Hz in two cameras simultaneously. The correction module allows also to rectify two images for implementation of stereo vision. Obstacle detection algorithms based on Inverse Perspective Mapping (IPM) and classiffication based on Color/Texture attributes are presented. The IPM transform is based in the perspective effect of a scene perceived from two different points of view. Moreover results of the detection algorithms from color/texture attributes applied on a multi-cameras system, are fused in an occupancy grid. An accelerator to apply homographies on images, is presented; this accelerator can be used for different applications like the generation of Bird's eye view or Side view. Multispectral vision is studied using both infrared images and color ones. Syn- thetic images are generated from information acquired from visible and infrared sources to provide a visual aid to the driver. Image enhancement specific for infrared images is also implemented and evaluated, based on the Contrast Lim- ited Adaptive Histogram Equalization (CLAHE). An embedded SLAM algorithm is presented with different hardware acceler- ators (point detection, landmark tracking, active search, correlation, matrix operations). All the algorithms were simulated, implemented and verified using as target FPGAs. The validation was done using development kits. A custom board integrating all the presented algorithms is presented. Virtual components developed in this thesis were used in three different projects: PICASSO (stereo vision), COMMROB (obstacle detection from a multi-cameras system) and SART (multispectral vision).

<p>Research and development on vehicle safety has lately started to direct its focus towards how to actively support the driver and make it easier for her to drive safely through letting Advanced Driver Assistance Systems (ADAS) have effect on how the driver interacts with the vehicle and the surrounding traffic. This requires research on both how the driver and vehicle perform in different situations, in terms of psychology, cognition and individual differences. In addition, physical limitations and requirements of the driver and the vehicle must be taken into account. Therefore scenarios for evaluation of these systems are required. In the area of user-centered design a rather new method, Personas, is being adopted. This thesis tries to explore if the Persona method is a viable tool for creating scenarios for such evaluations. Experiences after completing this work imply that personas indeed is a viable way to include aspects and raise issues concerning individual variability and situational context in ADAS scenarios.</p>

The transportation sector has been identified as one of many sources of today's energetic and environmental problems. With constantly increasing numbers of vehicles on the road, non-renewable fossil fuels are becoming scarce and expensive. In addition, due to the pollutant emissions of internal combustion engines, the transportation sector is a major producer of greenhouse gas emissions. To resolve these problems researcher are looking for technological solutions, such as more efficient components and alternative drive train technologies, on one hand. On the other hand, work is being done to ensure the most efficient utilization of available technological resources. Eco driving is one way to immediately reduce a driver's energy consumption. In this thesis the potential gains of eco driving for passenger vehicles will be discussed. The main objective of this work is to, first, identify and compare drive train specific, optimal vehicle operation. Secondly, the effect of real-life constraints on potential gains of eco driving is evaluated. In addition, an approach to integrate mathematical optimization algorithms in an advanced driver assist system for eco driving is proposed. Physical vehicle models are developed for three representative vehicles: the conventional, electric and power-split hybrid vehicle. Using real-life and standard drive cycles a baseline mission is defined by specifying trip and road constraint. Applying the dynamic programming algorithms the trajectory optimization problem is solved, minimizing energy consumption for the trip. The effect of traffic on potential gains of eco driving is discussed, considering a vehicle following situation. Integrating emission constraints in the optimization algorithm the environmental advantages of eco driving are discussed. Finally, the developed algorithms were integrated in a driver assist system. Experimental tests on a driving simulator were used to verify the effectiveness of the system, as well as driver acceptance.

Recently, autonomous vehicles have received worldwide attentions from academic research, automotive industry and the general public. In order to achieve a higher level of automation, one of the most fundamental requirements of autonomous vehicles is the capability to respond to internal and external changes in a safe, timely and appropriate manner. Situational awareness and decision making are two crucial enabling technologies for safe operation of autonomous vehicles. This thesis presents a solution for improving the automation level of autonomous vehicles in both situational awareness and decision making aspects by utilising additional domain knowledge such as constraints and influence on a moving object caused by environment and interaction between different moving objects. This includes two specific sub-systems, model based target tracking in environmental perception module and motion planning in path planning module. In the first part, a rigorous Bayesian framework is developed for pooling road constraint information and sensor measurement data of a ground vehicle to provide better situational awareness. Consequently, a new multiple targets tracking (MTT) strategy is proposed for solving target tracking problems with nonlinear dynamic systems and additional state constraints. Besides road constraint information, a vehicle movement is generally affected by its surrounding environment known as interaction information. A novel dynamic modelling approach is then proposed by considering the interaction information as virtual force which is constructed by involving the target state, desired dynamics and interaction information. The proposed modelling approach is then accommodated in the proposed MTT strategy for incorporating different types of domain knowledge in a comprehensive manner. In the second part, a new path planning strategy for autonomous vehicles operating in partially known dynamic environment is suggested. The proposed MTT technique is utilized to provide accurate on-board tracking information with associated level of uncertainty. Based on the tracking information, a path planning strategy is developed to generate collision free paths by not only predicting the future states of the moving objects but also taking into account the propagation of the associated estimation uncertainty within a given horizon. To cope with a dynamic and uncertain road environment, the strategy is implemented in a receding horizon fashion.