Volvo Cars is working on reducing the frequency of small margin driving situation in order to reach Vision 2020; that no one should be seriously injured or killed in a new Volvo car by 2020. In this specific case, there is one main goal; to increase the usage of Adaptive Cruise Control (ACC). ACC helps the driver by setting a time interval to the vehicle ahead, adapting the vehicle’s speed according to the set time interval. When using ACC, drivers will decrease the risk of crashes related to visual distraction, where close following conditions leaves the driver with too little time to recover when a lead vehicle unexpectedly brakes. By using ACC one increases the time for action during a potential crash situation. The purpose of the thesis work is to develop and iteratively test an ACC in-vehicle usage feedback concept that is using the principles of nudging. Nudging is an idea that relates to subconsciously pushing humans in a desired behavioural direction. Also, focus was on finding the right method for evaluation using traditional Usability testing as the ground design outline method. This work is divided into three phases; Research phase, Development phase and Final Performing phase. The research phase provides the opportunity to get to know the users and their experience of using ACC. The development phase was divided into two areas; the application development and method development. The third phase was the final performance, where the developed method was applied on the final implemented concept. This work resulted in a nudging concept aiming to encourage the driver towards a more frequent ACC usage, and a developed method for measuring drivers’ behaviour and experience on the usability and satisfaction of the concept.

Genom att fusionera (sammanföra) data från olika sensorer kan ett resultat uppnås som ger mer än de enskilda sensorerna var för sig. Här utreds vilka sensorer och sensorfusionsmetoder som kan vara aktuella att använda för Scanias adaptiva farthållare (ACC). Den adaptiva farthållaren anpassar det egna fordonets hastighet inte bara till en förinställd hastighet utan även till framförvarande fordons hastighet. Scanias ACC-system använder idag en radar för måldetektering. Det finns ett antal algoritmer och arkitekturer som passar för sensorfusionssystem beroende på nivån hos sensordatan och användningsområdet. Minstakvadratmetoder kan användas då data ska matchas mot en fysisk modell, ofta med lågnivådata. När tillgänglig data är på den nivån att den används direkt för att fatta beslut kan sannolikhetsmetoder användas. Intelligent fusion består av kognitiva modeller som avser härma den mänskliga beslutsprocessen. Till detta passar data på hög nivå. Två lösningar, för två olika sensoruppsättningar, har tagits fram. Båda lösningarna är uppbyggda av bayesiska nätverk. Det första nätverket fusionerar data från den befintliga radarenheten med data från en kamera som detekterar vägmarkeringar. Resultaten visar att filbyten kan upptäckas tidigare i och med fusionen. Det andra nätverket använder sig av två radarenheter, den ursprungliga samt en likadan enhet till, vilket resulterar i ett bredare synfält. Nätverket avgör vilka mål hos respektive radar som kan anses vara samma matchade mål. Informationen kan användas för att öka redundansen i systemet samt för att upptäcka mål tidigare än förut. / By fusing data from different sensors a result can be achieved that is worth more than the data from each sensor by itself. Which sensors and sensor fusion methods that could be used in Scania's adaptive cruise control system (ACC) is investigated. The ACC system adapts the vehicle's speed not only to a driver decided set speed but also to the speed of preceding vehicles. Scania's ACC system uses a radar for target detection. There exists a number of algorithms and architectures fit for use in sensor fusion systems. Which one to use depends on the level of the data to be fused and on the field of application. Least square methods are used when matching data to a physical model, data to be used is often at a low level. When working with data at decision level, probability methods are favored. Another example is intelligent fusion, cognitive methods intending to mimic the human decision process. Suitable data is data at a high level. Two solutions, for two different sensor sets, are proposed. Both solutions are made out of Bayesian networks. The first one fuses data from the existing radar unit with data from a camera which detects lane markings. The results show that lane changes can be detected earlier thanks to the fusion. The second network uses two radar sensors of the same kind as the first, resulting in a wider field of view. The network decides which ones of each radars targets that are the same matching targets. This information could be used to increase the redundancy of the system and to detect targets earlier.

Current day VLSI systems have started seeing increasing percentages of multiple energy domain components being integrated into the mainstream. Energy domains such as mechanical, optical, fluidic etc. have become all pervasive into VLSI systems and such systems are being manufactured routinely. The framework required to design such an integrated system with diverse energy domains needs to be evolved as a part of conventional VLSI design methodology. This is because manufacturing and design of these integrated energy domains although based on semiconductor processing, is still very ad-hoc, with each device requiring its dedicated design tools and process integration. In this thesis three different approaches in different energy domains, have been pro-posed. These three domains include modelling & simulation, synthesis & compilation and formal verification. Three different scenarios have been considered and it is shown that these tasks can be co-performed along with conventional VLSI circuits and systems. In the first approach a micro-mechanical beam bending case is presented. A thermal heat ow causing the beam to bend through thermal stress is analyzed for change in capacitance under a single analysis and modelling framework. This involves a seamless analysis through thermal, mechanical and electrical energy domains. The second part of the thesis explores synthesis and compilation paradigms. The concept of a Gyro-compiler analogous to a memory compiler is proposed, which primarily generates soft IP models for various gyro topologies. The final part of this thesis deals in showcasing a working prototype of a formal verification framework for MEMS based hybrid systems. The MEMS verification domain today is largely limited to simulation based verification. Many techniques have been proposed for formal verification of hybrid systems. Some of these methods have been extended to demonstrate, how MEMS based hybrid systems can be formally verified through ex-tensions of conventional formal verification methods. An adaptive cruise control (ACC) system with a gyro based speed sensor has been analyzed and formally verified for various specifications of this system.

Objective: The Vision Zero initiative pursues the goal of eliminating all traffic fatalities and severe injuries. Today’s advanced driver assistance systems (ADAS) are an important part of the strategy toward Vision Zero. In Germany in 2018 more than 26,000 people were killed or severely injured by traffic accidents on motorways and rural roads due to road accidents. Focusing on collision avoidance, a simulative evaluation can be the key to estimating the performance of state-of-the-art ADAS and identifying resulting potentials for system improvements and future systems. This project deals with the effectiveness assessment of a combination of ADAS for longitudinal and lateral intervention based on German accident data. Considered systems are adaptive cruise control (ACC), autonomous emergency braking (AEB), and lane keeping support (LKS). Methods: As an approach for benefit estimation of ADAS, the method of prospective effectiveness assessment is applied. Using the software rateEFFECT, a closed-loop simulation is performed on accident scenario data from the German In-Depth Accident Study (GIDAS) precrash matrix (PCM). To enable projection of results, the simulative assessment is amended with detailed single case studies of all treated cases without PCM data. Results: Three categories among today’s accidents on German rural roads and motorways are reported in this study: Green, grey, and white spots. Green spots identify accidents that can be avoided by state-of-the-art ADAS ACC, AEB, and LKS. Grey spots contain scenarios that require minor system modifications, such as reducing the activation speed or increasing the steering torque. Scenarios in the white category cannot be addressed by state-of-the-art ADAS. Thus, which situations demand future systems are shown. The proportions of green, grey, and white spots are determined related to the considered data set and projected to the entire GIDAS. Conclusions: This article describes a systematic approach for assessing the effectiveness of ADAS using GIDAS PCM data to be able to project results to Germany. The closed-loop simulation run in rateEFFECT covers ACC, AEB, and LKS as well as relevant sensors for environment recognition and actuators for longitudinal and lateral vehicle control. Identification of green spots evaluates safety benefits of state-of-the-art level 0–2 functions as a baseline for further system improvements to address grey spots. Knowing which accidents could be avoided by standard ADAS helps focus the evolution of future driving functions on white spots and thus aim for Vision Zero.

Indiana University-Purdue University Indianapolis (IUPUI) / Automotive technology has grown from strength to strength in the recent years. The main focus of research in the near past and the immediate future are autonomous vehicles. Autonomous vehicles range from level 1 to level 5, depending on the percentage of machine intervention while driving. To make a smooth transition from human driving and machine intervention, the prediction of human driving behavior is critical. This thesis is a subset of driving behavior prediction. The objective of this thesis is to predict the possibility of crash and implement an appropriate active safety system to prevent the same. The prediction of crash requires data of transition between lanes, and speed ranges. This is achieved through a variation of hidden Markov model. With the crash prediction and analysis of the Markov models, the required ADAS system is activated. The above concept is divided into sections and an algorithm was developed. The algorithm is then scripted into MATLAB for simulation. The results of the simulation is recorded and analyzed to prove the idea.

No description available.

Modern cars have a multitude of driver assistance functions that aim to support the driver in his/her everyday driving. One part of this is the Adaptive Cruise Control (ACC) that aims to keep a driver-specified speed. However, this set speed might be perceived as too high for some curves and as a result the driver will interrupt the system and take control again. To avoid this, a Curve Speed Adaptation (CSA) system aims to adapt the speed for an upcoming curve. Such a system should aim to mimic a driver and take into consideration how the driver would behave if he/she were to drive. This work aims to find a set of so-called Driving Modes that can describe how drivers with different driving styles drive through curves with different road properties by analysing recorded manual driving. A nested clustering approach is tested to divide curves into groups based on the driving style they were driven at and their road properties. The results show that this approach is able to capture different driving behaviours through curves. The road type and speed limit of a curve seem thereby to have the main influence on the driving behaviour. Clustering curves first by their driving style followed by the road properties yields thereby the more distinguishable Driving Modes. However, further improvements of the clustering methods are necessary to improve the obtained Driving Modes. The results of this thesis can form the basis for the development of a Curve Speed Adaptation system that adjusts for both the individual driver as well as particular road properties to improve the driver's comfort. / Moderna bilar har en mängd förarstödsystemer som syftar till att stödja föraren i sin dagligakörning. En av dem är Adaptive Cruise Control (ACC) som syftar till att hålla en hastighetspecificerad av föraren. Men denna hastighet kan uppfattas som för hög för vissa kurvoroch som resultat tar föraren kontrollen igen själv. För att undvika detta ska ett Curve SpeedAdaptation (CSA) system anpassa hastigheten för en kommande kurva. Ett sådant systembör sträva efter att efterlikna en förare och ta hänsyn till hur föraren skulle köra själv. Dethär examensarbetet syftar till att hitta så kallade körlägen som kan beskriva hur förare medolika körstilar kör genom kurvor med olika omständigheter genom att analysera manuellakörningar. En nestad klustringsmetod testas för att dela upp kurvor i grupper baserat påkörstilen som de kördes på och deras vägegenskaper. Resultaten visar att denna metod kanfånga olika körningsbeteenden genom kurvor. Vägtypen och hastighetsbegränsningen fören kurva verkar därmed ha huvudinverkan på körbeteendet. Att dela kurvorna först efterderas körstil följt av vägegenskaper ger bättre körlägena. Men ytterligare förbättringar avklustringsmetoderna är nödvändig för att förbättra de erhållna körningsmetoderna. Resul-taten av detta examensarbetet kan utgöra grunden för utvecklingen av ett kurvhastighetsanpassningssystem som anpassar både för den enskilda föraren och speciella vägegenskaperför att förbättra förarens komfort.

The current development of autonomous vehiclesallow for several new applications to form and evolve. One ofthese are platooning, where several vehicles drive closely togetherwith automatic car following. The method of getting informationabout the other vehicles in a platoon can vary. One of thesemethods is using visual information from a camera. Having acamera on-board an autonomous vehicle has further potential, forexample for recognition of objects in the vehicle’s surroundings.This bachelor thesis uses small RC vehicles to test an example ofa vision based platooning system. The system is then evaluatedusing a step response, from which the stability of the systemis analyzed. Additionally, a previously developed communicationbased platooning system was tested in the same way and it’sstability compared. The main conclusion of this thesis is that it isfeasible to use a camera, ArUco marker and an Optimal VelocityRelative Velocity model to achieve a vision based platoon on asmall set of RC vehicles. / Forskningsframsteg inom området autonoma fordon möjliggör utveckling av ett flertal nya tillämpningar. En av dessa är platooning, som innebär att flera fordon kör nära varandra med automatisk farthållning. Metoden för att erhålla information om de andra fordonen i platoonen kan variera. En av dessa metoder är att använda visuell information från en kamera. Att ha en kamera ombord på ett autonomt fordon har stor potential, exempelvis för detektering av objekt i fordonets omgivning. Det här kandidatexamensarbetet använder små radiostyrda bilar för att testa ett exempel av ett kamerabaserat platooning-system. Systemet är sedan utvärderat med hjälp av ett stegsvar, från vilket stabiliteten av systemet är analyserat. Dessutom testas ett tidigare utvecklat kommunikationsbaserat platooning-system, hittills bara testat i simulering, på samma uppsättning bilar. Den huvudsakliga slutsatsen av detta arbete är att det är möjligt att använda en kamera, ArUco markör och en Optimal Velocity Relative Velocity modell för att uppnå kamerabaserad platoon med en liten uppsättning radiostyrda bilar. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

Cette thèse est consacrée à étudier les applications potentielles de véhicules autonomes et communications V2X pour construire les systèmes de transport intelligents. Premièrement, le comportement de caravane dans un environnement de véhicule connecté est étudié. Un algorithme de commande de caravane est conçu pour obtenir l'espacement sécuritaire ainsi que la conformité de la vitesse et de l'accélération. Deuxièmement, à plus grande échelle, les caravanes autour d'une intersection sont considérées. Le débit pendant une période de signal de trafic peut être amélioré en tirant profit de la capacité redondante de la route. Dans diverses contraintes, les véhicules peuvent choisir d'accélérer et rejoindre la caravane précédente ou à décélérer de déroger à l'actuel. Troisièmement, une intersection sans signalisation en VANET est considérée. Dans des conditions de faible trafic, les véhicules peuvent réguler leur vitesse avant d'arriver à l'intersection en fonction du temps d'occupation de la zone de conflit (TOZC) stocké au niveau du gestionnaire, afin qu'ils puissent traverser l'intersection sans collision ni arrêt. Le délai peut être réduit en conséquence. Enfin, un algorithme de gestion d'intersection autonome universelle, qui peut fonctionner même avec le trafic lourd, est développé. Le véhicule cherche à sécuriser les fenêtres entrant dans le TOZC. Ensuite, sur la base des fenêtres trouvées et le mouvement du véhicule qui précède, les trajectoires des véhicules peuvent être planifiées en utilisant une méthode de programmation dynamique segmentée. Tous les algorithmes conçus sont testés et vérifiés avec succès par des simulations dans scénarios différents / This thesis is devoted to study the potential applications of autonomous vehicles and V2X communications to construct the intelligent transportation systems. Firstly, the behavior of platoon in connected vehicle environment is studied. A platoon control algorithm is designed to obtain safe spacing as well as accordance of velocity and acceleration for vehicles in the same lane. Secondly, in larger scale, the platoons around an intersection are considered. The throughput in a traffic signal period can be improved by taking advantage of the redundant road capacity. Within diverse constraints, vehicles can choose to accelerate to join in the preceding platoon or to decelerate to depart from the current one. Thirdly, an unsignalized intersection in VANET is considered. In light traffic conditions, vehicles can regulate their velocities before arriving at the intersection according to the conflict zone occupancy time (CZOT) stored at the manager, so that they could get through the intersection without collision or stop. The delay can be reduced accordingly. Finally, an universal autonomous intersection management algorithm, which can work even with heavy traffic, is developed. The vehicle searches for safe entering windows in the CZOT. Then based on the found windows and the motion of preceding vehicle, the trajectories of vehicles can be planned using a segmented dynamic programming method. All the designed algorithms are successfully tested and verified by simulations in various scenarios

Cette thèse est consacrée à l'analyse de performance de Régulateurs de Vitesse Adaptatifs Coopératifs(CACC) pour un train de véhicules intelligents afin de réduire la congestion du trafic et améliorer la sécurité routière.Premièrement, la politique d'espacement, à Intervalles Constants de Temps (CTH) est introduite. Basé sur cette politique d'espacement, un nouveau système décentralisé de Deux-Véhicules-Devant CACC (TVACACC) est proposé, dans lequel l'accélération souhaitée de deux véhicules précédents est prise en compte. Ensuite, la stabilité de la chaîne du système proposé est analysée théoriquement. Il est démontré que grâce à l'aide de la communication multiple entre véhicules, une meilleure stabilité de la chaîne est obtenue par rapport au système conventionnel. Un train de véhicules dans le scénario Stop-and-Go est simulé avec une communication parfaite puis dégradée. Le système proposé donne un comportement stable de la chaîne, correspondant à l'analyse théorique.Deuxièmement, une technique de dégradation pour CACC est présentée comme stratégie alternative lorsque la communication sans fil est partiellement ou complètement perdue. La stratégie proposée, appelée DTVACACC, utilise le filtre de Kalman pour estimer l'accélération actuelle du véhicule précédent qui remplace l'accélération souhaitée. Il est démontré que la performance pour le DTVACACC, peut être maintenue à un niveau beaucoup plus élevé.Enfin, une approche d’Apprentissage par Renforcement (RL) pour système CACC est proposée. L' algorithme politique- gradient est introduit pour réaliser le contrôle longitudinal . Ensuite, la simulation a montré que cette nouvelle approche de RL est efficace pour CACC / This PhD thesis is dedicated to the performance analysis of Cooperative Adaptive Cruise Control (CACC) system for intelligent vehicle platoon with the main aims of alleviating traffic congestion and improving traffic safety. At first, the Constant Time Headway (CTH) spacing policy for vehicle platoon is introduced. Based on this spacing policy, a novel decentralized Two-Vehicle-Ahead CACC (TVACACC) system is proposed, in which the desired acceleration of two front vehicles is taken into account. Then the string stability of the proposed system is theoretically analyzed. It is shown that by using the multiple wireless communication among vehicles, a better string stability is obtained compared to the conventional system. Vehicle platoon in Stop-and-Go scenario is simulated with both normal and degraded communication.Secondly, a graceful degradation technique for CACC was presented, as an alternative fallback strategy when wireless communication is lost or badly degraded. The proposed strategy, which is referred to DTVACACC, uses Kalman filter to estimate the preceding vehicle’s current acceleration as a replacement of the desired acceleration. It is shown that the performance is maintained at a much higher level.Finally, a Reinforcement Learning (RL) approach of CACC system is proposed. The policy-gradient algorithm is introduced to achieve the longitudinal control. Then simulation has shown that this new RL approach results in efficient performance for CACC.