Development of autonomous driving control algorithms based on ASIL scenarios.

The advancement of autonomous systems has catalyzed significant transformations across various industries, notably the automotive sector. This disruptive technology has become a cornerstone of innovation, fundamentally reshaping conventional paradigms of transportation and mobility. The incorporation of autonomous features into vehicles holds immense potential for enhancing efficiency, comfort, and, most importantly, safety on our roadways. This study aims to implement autonomous driving functionalities through a systematic approach encompassing perception, motion planning, control, and actuation. The primary objective is to evaluate the efficacy and outcomes of different control and motion planning algorithms concerning the critical facet of autonomous driving: safety. For safety assessment, the study will adhere to the Automotive Safety Integrity Level (ASIL) standards outlined in ISO 26262. Hazardous scenarios identified through Hazard Analysis and Risk Assessment (HARA) will be leveraged to compare the most and least complex control algorithms (PID and MPC). Real-world scenarios, representing both urban and rural settings, will be simulated within the Carla Simulation environment due to its capability in scenario modeling. Path planning will utilize the Carla Simulator API, while both the camera and obstacle sensors of the simulator will be employed for perception tasks. The car model will be based on the bicycle model, and motion planning will utilize adaptive cruise control. The project will be developed in Python programming, adhering to PEP8 standards, and executed on the Ubuntu operating system. Code quality and readability will be evaluated using Pylint linting analysis, with logging and output tracking in log and CSV file formats to store operational data. The study will culminate in a practical exploration of real-life scenarios within the Carla simulator, followed by a comparative analysis of outcomes, including cross-track and heading errors. These parameters will be used for ISO 26262 safety assurance by employing HARA to evaluate the safety parameters of the control algorithm.