One central challenge for Autonomous Driving (AD) systems is ensuring functional safety. This is affected by all parts of vehicle automation systems: environment perception, decision making, and actuation. The AD system manages its activity towards achieving its goals to maintain in the safety domain, upon an environment using observation through sensors and consequent actuators. Therefore, this research investigates the operational safety for the AD system. In this research, a simulation for the Autonomous Emergency Braking (AEB) system and a simple scenario are constructed on CARLA, an open-source simulator for autonomous driving systems, to investigate the factors that impact the performance of the AEB system. The time-series data that influence the AEB are collected and fed into three time-series analysis algorithms, Autoregressive Integrated Moving Average model (ARIMA), regression tree and Long short-term memory (LSTM), to select a suitable time-series algorithm to be used for the AEB system. The results show that weather, the measurement range of the sensors, and noise can affect the results of the AEB system. After comparing the performance of these three time-series algorithms through contrasting the recall and precision of these three algorithms to detect noise in the data, the results can be obtained that LSTM has the better performance for long-term analysis. And ARIMA is more suitable for short-term time-series analysis. LSTM is chosen to analyze the time-series data, since the long-term time-series analysis is necessary for the AEB system and it can detect the noise in the variables of the AEB system with better performance. / En central utmaning för AD system är att säkerställa funktionell säkerhet. Detta påverkas av alla delar av fordonsautomatiseringssystem: miljöuppfattning, beslutsfattande och aktivering. AD -systemet hanterar sin aktivitet för att uppnå sina mål att upprätthålla inom säkerhetsområdet, i en miljö som använder observation genom sensorer och därav följande ställdon. Därför undersöker denna forskning den operativa säkerheten för AD systemet. I denna forskning konstrueras en simulering för AEB -systemet och ett enkelt scenario på CARLA, en simulator med öppen källkod för autonoma körsystem, för att undersöka de faktorer som påverkar prestandan för AEB systemet. Tidsseriedata som påverkar AEB samlas in och matas in i tre tidsserieanalysalgoritmer, ARIMA, regressionsträd och LSTM, för att välja en lämplig tidsserie-algoritm som ska används för AEB systemet. Resultaten visar att väder, mätområdet för sensorerna och brus kan påverka resultaten av AEB systemet. Efter att ha jämfört prestandan för dessa tre tidsserie-algoritmer genom att kontrastera återkallelsen och precisionen för dessa tre algoritmer för att detektera brus i data kan resultaten erhållas att LSTM har bättre prestanda för långsiktig analys. Och ARIMA är mer lämpad för korttidsanalyser i tidsserier. LSTM väljs för att analysera tidsseriedata, eftersom långsiktig tidsserieanalys är nödvändig för AEB systemet och det kan detektera bruset i variablerna i AEB system med bättre prestanda.

This master's thesis is an investigation and evaluation of MELCOR (a software tool for severe accident analyses regarding nuclear power plants), or more correctly of the (ASEA-Atom BWR 75) reactor model developed for version 1.8.6 of MELCOR. The main objective was to determine if MELCOR, with the reactor model in question, is able to produce satisfactory results in severe accident analyses compared to results made by MAAP, which is currently the only official software tool for this application in Sweden. The thesis work is related to the RASTEP project. This project has been carried out in several stages on behalf of SSM since 2009, with a number of specific issues explored within an NKS funded R&D project carried out 2011-2013. This investigation is related to the NKS part of the project. The purpose with the RASTEP project is to develop a method for rapid source term prediction that could aid the authorities in decision making during a severe accident in a nuclear power plant. A software tool, which also gave the project its name, i.e. RASTEP (RApid Source TErm Prediction), is therefore currently under development at Lloyd's Register Consulting. A software tool for severe accident analyses is needed to calculate the source terms which are the end result from the predictions made by RASTEP. A set of issues have been outlined in an earlier comparison between MAAP and MELCOR. The first objective was therefore to resolve these pre-discovered issues, but also to address new issues, should they occur. The existing MELCOR reactor model also had to be further developed through the inclusion of various safety systems, since these systems are required for certain types of scenarios. Subsequently, a set of scenarios was simulated to draw conclusions from the additions made to the reactor model. Most of the issues (pre-discovered as well as new ones) could be resolved. However the work also rendered a set of issues which are in need of further attention and investigation. The overall conclusion is that MELCOR is indeed a promising alternative for severe accident analyses in the Swedish work with nuclear safety. Several potential benefits from making use of MELCOR besides MAAP have been identified. In conclusion, they would be valuable assets to each other, e.g. since deviations in the results (between the two codes) would highlight possible weaknesses of the simulations. Finally it is recommended that the work on improving the MELCOR reactor model should continue. / RASTEP