To bring down the number of traffic accidents and increase people’s mobility companies, such as Robot Engineering Systems (RES) try to put automated vehicles on the road. RES is developing the WEpod, a shuttle capable of autonomously navigating through mixed traffic. This research has been done in cooperation with RES to improve the localization capabilities of the WEpod. The WEpod currently localizes using its GPS and lidar sensors. These have proven to be not accurate and reliable enough to safely navigate through traffic. Therefore, other methods of localization and mapping have been investigated. The primary method investigated in this research is monocular Simultaneous Localization and Mapping (SLAM). Based on literature and practical studies, ORB-SLAM has been chosen as the implementation of SLAM. Unfortunately, ORB-SLAM is unable to initialize the setup when applied on WEpod images. Literature has shown that this problem can be solved by adding depth information to the inputs of ORB-SLAM. Obtaining depth information for the WEpod images is not an arbitrary task. The sensors on the WEpod are not capable of creating the required dense depth-maps. A Convolutional Neural Network (CNN) could be used to create the depth-maps. This research investigates whether adding a depth-estimating CNN solves this initialization problem and increases the tracking accuracy of monocular ORB-SLAM. A well performing CNN is chosen and combined with ORB-SLAM. Images pass through the depth estimating CNN to obtain depth-maps. These depth-maps together with the original images are used in ORB-SLAM, keeping the whole setup monocular. ORB-SLAM with the CNN is first tested on the Kitti dataset. The Kitti dataset is used since monocular ORB- SLAM initializes on Kitti images and ground-truth depth-maps can be obtained for Kitti images. Monocular ORB-SLAM’s tracking accuracy has been compared to ORB-SLAM with ground-truth depth-maps and to ORB-SLAM with estimated depth-maps. This comparison shows that adding estimated depth-maps in- creases the tracking accuracy of ORB-SLAM, but not as much as the ground-truth depth images. The same setup is tested on WEpod images. The CNN is fine-tuned on 7481 Kitti images as well as on 642 WEpod images. The performance on WEpod images of both CNN versions are compared, and used in combination with ORB-SLAM. The CNN fine-tuned on the WEpod images does not perform well, missing details in the estimated depth-maps. However, this is enough to solve the initialization problem of ORB-SLAM. The combination of ORB-SLAM and the Kitti fine-tuned CNN has a better tracking accuracy than ORB-SLAM with the WEpod fine-tuned CNN. It has been shown that the initialization problem on WEpod images is solved as well as the tracking accuracy is increased. These results show that the initialization problem of monocular ORB-SLAM on WEpod images is solved by adding the CNN. This makes it applicable to improve the current localization methods on the WEpod. Using only this setup for localization on the WEpod is not possible yet, more research is necessary. Adding this setup to the current localization methods of the WEpod could increase the localization of the WEpod. This would make it safer for the WEpod to navigate through traffic. This research sets the next step into creating a fully autonomous vehicle which reduces traffic accidents and increases the mobility of people.

The startup company Fleet Cleaner has developed a mobile robot, specialized in the hull cleaning of large cargo vessels. Navigation and localization of this robot is currently performed manually. This is a difficult process that is greatly complicated during operation. This is mainly due to the availability of relative positioning sensors only, which are prone to error build-up and noise, and to the difficulty of interpreting optical underwater images in turbid water conditions. Instead, operators must rely on acoustic images from a forward-looking sonar. In the field of mobile robotics, Simultaneous Localization and Mapping (SLAM) is an often used technique to improve navigation and localization by utilizing visual information. The objective of this thesis is to develop a sonar-based SLAM framework, tailored to working environment of the Fleet Cleaner robot. The thesis scope has been restricted to the conceptual design of such a framework and the implementation of one of the subsystems, visual odometry.  A conceptual design of a SLAM system is proposed using a systematic approach. Different working principles are evaluated according to operating conditions and requirements that specify desired behavior. Analysis of operating conditions reveal the limitations of sonar imagery, such as a high signal-to-noise ratio and inhomogeneous intensity patterns. In addition, the environment is sparse, with few distinct recognizable landmarks, limiting feature-based approaches. Because of these limitations, visual odometry is essential to reduce error build-up between loop closure corrections. A Fourier-based approach to visual odometry is implemented, taking the whole image view into account instead of extracted features. By analyzing the dominant peak in the phase correlation matrix, the in-plane sonar motion between consecutive image frames can be estimated. Several image processing steps are necessary to improve peak sharpness, increasing the quality of registration. To validate the proposed method, an experiment was conducted during cleaning of the Pioneering Spirit, the world’s largest construction vessel. Under normal circumstances, visual odometry showed less error build-up in the position estimate than wheel odometry. However, outliers appear when driving near the waterline, caused by reflections and wave reverberations. Ultimately, the proposed visual odometry method improves the current positioning system and serves as a basis for an integral SLAM implementation.

Trajectory generators are designed to create reference paths that are parametrized by time as well as by velocity or acceleration. In the literature, it has been noted that often path planners are the last step in the planning module of autonomous truck-trailer vehicles, which results in a collision-free track. A trajectory generator can improve this path by making it smoother, which improves passenger comfort and decreases stress on the engine. The available trajectory generators are in their majority based on large number of optimizations, which limits their real-time applicability. This research aims to create a computationally efficient parametric method for trajectory generation that leads to a continuous motion of the vehicle. Using cubic splines, a unique method of parametrization is applied such that a custom velocity profile is defined along the trajectory. Together with choosing appropriate boundary conditions for each path segment, a trajectory is generated that proves continuous reference tangent angle and yaw rate, while following a velocity profile that is continuous in acceleration. This leads to a continuous longitudinal and lateral motion of the truck-trailer vehicle. While continuity conditions are achieved, simulations results showed high reference yaw rate peaks, which could be difficult to track under non-ideal circumstances. An alternative generator was designed based on quintic splines, such that yaw rate peaks can be limited. However, this minimization leads to oscillations of the reference trajectory. A trade-off needs to be defined to balance between minimizing peak yaw rates and oscillations. Closed-loop simulations using a kinematic truck-trailer model show that tracking the generated trajectory lead to continuous steering wheel and velocity input signals. Furthermore, the trajectory was smoothly tracked without the use of an optimal controller. Eventually all research goals have been achieved. Further studies could analyse the performance of the proposed trajectory generator using a dynamic truck-trailer model such that effects of wheel slip and load transfer can be investigated. It is also of interest to analyse the trailer behaviour when tracking the trajectory.

Safety is an important parameter considered during the design of Advanced Driver Assistance Systems and fully autonomous vehicles. One of the ways to assess the road vehicle's safety is by estimating the likelihood with which the vehicle can react to prevent the danger. In the presence of an impending collision (hazard), the trajectory planning module in the autonomous vehicle would generate few escape trajectories to avoid the collision. The escape trajectory is chosen such that it maximises the safety of the vehicle based on certain criteria. One of these criteria is the vehicle's avoidance capability throughout the trajectory. This thesis presents an avoidance metric that is constructed using a computational procedure to quantify the avoidance capability of the vehicle in both pure longitudinal (1-D) and, combination of both lateral and longitudinal (2-D) scenario. The key idea is (a) Propagate forward in time the current state of the host (and the world) using a vehicle model to estimate the host's reachable set of states. (b) Carefully select a set of samples from the reachable set and repeat the propagation. (c) At every step, the trajectories that lead to collisions are eliminated. The ratio of the size of the region spanned by the remaining trajectories to the size of the region spanned by all the trajectories (including those that lead to collision) then constitute the estimate of the host's avoidance capability. Through simulations on specific use-cases, for a pure longitudinal motion, on comparison with Brake Threat Number (BTN), it was observed that the metric (from the proposed computational procedure) performs very similar to BTN and also takes a low computational time of 380 [ms] for a time horizon of 2.5 [s]. However, in the presence of dynamic obstacles, major differences in performance (such as discontinuities, step-like variation), were observed between the metric and BTN. In the case of the combination of both lateral and longitudinal motion, the computation time for the proposed procedure was found to be independent of the number of obstacles. To reason about the accuracy of the approximation of the reachable set for a double integrator model obtained from the proposed procedure, it was compared with the nodes obtained from Rapidly-exploring Random Trees (RRT), an under-approximation, and found that the nodes lie either very close or well within the boundary of the approximation. However, the computation time for the proposed procedure took around 10.87 [s] which comes as a major drawback.

The development of intelligent vehicle and autonomous driving asked a higher requirement of ADAS on its functionality. Currently, ADAS systems are able to detect and segment urban and highway driving scenes. They cannot, in general, extract ’meaning’ from this segmentation yet. Learning the intention of other road users will help ADAS understand surroundings and make a response. In a highway scenario, understanding what the preceding vehicle is about to do, is the minimum level of understanding the environment in order to take a decision about your own actions. Among the driving behaviors the preceding vehicle could do, lane change is a complex and dangerous one. Thus, we aimed to develop a real-time lane change intention recognition model. This report presents three models inspired by the Hidden Markov Models (HMMs) and Relevance Vector Machines (RVMs). Besides these two methods, we proposed a new model which combines them and overcome both of their main shortcomings. According to the testing result, the proposed model can correctly recognize more than 95% of the driving behaviors within 1 second the behavior starts, while the F1 score is also as high as 0.98. Besides the high accuracy, the model also has a good performance on the flexibility, testing complexity and the generalization ability.

The platooning of trucks provides significant benefits in the existing transportation systems regarding social, economic and environmental aspects. Due to the platoon's high dependence on sensors, it is critically important to have fault-tolerant countermeasures that deal efficiently with failures by achieving minimal risk condition. This thesis tackles designing an emergency escape manoeuvre for a faulty truck that has lost its ability to monitor the driving environment while moving in a platoon formation on a highway. A fallback strategy is proposed that leads the faulty truck to park on the shoulder of the road and allows the rest of the platoon participants to continue their journey. To solve this problem, first, a functional platooning system should be designed. Therefore, a nonlinear bicycle dynamic model is employed, while a new Bidirectional (BD) Cooperative Adaptive Cruise Control (CACC) system is utilized for control purposes. The emergency escape manoeuvre has to be pre-computed and ready to be triggered when the failure occurs, as concerning defined specifications, limitations and constraints. It is generated using a quintic splines methodology, while a new optimization approach regarding the minimization of jerk peaks is adopted, enhancing the efficiency of the design procedure. Furthermore, a gap-closing controller is employed and tuned accordingly to reform the platoon after the faulty truck abandons the formation. Moreover, a suitable decision logic is defined to achieve smooth transition among regular performance and fallback state at both platoon and inter-vehicle level. The functionality of each component is firstly evaluated individually, based on specific performance metrics. Finally, all the mentioned components are put together and the fallback strategy that executes emergency escape manoeuvre in platoon context is derived. Several simulation tests illustrate proof of principle for the proposed framework. It is shown that the proposed strategy indeed achieves minimal risk condition for the faulty truck achieving minimal tracking errors. A global applied strategy is adopted for the gap-closing controller by making healthy follower(s) travel with the maximum possible acceleration. At the same time, the leading truck of the formation maintains a constant speed until the time gap between them becomes $0.8s$, before the switch back to the CACC, to complete this action as fast as possible and safety issues in the driving performance. Additionally, the CACC system is evaluated to ensure that the string stability criterion is satisfied in the mode of operation. Moreover, a virtual leader mechanism is introduced to fill in faulty truck's blind spot in the road that arise from the sensor failure. It is shown that it can provide a sufficient window of movement for the platoon before switching to the fallback strategy.

With the tremendous increase of vehicles and limited infrastructure for its smooth movement, traffic jams have become a significant problem these days. To address this issue, significant research and development in the field of Intelligent Transportation System (ITS) is currently being carried out. One such technological development that allows grouping of vehicles into platoons, controlled by one leading vehicle has proved to be a fruitful and potential solution for the existing issue of traffic jams. The state of the art technology that enables vehicle platooning is called Cooperative Adaptive Cruise Control (CACC). In CACC, the individual vehicles are grouped into platoons and allowed to automatically adjust their speeds using on-board sensors and vehicle to vehicle communication to maintain a desired and safe inter-vehicle distance. The specialty of CACC is that it enables to have small inter-vehicle distance between the vehicles which increases road throughput and reduces air drag on the vehicle. Consequently, traffic jams and fuel emissions are reduced. However, a crucial challenge arrives due to its high dependency on mechatronic devices, as a fault in such devices could lead to unsafe conditions for the vehicles in a platoon. Hence, there is a need of making CACC application Fail-Operational, which means that the vehicles would continue to function safely under faults and failures of those devices. TNO in its EcoTwin III project has identified Electronics Control Unit (ECU) to be a very critical component and uses the concept of redundancy to provide the Fail-operational capability that can tolerate one failure of ECU. However, this implementation involves a transition period caused due to fault detection and switching of ECU. This transition period could jeopardize the safety of vehicles under conditions where a nominal ECU would have kept the vehicles safe. Thus the objective of this MSc thesis is to address the problem of the transition period of a homogeneous platoon under ideal communication network conditions. In the first part, a model is proposed that captures the effect of the transition from primary ECU to the standby ECU on the platoon dynamics. This is done for both warm and hot standby strategies which depend on the functionality of the standby ECU. We see how this transition period affects the overall safety of the system under a worst-case scenario (emergency braking). In the second part, to improve the safety and exclude the effect of the transition period, a new method of implementation of existing control law is proposed. This technique exploits the benefit of using communicated data from the preceding vehicle for generating control input during the transition period. Simulations are conducted for a two-vehicle platoon. The results show the effect of the transition period in a warm standby strategy will lead to collisions. The simulations also show that the hot standby strategy outperforms the warm standby one, though it cannot avoid collisions at large transition periods. On the other hand, the proposed new strategy shows the potential of using the communicated signal in preventing collision and shows significant improvements in comparison to other two strategies at the larger transition period.