Recently, formal methods have gained significant traction for describing, checking, and synthesizing the behaviors of cyber-physical systems. Among these methods, temporal logics stand out as they offer concise mathematical formulas to express desired system properties. In this thesis, our focus revolves around two primary applications of temporal logics in describing the behavior of autonomous system. The first involves integrating temporal logics with machine learning techniques to deduce a temporal logic specification based on the system's execution traces. The second application concerns using temporal logics to define traffic rules and develop a control scheme that guarantees compliance with these rules for autonomous vehicles. Ultimately, our objective is to combine these approaches, infer a specification that characterizes the desired behaviors of autonomous vehicles, and ensure that these behaviors are upheld during runtime. In the first study of this thesis, our focus is on learning Signal Temporal Logic (STL) specifications from system execution traces. Our approach involves two main phases. Initially, we address an offline supervised learning problem, leveraging the availability of system traces and their corresponding labels. Subsequently, we introduce a time-incremental learning framework. This framework is designed for a dataset containing labeled signal traces with a common time horizon. It provides a method to predict the label of a signal as it is received incrementally over time. To tackle both problems, we propose two decision tree-based approaches, with the aim of enhancing the interpretability and classification performance of existing methods. The simulation results demonstrate the efficiency of our proposed approaches. In the next study, we address the challenge of guaranteeing compliance with traffic rules expressed as STL specifications within the domain of autonomous driving. Our focus is on developing control frameworks for a fully autonomous vehicle operating in a deterministic or stochastic environment. Our frameworks effectively translate the traffic rules into high-level decisions and accomplish low-level vehicle control with good real-time performance. Compared to existing literature, our approaches demonstrate significant enhancements in terms of runtime performance. / 2025-01-17T00:00:00Z

<p dir="ltr">Path-tracking control is an integral part of motion planning in autonomous vehicles, in which the vehicle's lateral and longitudinal positions are controlled by a control system that will provide acceleration and steering angle commands to ensure accurate tracking of longitudinal and lateral movements in reference to a pre-defined trajectory. Extensive research has been conducted to address the growing need for efficient algorithms in this area. In this dissertation, a scenario and machine learning-based data-driven control approach is proposed for a path-tracking controller. Firstly, a Deep Reinforcement Learning model is developed to facilitate the control of longitudinal speed. A Deep Deterministic Policy Gradient algorithm is employed as the primary algorithm in training the reinforcement learning model. The main objective of this model is to maintain a safe distance from a lead vehicle (if present) or track a velocity set by the driver. Secondly, a lateral steering controller is developed using Neural Networks to control the steering angle of the vehicle with the main goal of following a reference trajectory. Then, a path-planning algorithm is developed using a hybrid A* planner. Finally, the longitudinal and lateral control models are coupled together to obtain a complete path-tracking controller that follows a path generated by the hybrid A* algorithm at a wide range of vehicle speeds. The state-of-the-art path-tracking controller is also built using Model Predictive Control and Stanley control to evaluate the performance of the proposed model. The results showed the effectiveness of both proposed models in the same scenario, in terms of velocity error, lateral yaw angle error, and lateral distance error. The results from the simulation show that the developed hybrid A* algorithm has good performance in comparison to the state-of-the-art path planning algorithms.</p>

Autonomous driving technology is continuously evolving at an accelerated pace. The road environment is always uncertain, which requires an evasive manoeuvre that an autonomous vehicle can take. This evasive behaviour to avoid accidents in a critical situation is analogous to autonomous racing that operates at the limits of stable vehicle handling. In autonomous racing, the vehicle must operate in highly nonlinear operating conditions such as high-speed manoeuvre on sharp turns, avoiding obstacles and slippery road conditions. These dynamically changing racing situations require advanced path planning systems with obstacle avoidance executed in real-time. Therefore, the motion planning problem for autonomous racing is analogous to safe and reliable autonomous vehicle operation in critical situations. This thesis project evaluates the application of traction adaptive motion planning to autonomous racing on different road surfaces for a small-scale test vehicle in real-time. The evaluation is based on a state-of-the-art algorithm that uses a combination of optimization, trajectory rollout, and constraint adaption framework called "Sampling Augmented Real-Time Iteration (SAARTI)". SAARTI allows motion planning and control with respect to time-varying vehicle actuation capabilities while taking locally adaptive traction into account for different parts of the track as a constraint. Initially, the SAARTI framework is adapted to work with the SmallVehicles-for-Autonomy (SVEA) system; then, the whole system is simulated in a ROS (Robot Operating System) based SVEA simulator with a Hardware-in-the-loop setup. Later, the same setup is used for the real time experiments that are carried out using the SVEA vehicles, and the different critical scenarios are tested on the SVEA vehicle. The emphasis was given to the experimental results; therefore, the results also consider computationally intensive localization inputs while the motion planner was implemented in real-time instead of a simulation setup. The experimental results showed the impact of planning motions according to an approximately correct friction estimate when the friction parameter was close to the actual value. The results indicated that the traction variation had indeed affected the lap time and trajectory taken by the test vehicle. The lap time is affected significantly when the coefficient of friction value is far away from the real friction coefficient. It is observed that the lap time increased significantly at higher values of friction coefficient, when involving more excessive over-estimation of the traction, leading to the oscillatory motion and lane exits. Furthermore, the non-adaptive case scenario result shows that the test vehicle performed better when given friction parameter inputs to the algorithm approximately equal to the real friction value. / Teknik för autonom körning har utvecklats i snabb takt de senaste åren. Trafikmiljön innehåller många källor till osäkerhet, vilket ibland kräver undanmanövrar av det autonoma fordonet. Undanmanövrar i kritiska situationer är analoga med autonom racing i det avseendet att fordonet opererar nära gränsen av dess fysiska förmåga. I autonom racing måste fordonet fungera i hög grad olinjära driftsförhållanden som höghastighetsmanöver i skarpa svängar, undvika hinder och halt väglag. Dessa dynamiska föränderliga racingsituationer kräver avancerad vägplaneringssystem med undvikande av hinder exekveras i realtid. Därför är rörelseplaneringsproblemet för autonom racing är analogt med det för säkra undanmanövrer i kritiska situationer. Detta examensarbete utvärderar tillämpningen av dragkraft adaptiv till autonom racing på olika väglag för ett småskaligt testfordon i realtid. Utvärderingen baseras på en algoritm som kallas "Sampling Augmented Real Time Iteration (SAARTI)" som tillåter rörelse planering och kontroll med avseende på tidsvarierande fordonsdynamik, på så vis tar algoritmen hänsyn till lokalt varierande väglag. Arbetet började med att integrera SAARTI-ramverket med testplattformen Small-Vehicles-for-Autonomy (SVEA). Därefter utfördes hardware-in-the-loop simuleringar i ROS (Robot Operating System), och därefter utfördes fysiska tester med SVEA plattformen. Under experimenten kördes SAARTI-algoritmen parallellt med en beräkningsintensiv SLAM-algoritm för lokalisering. De experimentella resultaten visade att adaptiv rörelseplanering kan avhjälpa problemet med lokalt varierande väglag, givet att den uppskattade friktionsparametern är approximativt korrekt. Varvtiden påverkas negativt när friktionsskattningen avviker från den verkliga friktionskoefficienten. Vidare observerades att varvtiden ökade vid höga värden på den skattade friktionsparametern, vilket gav upphov till mer aggressiva manövrer, vilket i sin tur gav upphov till oscillerande rörelser och avåkningar.