Once automatic vehicles are available, working from self-driving car (WFC) in the AV's mobile office will be a real option. It allows firms to socialize land costs for office space from the office lot to road infrastructure used by AV. Employees, in turn, can switch wasted commuting time into working hours and reduce daily time tied to working. We develop a microeconomic model of employer's offer and employees choice of WFC contracts and hours. Using data for Germany and the U.S., we perform Monte Carlo studies to assess whether WFC may become reality. Eventually, we study the impact of transport pricing on these choices. Our findings is, that WFC contracts are likely to be a standard feature of large cities given current wages, office, and current and expected travel costs. There is a clear decline of hours spent working in office. On average, WFC hours and distance traveled slightly exceed commuting figures.

The Localization and Map building module is a core building block for designing an autonomous vehicle. It describes the vehicle ability to create an accurate model of its surroundings and maintain its position in the environment at the same time. In this thesis work, we contribute to the autonomous driving research area by providing a proof-of-concept of integrating SLAM solutions into commercial vehicles; improving the robustness of the Localization and Map building module. The proposed system applies Bayesian inference theory within the occupancy grid mapping framework and utilizes Rao-Blackwellized Particle Filter for estimating the vehicle trajectory. The work has been done at Scania CV where a heavy duty vehicle equipped with multiple-Lidar sensory architecture was used. Low level sensor fusion of the different Lidars was performed and a parallelized implementation of the algorithm was achieved using a GPU. When tested on the frequently used datasets in the community, the implemented algorithm outperformed the scan-matching technique and showed acceptable performance in comparison to another state-of-art RBPF implementation that adapts some improvements on the algorithm. The performance of the complete system was evaluated under a designed set of real scenarios. The proposed system showed a significant improvement in terms of the estimated trajectory and provided accurate occupancy representations of the vehicle surroundings. The fusion module was found to build more informative occupancy grids than the grids obtained form individual sensors. / Modulen som har hand om både lokalisering och byggandet av karta är en av huvudorganen i ett system för autonom körning. Den beskriver bilens förmåga att skapa en modell av omgivningen och att hålla en position i förhållande till omgivningen. I detta examensarbete bidrar vi till forskningen inom autonom bilkörning med ett valideringskoncept genom att integrera SLAM-lösningar i kommersiella fordon, vilket förbättrar robustheten hos lokaliserings-kartbyggarmodulen. Det föreslagna systemet använder sig utav Bayesiansk statistik applicerat i ett ramverk som har hand om att skapa en karta, som består av ett rutnät som används för att beskriva ockuperingsgraden. För att estimera den bana som fordonet kommer att färdas använder ramverket RBPF(Rao-Blackwellized particle filter). Examensarbetet har genomförts hos Scania CV, där ett tungt fordon utrustat med flera lidarsensorer har använts. En lägre nivå av sensor fusion applicerades för de olika lidarsensorerna och en parallelliserad implementation av algoritmen implementerades på GPU. När algoritmen kördes mot data som ofta används av ”allmänheten” kan vi konstatera att den implementerade algoritmen ger ett väldigt mycket bättre resultat än ”scan-matchnings”-tekniken och visar på ett acceptabelt resultat i jämförelse med en annan högpresterande RBPFimplementation, vilken tillför några förbättringar på algoritmen. Prestandan av hela systemet utvärderas med ett antal egendesignade realistiska scenarion. Det föreslagna systemet visar på en tydlig förbättring av uppskattningen av körbanan och bidrar även med en exakt representation av omgivningen. Sensor Fusionen visar på en bättre och mer informativ representation än när man endast utgår från de individuella lidarsensorerna.

A transformative revolution in transportation is coming with the advent of Au-tonomous Vehicles (AVs), which are expected to increase mobility, reduce trafficcongestion, and save fuel. Although AVs present significant advantages, they alsopose substantial challenges, particularly when it comes to security and safety. Theaim of this study is to map out the existing knowledge in order to facilitate furtherresearch and development, which will hasten the rollout of secure and reliable au-tonomous vehicles. This, in turn, will enable a sustainable and efficient future fortransportation. Research on AV safety and security is reviewed in this thesis in acomprehensive systematic literature review. The search process identified a total of283 studies published between 2019 and 2022, out of which 24 studies were selectedthrough a multi-stage process according to our predefined protocol. Based on re-search topics in selected studies, our findings have a significant impact on the fieldof Artificial Intelligence and automated vehicles. Based on our findings, we canprovide a summary of current knowledge regarding the safety, security, and stabilityimplications of autonomous vehicles. Simulations, real-life experiments, and physi-cal tests were all used in the selected articles for evaluation. Aside from the excellentresults, we identified many limitations of the articles, including the limitations of thedata sets, the analysis of unusual events, and the verification practices.

Platooning is a method where a chain of vehiclesdrive with small inter-vehicular distances. The many benefitsof autonomous platooning includes improved fuel economy,less congestion and safer transportation. To create a safe andfunctional platoon the operational software needs to be able tohandle various road surfaces without the risk of a crash. Thisreport is aiming to improve the safety of a platoon by includingcommunication of data between vehicles in the chain. Specificallythe focus has been on transferring information about the tireslip, to model a cooperative adaptive cruise control (C-ACC)and combine the two. A system was designed using the dynamicsfor a quarter-car model and then connected to a controller and aplatoon of four vehicles. Simulations of when the leading vehiclebraked hard on two different road surfaces with and withoutthe slip awareness was conducted. The tire slip awareness in thecontroller consisted of proportional control on the error and alow-pass filter. The simulations showed that the inclusion of thetire slip in the controller improved the platooning performance,in the sense that the inter-vehicle distance could be contained.It was also shown the controller could be tuned so that the slipratios were limited. / Konvojkörning är en metod där en kedjaav fordon åker med små interna distanser. De många fördelarnamed förarlösa konvojer inkluderar förbättrad bränsleförbukning, mindre trafik och säkrare transportering. För atten säker och funktionell konvoj ska kunna skapas krävs detatt mjukvaran kan handskas med varierande vägunderlag utanrisk att krocka. Den här rapporten siktar på att förbättrasäkerheten i konvojkörning genom att överföra data till andrafordon i konvojkedjan. Speciellt har fokuset legat på överförainformation om däcksliring, att modellera en kooperative adaptivfarthållare (C-ACC) och sedan kombinera de två. Ett systemdesignades genom att använda dynamiken av en fjärdedelsbil och sen ansluta modellen till en konvoj med fyra fordon.Simulationer av när det ledande fordonet tvärbromsade på olikavägunderlag med och utan däcksliringsinfromation genomfördes.Däckslirnings i regulatorn bestod av proportionerlig kontroll påfelet och ett lågpassfilter. Simulationerna visade att inkluderingenav däcksliringsinformation i regulatorn förbättrar konvojensprestanda, på så sätt att de interna distanserna kan hanteras.Det kunde också påvisas att kontrollern kunde kalibreras så attslirningen begränsades. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

Multitask Learning (MTL) was conceived as an approach to improve thegeneralization ability of machine learning models. When applied to neu-ral networks, multitask models take advantage of sharing resources forreducing the total inference time, memory footprint and model size. Wepropose MTL as a way to speed up deep learning models for applicationsin which multiple tasks need to be solved simultaneously, which is par-ticularly useful in embedded, real-time systems such as the ones foundin autonomous cars or UAVs.In order to study this approach, we apply MTL to a Computer Vi-sion problem in which both Object Detection and Semantic Segmenta-tion tasks are solved based on the Single Shot Multibox Detector andFully Convolutional Networks with skip connections respectively, usinga ResNet-50 as the base network. We train multitask models for twodifferent datasets, Pascal VOC, which is used to validate the decisionsmade, and a combination of datasets with aerial view images capturedfrom UAVs.Finally, we analyse the challenges that appear during the process of train-ing multitask networks and try to overcome them. However, these hinderthe capacity of our multitask models to reach the performance of the bestsingle-task models trained without the limitations imposed by applyingMTL. Nevertheless, multitask networks benefit from sharing resourcesand are 1.6x faster, lighter and use less memory compared to deployingthe single-task models in parallel, which turns essential when runningthem on a Jetson TX1 SoC as the parallel approach does not fit intomemory. We conclude that MTL has the potential to give superior per-formance as far as the object detection and semantic segmentation tasksare concerned in exchange of a more complex training process that re-quires overcoming challenges not present in the training of single-taskmodels.

Autonomous Driving has recently gained a lot of recognition and provides challenging research with an aim to make transportation safer, more convenient and efficient. This emerging technology also has widespread applications and implications beyond all current expectations in other fields of robotics. Environment perception is one of the big challenges for autonomous robots. Though a lot of methods have been developed to utilize single sensor based approaches, since different sensor types have different operational characteristics and failure modes, they compliment each other. Different sensors provide different sets of data, which creates difficulties combining information to form a unified picture. The proposed solution consists of low level sensor fusion of LIDAR and stereo camera data using an occupancy grid framework. Bayesian inference theory is utilized and a real time system has been implemented on a 1/10 scale robot vehicle. The result of the thesis shows that it is possible to use a 2D LIDAR and stereo camera to build a map of the environment. The implementation focuses on the practical issues like blind spots of individ sensors. Overall, the fused occupancy grid gives better result than occupancy grids from individual sensors. Sensor confidence is higher for the camera since frequency of mapping of a 2D LIDAR is low / Autonom körning har nyligen fått mycket erkännande och erbjuder utmanande forskningsmöjligheter med målen att göra transporter säkrare, bekvämare och effektivare. Den framväxande tekniken har också tillämpningar och konsekvenser inom andra områden av robotteknik i en omfattning som vida överträffat förväntningarna. Att uppfatta den omgivande miljön är en av de stora utmaningarna för autonoma robotar. Även om många metoder har utvecklats där en enda sensor används, har de bästa resultaten uppnåtts genom en kombination av sensorer. Olika sensorer ger olika uppsättningar data, vilket skapar svårigheter att kombinera information för att bilda en enhetlig bild. Den föreslagna lösningen består av lågfrekvent sensorfusion av LIDAR och stereokamera med användning av rutnätsramar. Bayesisk inferensteori har använts och ett realtidssystem har implementerats på robotfordon i skala 1/10. Resultatet av examensarbetet visar att det är möjligt att använda en 2D-LIDAR och en stereokamera för att bygga en omgivningskarta. Genomförandet fokuserar på praktiska problem såsom problem med döda vinkeln hos dessa sensorer. Generellt ger det kombinerade rutnätet bättre resultat än det från enskilda sensorer. Sensortillförlitligheten är högre för kameran då 2D-LIDAR kartlägger med mycket lägre frekvens

Autonomous driving is the concept of a vehicle that operates in traffic without instructions from a driver. A major challenge for such a system is to provide a comprehensive, accurate and compact scene model based on information from sensors. For such a model to be comprehensive it must provide 3D position and semantics on relevant surroundings to enable a safe traffic behavior. Such a model creates a foundation for autonomous driving to make substantiated driving decisions. The model must be compact to enable efficient processing, allowing driving decisions to be made in real time. In this thesis rectangular objects (The Stixelworld) are used to represent the surroundings of a vehicle and provide a scene model. LIDAR and semantic segmentation are fused in the computation of these rectangles. This method indicates that a dense and compact scene model can be provided also from sparse LIDAR data by use of semantic segmentation. / Fullt självkörande fordon behöver inte förare. Ett sådant fordon behöver en precis, detaljerad och kompakt modell av omgivningen baserad på sensordata. Med detaljerad avses att modellen innefattar all information nödvändig för ett trafiksäkert beteende. Med kompakt avses att en snabb bearbetning kan göras av modellen så att fordonet i realtid kan fatta beslut och manövrera i trafiken. I denna uppsats tillämpas en metod där man med rektangulära objekt skapar en modell av omgivningen. Dessa beräknas från LIDAR och semantisk segmentering. Arbetet indikerar att med hjälp av semantisk segmentering kan en tät, detaljerad och kompakt modell göras även från glesa LIDAR-data.

Motion planning consists of finding a feasible path of an object between an initial state and a goal state, and commonly constitutes a sub-system of a larger autonomous system. Motion planners that utilize sampling-based algorithms create an implicit representation of the search space via sampling said search space. Autonomous systems that rely on real-time motion planning benefit from the ability of these algorithms to quickly compute paths that are optimal or near optimal. For sampling-based motion planning algorithms, the sampling strategy greatly affects the convergence speed of finding these paths, i.e., how the sampling distribution is shaped within the search space. In baseline approaches, the samples may be drawn with uniform probability over this space. This thesis project explores a learning-based approach that can utilize experience from previous successful motion plans to provide useful information in novel planning scenarios, as a means of improvement over conventional motion planning methods. Specifically, the focus has been on learning the sampling distributions in both the state space and the control space of an autonomous ground vehicle. The innovatory parts of this work consist of (i) learning the control space sampling distributions, and (ii) learning said distributions for a tractor-trailer system. At the core of the method is an artificial neural network consisting of a conditional variational autoencoder. This artificial neural network is capable of learning suitable sampling distributions in both the state space and control space of a vehicle in different planning scenarios. The method is tested in four different environments and for two kinds of vehicles. Evaluation is partly done by comparison of results with a conventional motion planning algorithm. These evaluations indicates that the artificial neural network can produce valuable information in novel planning scenarios. Future work, primarily on how the artificial neural network may be applied to motion planning algorithms, is necessary to draw further conclusions. / Rörelseplanering består av att hitta en genomförbar bana för ett objekt mellan ett initialtillstånd och ett måltillstånd, och utgör vanligtvis ett delsystem av ett större autonomt system. Rörelseplanerare som använder provtagningssbaserade algoritmer skapar en implicit representation av sökutrymmet via provtagning av sökutrymmet. Autonoma system som förlitar sig på rörelseplanering i realtid drar nytta av dessa algoritmers förmåga att snabbt beräkna banor som är optimala eller nästan optimala. För provtagningssbaserade rörelseplaneringsalgoritmer påverkar provtagningsstrategin i hög grad konvergenshastigheten för att hitta dessa vägar, dvs. hur provtagningsfördelningen är formad inom sökutrymmet. I standardmetoder kan stickproven dras med jämn sannolikhet över detta utrymme. Detta examensarbete utforskar en lärande-baserat metod som kan utnyttja erfarenheter från tidigare lyckade rörelseplaner för att tillhandahålla användbar information i nya planeringsscenarier, som ett medel för förbättring jämfört med konventionella rörelseplaneringsmetoder. Specifikt har fokus legat på att lära sig provtagningssfördelningarna i både tillståndsrummet och styrsignals-rummet för ett autonomt markfordon. De nyskapande delarna av detta arbete består av att (i) lära sig kontrollutrymmessamplingsfördelningarna, och (ii) inlärning av nämnda provtagningsfördelningarna för ett traktor-släpsystem. Kärnan i metoden är ett artificiellt neuralt nätverk bestående av en conditional variational autoencoder. Detta artificiella neurala nätverk är kapabelt att lära sig lämpliga provtagningsfördelningar i både tillståndsrummet och kontrollrummet för ett fordon i olika planeringsscenarier. Metoden testas i fyra olika miljöer och för två olika av fordon. Utvärdering görs delvis genom jämförelse av resultat med en konventionell rörelseplaneringsalgoritm. Dessa utvärderingar tyder på att det artificiella neurala nätverket kan producera värdefull information i nya planeringsscenarier. Mer forskning, i första hand med hur det artificiella neurala nätverket kan tillämpas på rörelseplaneringsalgoritmer, är nödvändigt för att dra ytterligare slutsatser.

The field of automated vehicle demands outstanding reliability figures to be matched by the artificially driving agents. The software architectures commonly used originate from decades of automation engineering, when robots operated only in confined environments on predefined tasks. On the other hand, autonomous driving represents an “into the wild” application for robotics. The architectures embraced until now may not be sufficiently robust to comply with such an ambitious goal. This research activity proposes a bio-inspired sensorimotor architecture for cognitive robots that addresses the lack of autonomy inherent to the rules-based paradigm. The new architecture finds its realization in an agent for autonomous driving named “Co-driver”. The Agent synthesis was extensively inspired by biological principles that contribute to give the Co-driver some cognitive abilities. Worth to be mentioned are the “simulation hypothesis of cognition” and the “affordance competition hypothesis”. The former is mainly concerned with how the Agent builds its driving skills, whereas the latter yields an interpretable agent notwithstanding the complex behaviors produced. Throughout the essay, the Agent is explained in detail, together with the bottom-up learning framework adopted. Overall, the research effort bore an effectively performing autonomous driving agent whose underlying architecture provides considerable adaptation capability. The thesis also discusses the aspects related to the implementation of the proposed ideas into a versatile software that supports both simulation environments and real vehicle platforms. The step-by-step explanation of the Co-driver is made up of theoretical considerations supported by working simulation examples, some of which are also released open-source to the research community as a driving benchmark. Eventually, guidelines are given for future research activities that may originate from the Agent and the hierarchical training framework devised. First and foremost, the exploitation of the hierarchical training framework to discover optimized longer-term driving policies.

In this thesis, an extension of the existing localization system of the ABSOLUT project is presented, with the aim of making it more resistant to GNSS errors. This enhanced system is based on the integration of a LiDAR sensor. Initially, a 3D map of the traversed route is created using the LiDAR sensor. This process employs an existing factor graph-based SLAM algorithm, which is made more stable and accurate through the inclusion of a surveyed elevation profile of the environment, the integration of vehicle odometry sensors, and bias estimates of the IMU. The generated map is used during the drive to determine the vehicle's position by comparing it with the currently captured point clouds. This procedure relies on a newly developed Error-State Kalman Filter that fuses LiDAR odometry with absolute LiDAR position estimates. To optimally use the pose estimation from the various sensor systems, an approach is proposed that adaptively combines the estimates based on the environment. The performance of the developed system is evaluated using real driving data.