Give me the two top highlights on the following: This article explores the ecosystem of autonomous vehicle technology, a field that is swiftly advancing and reshaping the transportation landscape due to recent innovations. Despite the immense potential of these innovations, their widespread adoption faces major difficulties. The dual concerns of cyber security and safety stand out as pivotal elements that necessitate a thorough investigation and proactive countermeasures. Our investigation demonstrates both the fundamental principles underpinning AV technology and the complicated information security dynamics behind their secure operation. We provided a comprehensive overview of potential cyber security attacks, with a particular emphasis on the emerging and unmitigated threat posed by machine learning $($ML$)$ attacks on deep neural networks $($DNNs$).$

The major threat presented under various cyber threats is a growing field of autonomous vehicle $($AV$)$ research that has attracted considerable academic interest and scholarly research. The lack of effective countermeasures to machine learning $($ML$)$ attacks on deep neural networks $($DNNs$)$ is a critical deficiency in our defensive mechanisms against the ever$-$evolving cyber security threats aimed at AVs. Unquestionably, the resilience and impregnability of AVs against such intrusions will be crucial in determining their future growth and gaining wider societal approval. Identifying, categorizing, and comprehending these prospective assaults is an additional crucial aspect of our work. We have classified these threats based on the principles of data availability, authenticity, integrity, and confidentiality, providing a comprehensive analysis of the threats currently confronting the AV industry. In addition to demonstrating the current status of the threat landscape, this classification uncovers the gaps in existing defense strategies, thereby spotlighting crucial research and mitigation areas. For each type of attack, we determined its status in terms of mitigation: fully mitigated for threats that have been completely neutralized by existing countermeasures, partially mitigated for threats that remain viable under certain circumstances, and uncovered for threats that require additional research or for which existing solutions have proven insufficient. The aforementioned findings demonstrate the urgent need for enhanced security measures in the AV technology industry. Given the potentially catastrophic effects of security flaws in these systems, ensuring their safety is not merely a recommendation but an absolute necessity.

Therefore, future research must concentrate on the development and implementation of resilient encryption protocols, the remediation of fundamental vulnerabilities, and the design of inventive countermeasures that can adapt to an ever$-$changing threat landscape. As we stand on the edge of a new era in which autonomous vehicles could transform our transportation systems, it is crucial that we maintain a proactive stance regarding the cyber security risks and safety challenges that accompany this technological revolution. By establishing a robust and secure operational environment for unmanned autonomous vehicles, we can not only ensure their operational effectiveness but also inspire public confidence in their deployment. Nonetheless, it is crucial to remember that this attempt is a process, not a destination. As the threat environment evolves, our defensive strategies must also evolve. By maintaining alertness, conducting sustained research, and collaborating across disciplines, we can effectively navigate this complex environment and safeguard the promising future of autonomous vehicles from the cyber security threats they face.