# Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
## 4 Safety Warning and Response Strategies Based on Fatigue and Distraction Detection
### Multi-Level Response Mechanism Combining Autonomous Driving Technology  
As the process of automobile intelligence and connectivity accelerates, integrating autonomous driving technology to establish a multi-level safety response mechanism offers a new solution for reducing the driving risks associated with driver fatigue or distraction. Within this framework, the system first attempts to alert the driver's attention through warning prompts and provides preliminary safety interventions using Advanced Driver Assistance Systems (ADAS) in emergency situations, playing a crucial role when the driver's response capabilities are insufficient to avoid potential risks. If primary measures fail to effectively reduce the accident risk, or if the driver fails to respond in time, the Autonomous Driving System (ADS) is prepared to take over vehicle control. During this process, ADS uses its advanced perception and decision-making capabilities to automatically navigate the vehicle to a safe area or take the most appropriate actions to ensure the safety of both the occupants and surrounding traffic participants. Currently, despite the rapid progress in autonomous driving technology, including capabilities demonstrated in experimental and limited commercial deployments, there are still challenges to achieving full automation takeover, such as perception complexity, decision-making reliability, and legal and ethical issues [72; 73]. Furthermore, it is critical to acknowledge that these intelligent vehicles are susceptible to potential cybersecurity threats from malicious actors. Such vulnerabilities necessitate the incorporation of cybersecurity measures within the autonomous driving framework to safeguard against cyberattacks that could compromise vehicle safety and functionality. [PERSON] et al. [74] highlighted the importance of considering cybersecurity implications in cooperative automated vehicle systems, stressing the need for enhanced redundancy. [PERSON] et al. [75] emphasized that, with increased connectivity and automation, vehicles face heightened risks of cybersecurity attacks, calling for a focus on addressing these risks in the autonomous vehicle sector. In the context of cyberattacks during the vehicle handover process, the challenge of distinguishing between abnormal driving behaviors caused by driver issues, such as fatigue or distraction, and those induced by cyberattacks is significant. Addressing this challenge, several scholars have conducted in-depth studies aimed at clearly identifying the effects of cyberattacks on autonomous driving control systems. For instance, [PERSON] et al. [76] explored secure adaptive control for autonomous vehicles under cyberattacks, while [PERSON] et al. [77] focused on cyber-physical system-based path-tracking control, underscoringthe necessity for sophisticated detection and mitigation strategies. These investigations reveal the complexities of maintaining autonomous vehicle safety in the face of cyber threats, emphasizing the covert nature of these threats and the critical need for advanced techniques in attack detection and response. Additionally, [PERSON] et al. [78] proposed a proactive approach to cyber risk classification for CAVs, utilizing a Bayesian Network model predicated on known software vulnerabilities, representing a methodological advancement in effectively predicting and mitigating cyber risks. Building on the understanding of cyberattacks' impact on autonomous driving perception and control systems, numerous scholars have investigated real-time detection methods aimed at mitigating the effects of such attacks. [PERSON] et al. [79] enhanced sensor anomaly detection and identification capabilities significantly by combining Kalman filtering with multi-layered deep learning methods. [PERSON] et al. [80] developed a comprehensive framework for detecting and isolating cyberattacks on autonomous driving systems, offering effective strategies to ensure that vehicle localization and navigation remain unaffected by cyber disturbances. Furthermore, [PERSON] et al.