The field of autonomous vehicles is moving towards a increased focus on cybersecurity, as the complexity and interconnectedness of these systems render them vulnerable to various threats. Researchers are developing comprehensive threat models and conducting systematic risk assessments to evaluate vulnerabilities across different components of autonomous vehicles, including perception modules, planning systems, and communication interfaces. Innovative solutions are being proposed to mitigate the risks associated with autonomous vehicles, such as the impact of inference time attacks on perception sensors and the effects of emergency vehicle lighting on object detection performance. Noteworthy papers include: SoK: Stealing Cars Since Remote Keyless Entry Introduction and How to Defend From It, which provides a comprehensive systematization of knowledge on Remote Keyless Entry systems and their security status. PaniCar: Securing the Perception of Advanced Driving Assistance Systems Against Emergency Vehicle Lighting, which proposes a robust framework to enhance the resilience of object detectors against the effects of activated emergency vehicle lighting.