Robust Trajektorieförutsägelse för Autonoma Fordon med Konform Maskininlärning : Osäkerhetskvantifiering med Sannolikhetsgarantier för Säker Banutformning i Motorvägsscenarier

Ensuring safety in autonomous driving requires not only accurate predictions of surrounding traffic agents but also reliable quantification of the uncertainty associated with those predictions. This thesis addresses the problem of rigorous uncertainty quantification in trajectory forecasting for autonomous heavy vehicle combinations operating in highway environments. In such safety-critical scenarios, black-box machine learning models often suffer from performance degradation due to out-of-distribution inputs, which can lead to unsafe path planning decisions if not properly accounted for. To tackle this, we investigate and extend a family of methods known as conformal prediction: a model-agnostic framework that wraps around any trajectory predictor and constructs prediction regions with guaranteed statistical coverage, without assuming specific data distributions. The project analyzes state-of-the-art conformal methods for both finite and infinite-horizon control settings, with focus on their empirical performance in simulated highway scenarios. We propose novel extensions that embed heuristic uncertainty or leverage time-series modeling to adapt prediction sets online, improving coverage under distribution shifts. These methods are tested in a closed-loop autonomous truck simulator, where predicted regions are integrated with a model predictive controller (MPC) responsible for generating safe and feasible paths. This MPC integration is not the focus of the research, but is instead used as a case study to demonstrate how quantified uncertainty can inform downstream planning. Results show that the novel adaptive conformal methods maintain coverage under challenging and changing traffic configurations and produce uncertainty bounds that are more reliable than those based on heuristic methods. This work enables downstream systems, such as path planners, to better handle prediction uncertainty with formal guarantees, closing the gap between learning-based forecasting and robust motion planning.