In recent years, deep learning has gained tremendous popularity for solving various challenges in highly automated driving, such as environment perception, sensor fusion, motion planning, etc. In this context, semantic segmentation appears to be a vital task that helps identify objects in a scene around the car. However, deep learning models frequently struggle with robustness concerning diverse input distribution shifts, including sensor noise, photometric changes, motion blur, and adversarial attacks. This lack of robustness poses a substantial safety challenge that must be addressed to ensure the reliability and safety of highly automated driving systems. This thesis focuses on the lack of robustness issue in camera-based semantic segmentation tasks. We begin by benchmarking two state-of-the-art semantic segmentation models for their resilience to input corruptions and adversarial attacks. The benchmarking uncovers robustness issues in both models, with adversarial attacks inflicting more damage than corruptions. We then propose three novel methods to enhance these models’ robustness across a variety of attacks and corruptions simultaneously. Detailed evaluations and research directions are presented for each method and open the door to improved safety and reliability of autonomous systems in complex environments.